New Virtual Sonar and Wireless Sensor System Concepts J. A. Bucaro, B. H. Houston, and A. J. Romano Naval Research Laboratory, Washington, D.C. 20375

Recently, exciting new sensor array concepts have been proposed which, if realized, could revolutionize how we approach surface mounted acoustic sensor systems for underwater vehicles. Two such schemes are discussed here — so-called "virtual sonar" which is formulated around Helmholtz integral processing and "wireless" systems which transfer sensor information through radiated rf signals. The "virtual sonar" concept provides an interesting framework through which to combat the deleterious effects of the structure on surface mounted sensor systems including structure-borne vibration and variations in structure-backing impedance. The "wireless" concept would eliminate the necessity of a complex wiring or fiber-optic external network while minimizing vehicle penetrations.

INTRODUCTION

The growing possibility of being able to implement acoustic systems with high sensor counts (~ 10 4) has motivated consideration of how we might exploit such a capability when it does indeed become a reality. In particular, fiber optic sensor arrays utilizing in-fiber Bragg gratings and the revolution underway in MEMS/NEMS silicon-based sensor technologies suggest that such high sensor count systems might be “just around the corner.” Our considerations of how one might exploit this future technology for underwater vehicle sonars has centered on long-standing technical and engineering issues which have hampered these applications. These are, first and foremost, the deleterious effects FIGURE 1. Helmholtz integral reconstruction for an of structure-borne noise and hull impedance spatial incident plane wave, point force, and the two combined. and temporal variability. But they include as well limited apertures and the necessity for multiple hull over the surface of an underwater structure for which penetrations for sensor signal feed through. Two new there is high spatial density acoustic pressure and sensor array concepts — one called “virtual sonar”[1] normal velocity sensor data. The important result and the other involving wireless arrays based on here is that for the case of a structure excited by both cellular communication[2] — offer a new perspective an incident acoustic signal and interior noise sources on how to approach surface mounted acoustic sensor applied to the hull, when the acoustic field is systems in the attempt to mitigate these problems. evaluated inside the surface of the structure, only the The "virtual sonar" concept provides an interesting “virtual” incident field remains. This is illustrated in framework through which to combat unwanted Figure 1 taken from Reference[1] for an evacuated, effects of the structure on surface mounted sensor thin cylindrical shell at kaa = 5, where ka is the systems. The "wireless" concept would eliminate the acoustic wavenumber and a is the shell radius. To necessity of a complex wiring external network while illustrate the structural noise reducing properties of minimizing vehicle penetrations. In the following, we “virtual” sonar processing, Figure 2 displays the discuss these concepts and how they might be spatial Fourier transform of the pressure over the synergistically applied in the case of an underwater length of the cylindrical shell section for this case. AUV. The lowest curve is the transform of the virtual sonar evaluated along the central axis, as well as for the ARRAY CONCEPTS true 1 Pa incident field (these two curves overlap). The upper curves are the transforms of the total Virtual Sonar surface response along a line on the lateral surface of the cylinder when the interior force (normalized by the square of the shell thickness) is 104 Nt/m2 and 103 The “virtual sonar” concept[1] is based on simple Nt/m2, respectively. One can see the superior considerations of evaluating the Helmholtz integral performance of the virtual interior sonar regarding structure-borne noise across the entire wavenumber spectrum including the acoustic domain (-ka to + ka).

10000

104 Nt/m2 1000

Exterior Line Array 103 Nt/m2 100

Virtual Sonar 10 FIGURE 3. Wireless array on an AUV

In the case of a small AUV, an individual cell 1 might access sensors located within approximately a -k k a a half meter radius. At the high rf frequencies 0.1 -20 -15 -10 -5 0 5 10 15 20 involved, the high absorption in water necessitates Wavenumber FIGURE 2. Spatial Fourier transforms of the pressure over the introduction of a suitable waveguide material to an exterior line and for the interior “virtual” line. The allow sufficient propagation even over these modest excitation is an incident plane wave and an interior point base-cell distances. The field strength of the lowest force. The transform for the incident field overlaps that for TEM order mode is approximately proportional to the the virtual sonar. following factors

Results of the type shown above require sensor 2 - f r tan G ~ c/ 2 / p f r e (1) spatial sampling on the order of two per structural 0 wavelength. Azimuthally, the number of sensors, N, where c is the speed of light, is the dielectric would be 2nmax, i.e twice the highest flexural circumferential harmonic. Axially, N would be constant, f the frequency, r the propagation distance, the permeability, and the loss factor. Eq. (1) 2L/lf, i.e. twice the number of flexural wavelengths 2/5 indicates that what is desired is a material with a low (lf) along the shell of length L. Now nmax µ (k aa) 1/2 index of refraction and a low loss factor. In water, and lf µ (ka a) . Calculations for the flexural wave the rf signal decays by 10-5 in 1 cm. This compares dispersion and cut-off frequencies versus nmaxfor a plastic-like, 1.8m AUV shell structure predict the to 2m, 12m, 20m, and 1km for polyurethane, nylon, 3 Teflon, and Styrofoam, respectively. Thus, a thin following. At kaa = 1, about 10 measurement points layer of a material of this type would provide a would be required, while for kaa =10, about 8600. This requires indeed a large number of sensors; sufficiently low loss waveguide in which to however, it would enable a “noiseless” array. In propagate the sensor-to-base cell signals. addition, with both pressure and velocity Estimates indicate that on the order of 1.5mW of (acceleration) sensors employed, hull impedance electrical power would have to be supplied to each spatial and temporal variations would be of no sensor/radio pair assuming 10nJ/bit. This could be consequence. distributed in a number of ways including power broadcasting and energy harvesting of thermal gradients or fluid flow. Wireless Array ACKNOWLEDGEMENT One approach for accessing such a large sensor count system is a wireless array. As depicted in This work is supported in part by ONR. Figure 3, wireless sensor/radios would be distributed over the surface of the structure. As many as one REFERENCES thousand sensor/radio pairs could be tied in to a specific cell cite, and there could be tens of cells over the body. The sensor information from this relatively 1. A.J. Romano, J.A. Bucaro, B.H. Houston, and small number of cells could be fed onto a single line E. G. Williams, J.Acoust. Soc. Am. 108, 2823-2828 which then penetrates the hull. The two major (2000) technology issues here involve propagation of the 2. A. Mehrotra, Cellular Radio: Analog and Digital radiating gigahertz rf signals from sensors to base Systems, Artech House, Boston, MA, 1994 cell and powering of the individual sensor/radio devices. Development of Thin, Low Frequency Electroacoustic Projectors for Underwater Applications

T. R. Howartha and J. F. Tresslerb

aNAVSEA Division Newport, Newport, RI USA bNaval Research Laboratory, Washington, DC USA

Two acoustic transducer panels have been designed, fabricated and electroacoustically evaluated. These panels featured ‘cymbal’ drivers sandwiched between a radiating cover plate and a tungsten backing plate. The acoustic output shows resonance frequencies of both transducer panels below 1 kHz.

INTRODUCTION Materials Research Laboratory at Penn State in the mid 1990’s and have been investigated for use in a The two most common acoustic projector technologies number of applications [3]. A cymbal consists of an used on unmanned underwater vehicle (UUV) electroactive ceramic disk sandwiched between and platforms are tonpilz transducers and piezocomposites. mechanically bonded to two thin metal caps. Each cap Both of these technologies, however, are typically is shaped in a die press so that it will contain a shallow designed for use at frequencies above 10 kHz. A U.S. air cavity underneath its inner surface after it is bonded Navy designed ‘1-3’ piezocomposite 2.54 cm in height to the face of the ceramic disk. The caps serve as with a projector radiating face of 15.24 cm by 7.62 cm mechanical transformers for converting the small was demonstrated to exhibit broadband characteristics radial displacement and vibration velocity of the between 10 kHz and 100 kHz [1]. At its resonance electroactive disk into a much larger axial direction frequency of 100 kHz, the TVR was measured to be displacement and vibration velocity normal to the apex 174 dB// Pa/V @ 1 m. At 1 kHz, the TVR was less of the caps. Hence, the cymbal driver primarily than 110 dB// Pa/V @ 1 m. utilizes the ‘31’ contribution of the active ceramic to To generate high acoustic output at frequencies below achieve flexure in the caps. 10 kHz, free-flooded piezoelectric ceramic rings, Cymbals with two different diameters (12.7-mm and electromagnetic drivers, and flextensional transducers 15.9-mm, designated as Type 1 and Type 2, have traditionally been used. However, due to their respectively) were used in this study. Titanium was selected as the cap material because of its low density large size and weight, these technologies are not easily 3 adaptable or convenient for use in UUV platforms. (4500 kg/m ), moderate elastic modulus (120 GPa), For the implementation of low frequency acoustic and oxidation resistance. The electroactive material sources on UUV platforms, advanced hardware is was Navy Type VI (PZT-5H) piezoelectric ceramic. required. The U.S. Navy has designed, built and This material was selected because its very large evaluated novel prototype underwater electroacoustic piezoelectric d31 coefficient served to generate the projectors that have a fundamental resonance largest flexure (i.e., displacement) in the caps. frequency below 1 kHz. The active component of Prior to bonding the caps to the ceramic, studs 1.4 mm these projectors is slightly less than 0.5 cm in height in diameter and 4.6 mm long with UNF 0-80 threads and has a radiating face of 15.24 cm by 7.62 cm, were welded to the apex of the caps to form both the making them ideal candidates for use in mobile mechanical and electrical means of handling. platforms and littoral environments. ACOUSTIC TRANSDUCTION PANEL THE CYMBAL DRIVER The low frequency acoustic projector panels consisted The low frequency underwater acoustic transducers of an array of cymbal drivers sandwiched between a utilize miniature class V flextensional drivers [2], 12.7 mm thick tungsten backing plate and a 2.2 mm commonly known as ‘cymbals’, as the active elements thick copper electroplated carbon graphite epoxy board in the projector. Cymbals were developed at the (solid all uni-carbon from Aerospace Composite Products, San Leandro, CA). Figure 1 shows the driven to a maximum of 750 Vrms because further cymbal elements mounted into the tungsten backing evaluation was desired and we didn’t want to take the plate before the cover plate was attached. risk of failure. Comparing the transmitting response of Xducer I both before and after being subject to a drive level of 750 Vrms indicated no adverse effects due to the high drive.

150

12.7-mm diameter driver (solid line) 15.9-mm diameter driver (dashed line) 140

130

120

FIGURE 1: Cymbal elements on backing plate. TVR (dB//uPa/V @ 1 m)

The total radiating area of the projector was 152.4 mm 110 by 76.2 mm. Two transducers, designated as Xducer I and Xducer II, were built and tested. Xducer I consisted of 50 Type 1 cymbal drivers in a 10 by 5 100 arrangement. Xducer II contained 32 Type 2 elements 0.1 1 10 100 in an 8 by 4 configuration. The studded cymbals were Frequency (kHz) initially torqued into a predrilled and threaded tungsten backing plate. Next, the drilled out graphite cover plate was lowered into place so that it rested over the FIGURE 2: TVR comparison of transducers. top of the cymbal array. The cover plate was similarly torqued onto the studded cymbals with hex nuts. Standard underwater calibration measurements on the ACKNOWLEDGMENTS low frequency projectors were performed at the NAVSEA Division Crane Glendora Lake Facility in The authors express their appreciation to Walter Sullivan, Indiana. The measurements were performed Carney, Kirk Robinson and Mel Jackaway of at a depth of 11.3 meters and a water temperature of 10 NAVSEA Crane Division for fabrication and degrees C. The transmitting voltage response (TVR) measurements of the panels. The authors acknowledge for the transducers as a function of frequency was the support of the Office of Naval Research. measured from 0.5 kHz to 100 kHz and is shown in figure 2. The low frequency resonances, 0.6 kHz in the case of Xducer II and 0.9 kHz in the case of REFERENCES Xducer I are a result of the piston-like motion of the cover plate being driven by the individual cymbal 1. T.R. Howarth and R. Y. Ting, “Development Of A drivers. Both projectors exhibit markedly higher TVR Broadband Underwater Sound Projector,” CD than that of a 1-3 piezocomposite with the same Proceedings of OCEANS ‘97 MTS/IEEE Conference, radiating area. Unlike the 1-3 piezocomposite, IEEE Publications (ISBN 0-7803-4111-2/97), however, the cymbal-based transducers are Piscataway, NJ, 1-7, 1997. multiresonant as the cap and flexing graphite plates flex throughout the frequency bands. The beam 2. R. E. Newnham and A. Dogan, Metal-electroactive patterns at 1 kHz for the two projectors both show the ceramic composite transducer, U. S. Patent 5,729,077, omnidirectional response as is to be expected at these issued March 17, 1998.. frequencies. The sound pressure level (SPL) was 3. J. Zang, W. J. Hughes, P. Bouchilloux, R. J. Meyer Jr., K. measured for Xducer I as a function of frequency and Uchino and R. E. Newnham, "A Class V flextensional drive level. Nonlinear behavior becomes apparent at a transducer: The Cymbal," Ultrasonics, 37, 387-393, drive level above 500 Vrms. The transducer was only 1999. Experimental Results of Passive Phase Conjugation Applied to Underwater Acoustic Communication W. L. J. Fox, D. Rouseff, D. R. Jackson, and C. D. Jones University of Washington, Applied Physics Laboratory, 1013 NE 40th St., Seattle, WA 98105, USA

In May 2000, an experiment was performed to demonstrate the effectiveness of passive phase conjugation (PPC) for underwater acoustic communications. PPC is a coherent array and signal processing scheme that has strong theoretical ties to active phase conjugation and time-reversal mirrors in the ocean. It differs from active techniques in that an array of transducers need only receive, while the transmitter can be a single transducer. Previous results have been presented showing performance of the algorithm for cases with a vertical receive array spanning the water column. This paper will show further results with a truncated receive array.

THEORETICAL FOUNDATIONS OF impulse responses can be measured by re-probing, which PASSIVE PHASE CONJUGATION does impose a certain data rate overhead.

Phase conjugate acoustics have previously been demonstrated in the ocean [1]. The basic phenomenon EXPERIMENTAL RESULTS can be described by the following. A source transmits a signal, a distorted version of which is received by the An experiment to demonstrate the effectiveness of transducerss of a distant vertical array. The signal is typi- PPC for underwater acoustic communication was per- cally distorted by time spread due to multiple interactions formed in Puget Sound near Seattle, WA in May 2000. with the sea surface and bottom as it propagates down the Some results of that experiment were previously pre- acoustic channel. If these distorted receive signals are sented [3, 4]. Those results were for cases where the re- time reversed and transmitted from their respective trans- ceive array spanned nearly the entire water column. A ducers, it can be shown theoretically [2] that there will be similar case is shown in the first example here. For this a spatio-temporal focusing of this second transmission at part of the experiment, both vessels (source and receiver the location of the original transmission. deployment) were moored in 30 m of water roughly

It is the time-spread distortion mentioned above and 650 m away from each other. The source was deployed at its time variable nature that pose the main hurdles to high a depth of 15 m. data rate underwater acoustic communications. Compu- Figure 1 shows the channel impulse responses as mea-

tationally complex adaptive equalizers are the standard sured on the 14-element receiving array, with element

technique for addressing these problems. A technique spacing of 1 ¡ 7 m. This configuration spanned roughly called passive phase conjugation (PPC) has been devel- 74% of the water column. The probe was sent, followed oped and presented [3, 4] as a computationally simple al- by a 50 ms blank window to measure the channel im- ternative. pulse response, subsequently followed by the communi- cations symbol stream which can be seen at the right side PPC is designed for coherently modulated communi- of Fig. 1. The average estimated SNR per channel for this cation from a point in the water column to an array with case is 12.9 dB using the equation

vertical spatial diversity. In its most simple form, the ¤

communicating source sends a probe signal followed by a 14 N50 N50 §

∑ ∑ 2 ¦ ∑ 2 ¥

blanking period to allow for channel clearing followed by xi ¥ n x j n

£ £

n £ 1 i 1 j 1 ¢

the communication symbol stream. The receivers initally ¤ SNRest (1)

see the probe signal followed by the time-spread multi- 14 N50 ¨ ∑ ∑ 2 §

paths. Each channel uses this estimated channel impulse x j ¥ n £ n £ 1 j 1 response (different for each receiver) as a matched filter for the following communications symbols. The resulting where N50 is the number of data samples in a 50 ms win- matched filter outputs are integrated over the array. For dow, the indices i correspond to samples after the start a vertical array that spans the water column with dense of the probe reception, the indices j correspond to sam-

enough spatial sampling, the result can be shown to can- ples before the probe reception (i.e., noise only), and the

© ¥ cel nearly perfectly inter-symbol interference [4], anal- x i ¥ j n are the real-valued data samples from receiver n. ogous to the theoretical foundations of the active phase The data were transmitted as a random (but known) conjugation phenomenon. Time variability of the channel sequence of binary phase shift keyed (BPSK) symbols at Raw Hydrophone Data: 130145916 Output of D−BPSK Demodulation: 130163949 1.5 1 2 1 3 4 5 0.5 6

7 0 8 Constellation 9 −0.5

Receiving Hydrophone 10 11 −1 12 13 14 −1.5 0 1 2 3 4 5 1.04 1.05 1.06 1.07 1.08 1.09 Time (s) Time (s) FIGURE 3. Differential BPSK demodulation results, element

FIGURE 1. Probe pulse response on 14-element array with

spacing 1 0 m. start of data sequence, element spacing 1 7 m.

Output of DBPSK Processing: 130145916 1.5 was still achieved. Note, however, that the constellations have widened and moved closer to 0, meaning a higher 1 probability of symbol error. Future work on this topic will include investigation 0.5 of the algorithm’s performance for non-vertical arrays. We will also look at the computational and performance 0 trade-offs involved with using decision directed block es-

Constellation timation of the channel impulse response in place of the −0.5 isolated probe concept.

−1

−1.5 ACKNOWLEDGMENTS 0 1 2 3 4 5 Time (s) This work was funded by the Office of Naval Re-

FIGURE 2. Differential BPSK demodulation results, element search.

spacing 1 7 m.

REFERENCES a rate of 2174 symbols per second, and demodulated dif- 1. W. A. Kuperman, W. S. Hodgkiss, H. C. Song, T. Akal, C. ferentially (i.e., phase shift between symbols, rather than Ferla, and D. R. Jackson, J. Acoust. Soc. Am., 103, pp. 25- absolute phase, is important). The result of the demodu- 40. lation can be seen in Fig. 2. Ideally, values differentially 2. D. R. Jackson and D. R. Dowling, J. Acoust. Soc. Am., 89, encoded as ’1’ will take on a value of 1.0 in the constella- pp. 171-81. tion plot, and values encoded as ’0’ will take on a value of -1.0 in the plot. The ’x’ symbols denote symbols encoded 3. D. R. Jackson, D. Rouseff, W. L. J. Fox, C. D. Jones, J. as ’1’, and the ’o’ symbols denote those encoded as ’0.’ A. Ritcey, and D. R. Dowling, J. Acoust. Soc. Am., 108, p. 2607. Note that there are no symbol errors in this data set. Roughly 100 minutes later, data was taken with a 4. D. R. Jackson, D. Rouseff, W. L. J. Fox, C. D. Jones, J. A. Ritcey, and D. R. Dowling, "Underwater acoustic commu- re-deployed receive array whose element spacing was

nication by passive phase conjugation: Theory and experi-

1 ¡ 0 m, i.e., spanning roughly 43% of the water col- mental results," IEEE J. Oceanic Eng. (accepted for publi- umn. Figure 3 shows the demodulation results for this cation).

case. At this time the noise background had increased, ¢

with SNRest 2 ¡ 2 dB. Despite the decreased SNR and re- duced water column coverage, error-free communication Optimal Superimposing of the Normalized Quasi Rigid Echo Spectrum on the Quasi Rigid Form Function : Targets Sizing

P. Schweitzera, J. Mathieua,b, E. Tisseranda, D. Bellefleurb aL.I.E.N., Faculté des Sciences, Université Henri Poincaré - NANCY I, B.P. 239, 54506 VANDOEUVRE-LES- NANCY CEDEX, FRANCE. bG.E.M.C.E.A. - 149, rue Gabriel Péri - 54500 VANDOEUVRE-LES-NANCY

We developped a methodology based on the analysis of the backscattered echo from an immersed target for the determination of its size. The Quasi Rigid Backscattered Echo (QRBE) defined as the first part of the backscattered echo contains the size information of the target. The Quasi Rigid Form Function (QRFF) is then constructed by considering only the quasi rigid backscattered echo. The Normalized QRBE spectrum obtained by practice can be interpreted as a segment of the QRFF. The object of this paper is to present how we find the optimal super imposition between them in order to find the size of the target. We present successively the two steps for the method. First we determine the classes of solutions according to the system bandwith B in use. These classes are defined by all the size for which the QRFF has the same and type of extrema. We improve then this estimate by mean squares criterian applied in this class of belonging. We present the experimental setup and show different size determination results for different steel wires.

INTRODUCTION 1 Relative amplitude We propose an ultrasonic method to determine the 1 size of an immersed wire. This can be done comparing 1 its quasi rigid backscattered echo spectrum on a 0.8 reference function. The different steps of the method of surimposition in order to find the size of the target 0 1 2 3 4 are presented successively in this paper. ka FIGURE 1. Modulus of the steel QRFF versus ka. THE QUASI RIGID BACKSCATTERED ECHO (QRBE) EXPERIMENTAL CONDITIONS

We define the QRBE, for an immersed target The samples (steel wires) are vertically imbedded (wire) in water and insonified by a plane ultrasonic in a tank filled with still water. The emitter-receiver is wave, as the first part of the backscattered echo. Its a broadband transducer of 1 cm diameter, acting in duration is equal to the backscattered echo of an frequencies in the range of 0,7 MHz to 1,5 MHz. infinitively rigid wire of same size. SIZING BY SPECTRAL ANALYSIS. The first part of the echo is mainly constituted by Scholte-Stoneley and specular waves which contain Method the size information of the target [1]. The first step of our method is the calibration. THE QUASI RIGID FORM FUNCTION block We calculate the spectrum E (f) of the QRBE from (QRFF) QR a block which simulate a solid half space. Then this block is replaced by the wire to be sized Contrary to the form function which expresses all wire the scattering waves generated by a wire, there is no EQR (f) analytical expression of the QRFF f (ka). The quasi
QR The ratio ENQR(f) can be interpreted as rigid form function is calculated by taking into account block EQR (f) the quasi rigid part of the echo we simulate. The details of the method is given in [2]. a segment of the quasi-rigid form function. wire square algorithm in order to find the target size. EQR (f) is the quasi-rigid part of the echo. This relation is only valid in the limited bandwidth PERFORMANCES B of the measuring chain so for ka limited to the ! f f 1 In order to test the performances of our method, we interval 2 min a ; 2 max a . c is the sound speed #" c c 32 wire have superimposed the segment EQR (f) on the in water and “a” the size of the target The last step is to determine the size of the wire QRFF. After identification, we obtain a=394 mm, the real radius is a  400 M 10 . under test by replacing the segment ENQR(f) on the QRFF. 1.15

Positioning the segment on the QRFF fA()

fmes( ) The first step consists in dividing the QRFF in several parts to group together different target sizes in 0.65 the same class. For a given class, corresponding to a 80 140 group of sizes, the segment of QRFF in the bandwidth of the system will have the same number and type of FIGURE 4 : Optimal super imposing of ENQR on FFQR for extrema. a steel wire of radius 400 m.

2 CONCLUSION Relative amplitude amin amax A new concept to find the size of a immersed target 1 (wire) using the first part of the backscattered echo is presented in this paper. The methodology is to super impose the spectra of the experimental quasi rigid part of the echo on a reference function. The 0 0.5 1 1.5 2 2.5 3 Frequency (MHz) measurements, performed with steel wires immersed in water, permit to determine their size with a good FIGURE 2. FFQR for the extreme radius of a class of size precision. Research work to find the size of sphere target and Between these two limit values amin and amax , both wires of different matter (copper, aluminium …) are QRFF present two extrema (figure 2). also made. In a second step, the analysis of the practicle segment

ENQR(f) allows to place it in his own class. ACKNOWLEDMENTS Figure 3 show the normalized spectrum for a steel wire of 1 mm diameter. The segment has 2 extrema. This work is supported by the French Lorraine Region and G.E.M.C.E.A. Relative amplitude 1 REFERENCES

1. H. Uberhall, “Surface waves in acoustics”, Physical 0.5 Reduced Acoustics vol.10, Academic Press, 1973. Bandwidth B [0.75 ; 1.45] 2. Mathieu J., Schweitzer P., Tisserand E., Bellefleur 0 0.75 1.45 D. 10-13 july 2000. NParticle size measurements by Frequency (MHz) spectral analysis : quasi-rigid form function study”. Proceedings of the fifth European Conference on FIGURE 3. Normalized spectrum of steel wire Underwater Acoustics, ECUA 2000, Lyon (France), pp. 1887-1892. The last step is to minimize the euclidian distance

between ENQR(f) and the FFQR with an mean Implosion Sound Sources N. Yen

CLY Associates, P. O. Box 6806, Alexandria, VA, 22306-6806 USA

Acoustic pressure caused by implosion has impulsive characteristics similar to those generated by an explosive, but the signature is highly temporally and spatially localized without the safety issues with explosives. The present study focuses on the practical utilization of a highly concentrated pressure pulse caused by an implosion mechanism as a sound source. The operational principle of an implosion sound is simply derived from the conversion of hydrodynamic energy to acoustic energy from the collapse of a cavity. However, the controllability of an implosion can be manipulated by directing the one-dimensional flow into a confined specially shaped cavity to form the spherical convergence. Based on the physics of the collapse of a spherical cavity, the reflection of a self-focused wave can create a delta-function like pressure pulse. For underwater application, because no air is trapped in the closed space initially, the acoustic signature is a simple pressure pulse without any interference of bubbles generally observed in the explosive or air gun operations. The simple structure of the implosion mechanism can be adapted for various engineering designs to meet the practical needs in oceanographic and underwater research activities.

INTRODUCTION (e) tremendous temperature at the center causes dissociation, (f) light emits from black body radiation or Current technology for generating impulsive sound waves ionization, (g) the reflected implosion propagates out as a for underwater surveying and communication applications sound pulse. The acoustic emission from a 65 m bubble employs the detonation mechanism. The acoustic signal measured in the laboratory [4] has an intensity magnitude obtained in this way is not controllable: it consists of about 170 dB/1 Pcal. The estimated conversion interference caused by bubbles' resonances and it is efficiency from the mechanical energy to the acoustic sensitive to the variation of charges and operational depth. energy is about 75%. Other methods use various types of frequency synthesis approaches to form a broad band signal. However, such a sound source is not temporally and spatially localized, and its efficiency is low due to the compensation of frequency response during the electro-mechanical transduction. A highly concentrated pressure pulse caused by an implosion mechanism appears to alleviate those problems. A brief review on some of the implosion underwater sound sources (a) (b) (c) (d) (e) (f) (g) is given in references 1. The presentation covered in this short paper is focused on the control of the implosion sound generation. Extensive discussion of this subject is described FIGURE 1. Phenomenological Interpretation of Implosion in references 2 and 3.

IMPLOSION MECHANISM CONTROLLED IMPLOSION

Implosion is formed by directing waves toward a focal The practical way to generate a controlled implosion is center. Because of the energy concentration, an extremely through the manipulation of a plane shock wave [5] strong pressure and high temperature can be built up in a according to the Chester-Chisnell-Whitam (CCW) model very small region, thereby generating a sharp shock wave [6] by forcing it to converge to a single point. Fig. 2(a) propagating outwards. A simple mathematical model of this shows closed tubes with three different shaped terminals: phenomenon can be (i) cylindrical, (ii) CCW form, (iii) conical. The measured formulated by the collapse of a spherical void such as the waveforms of implosion sound from those controlled Rayleigh-Plesset's equation used in analyzing the enclosed cavities of one inch diameter tube are displayed cavitation. The phenomenological interpretation of the in Fig. 2(b) respectively. The highest peak pressure pulse implosion process sequence shown in Fig. 1 are: (a) the can reach to an intensity of 190 dB/1 Pcal. The CCW collapse of a bubble generates a spherical shock wave, (b) shaped terminal provides a well defined pulse with a the implosion shock wave converges to the cavity center, duration of less than 100 s. (c) a high pressure pulse is created by energy concentration, (d) the intensity of a shock front is enhanced by reflection, FIGURE 3. An Implosion Sound Projector

DISCUSSION

The use of an implosion sound source described here has demonstrated that an underwater acoustic wave 0 .15 .3 ms generator designed with this type of mechanism is simple (a) (b) and safe to operate for undersea remote surveying FIGURE 2. Enclosed Cavity Implosion application. At the current stage, only a simple prototype has been constructed and laboratory tests have IMPLOSION SOUND PROJECTORS demonstrated its practicability for underwater application as an effective sound source. Because of the simplicity of Various types of implosion sound projectors [3] can be the implosion mechanism in comparison with the constructed based on the controllable implosion scheme traditional acoustics radiators, this type of a sound source described in the previous section. Their design parameters has much better performance features in the areas of depend on the application requirements and cost acoustic signature (a temporal and spatial concentrated constraints. The sketch shown in Fig. 3 is a project element wide band signal), operation handling (light weight), which has the feature that can be integrated with undersea system integration (small size), and low cost (less logistic survey system for directional scanning. This sound support). Many alternative designs other than those generated unit consists of a housing #61, the implosion mentioned in this paper can be adapted for some chamber # 14 with its implosion center at #16. An engineering modifications to meet the needs in electronic controlled valve #66 is used to create an artificial oceanographic and underwater research activities. Future void in #14 with a vacuum pump. For the implosion effort for implosion acoustics studies will be directed to operation, the valve switches the tube connection to a high- the optimization of the structure design based on a pressure tank and forces the fluid #38 inside the housing specific task requirement. squash to the tip of the chamber #62 and causes a convergent collapse center at # 16. The cover #57 is an acoustic transparent material so the generated acoustic REFERENCES wave can be directed out through this window. Many of 1. Yen, N., "Application of Implosion to Underwater Sound such a unit #40 can be arranged to form an array. With the Generation”, J. Acoust. Soc. Am. 100, p 2716, (1996). proper placement of implosion units and timing delay opening of control valves, a directional radiated sharp 2. Yen, N., “An Implosion Sound Source for Undersea Exploration implosion acoustic impulse can be formed. Preliminary Applications”, in Proceedings of Second International Ocean and Atmosphere Conference, Central Weather Bureau, Taipei, 2000, tests of a chamber size of 1" diameter and 1.5" in depth in pp. 315-318. the laboratory tank operated with vacuum of 0.022 ATM and a high-pressure of 1 ATM, generates an impulse of 3. Yen, N., An unpublished NRL proposal and "Controllable Implosive Sound Projector", Reg. Number H1664, United States peak intensity 195 dB/1 Pcal with a pulse width less than Statutory Invention Registration, July 1, 1997. 50 s at 3 feet away from the implosion projector can be directed to a desired direction for remote scanning search. 4. Stottlemyer, T.R., An Experimental Study of the Acoustic Emission from Collapsing Cavities in Liquids, A Ph. D. dissertation, Yale University, New Haven, 1996.

5. Yen, N., "Controlled Implosion Sound Generation", Nonlinear Acoustics in Prospective, 14th International Symposium on Nonlinear Acoustics, Ed. R. J. Wei, Nanjing University Press, Nanjing, 1996, pp 292-297.

6. Whitham, G.B., Linear and Nonlinear Waves, Wiley-Interscience, New York, 1974 Surface Acoustic Waves for Sediment Characterization; from Sonar to Tomographic Approach

M. E. Zakharia a and E. Mouton b

a French Naval Academy IRENav, 29240, Brest Naval, France, [email protected] b SAGE-GEODIA, Les Clachs, 34560, Poussan, France, [email protected]

Surface acoustic waves (such as Stoneley-Scholte Waves (SSW) travel along the interface between the seawater and the seabed; they are guided in a layer of about a wavelength and carry information on the first meters of sediment. Their velocity can be used for the inversion of fine properties of the sediment. Several experiments have shown that these waves can also be used for the detection of buried objects. Experiments on tomographic reconstruction of an anomaly in the sediment using SSW (impedance changes due to the presence of bubbles, for instance) have provided very interesting results and highlighted the relevance of such a technique for sediment description at sea.

INTRODUCTION Detection of a buried object Several applications (such as offshore, cable and pipeline installation, mine detection, propagation Several tank experiments have been carried out on prediction, slope stability studies…) need an accurate buried targets in a homogeneous resin [4]. For a knowledge of the seabed properties. This work will transmitter position, the surface was finely scanned show how Stoneley-Scholte Waves (SSW) can be a and the SSW energy on the interface was computed. relevant tool for seabed characterization and sub- Figure 1 displays the results of such a computation. In bottom imaging. this figure, one can see from top to bottom: S the incoming wave loosing some energy while propagating, Fine characterization of sediments S a zone of interference between the incoming wave and the one reflected by the sphere As the penetration depth of SSW depends on the S a shadow zone after the sphere very similar to frequency, each frequency bin carries information on a shadows encountered in sidescan sonar. corresponding layer; when using wideband signals, the group velocity dispersion of the SSW depends on the velocity profile in the sediment. The group velocity can be easily measured on a time-frequency representation of wideband transmitted signals [6]. Several simulations and experiments have shown that velocity dispersion could be predicted with accuracy better than 5% (direct problem) [6].

The inverse problem consists in determining the sediment properties from SSW properties. A new FIGURE 1. Energy distribution of SSW at water approach, based on neural network has been developed bottom interface in the presence of a spherical target for this purpose. It showed, on experimental data, that size: 16 mm; frequency 0.1 MHz, wavelength 10 mm. a comparable accuracy (better than 5%) can be 2-dB/ gray level, scales 0.1 x 0.1 meters. obtained on the data from inverse problem solution [5]. Such results were very encouraging: if SSW Similar effects have been observed for various targets properties are sensitive to small changes in the (even when comparable in size to a wavelength). They sediment, they should be very sensitive to the presence showed that the presence of a target scatters the energy of a buried target (large variation of impedance). of SSW in all direction (instead of forward direction). The absence of energy can thus be used to detect the presence of a target (like in sidescan sonar). Preliminary work (still in progress) has shown that a sonar approach (monostatic transmitter and receiver) can also be used for target detection.

Reflection and transmission properties

The reflection and transmission of SSW was studied in the solid-solid configuration. As they are evanescent waves, two hypotheses have been found: continuity conditions for either each component or their resultant. FIGURE 2. Tomographic reconstruction of a From experiments, we have found that the second one cylindrical inclusion in a resin. matches better and that, at oblique incidence, SSW Scales: 0.2 x0.2 m, 18 m/s by gray level follow laws similar to Snell-Descartes ones [2]: S SSW is reflected as a SSW (with same velocity) S SSW is transmitted as another SSW (with a SS velocity corresponding to the second medium) REFERENCES S A critical angle was observed (similar to the one encountered for compression waves). 1. M. E. Zakharia and P. Chevret « Neural network S approach for inverting velocity dispersion; application No other waves or components were observed. to sediment and to sonar target characterization, Inverse Problems 16 (2000) 1963-1708.

Tomography 2. E. Mouton, J. Châtillon et M.E. Zakharia, Etude expérimentale de la réflexion et de la transmission des The properties cited above allowed a tomographic ondes de surface de type Stoneley-Scholte à l'interface reconstruction of velocity using SSW. Several mock- de deux milieux solides, Actes du 5e Congrès Français ups and geometrical configurations were studied using d'Acoustique, CFA 2000, septembre 2000, Lausanne, a cylindrical inclusion in homogeneous sediment [3]. Suisse, pp. 80-83. Figure 2 shows an example of tomographic 3. E. Mouton and M. E. Zakharia, Reconstruction of reconstruction of the SSW velocity. The position of sediment inhomogeneities using surface wave the inclusion has also been displayed on the figure. tomography, in Proceeding European Conference on The results clearly show the high quality of Underwater Acoustics (ECUA2000), Lyon, July 2000, tomographic reconstruction using the backpropagation M.E. Zakharia, P. Chevret and P. Dubail editors, method. European Commission Brussels (Belgium), Vol. 1 pp. 245-250.

4. M.E. Zakharia and J. Châtillon, Interaction of interface CONCLUSION waves with a buried object, in Proceedings of The Third European Conference on Underwater Acoustics Results from various tank experiments and associated (ECUA), Heraklio (Greece), June 1996, J.S. Papadakis signal processing schemes showed the ability of SSW Ed., European Commission Brussels, pp. 39-44. to provide accurate information on fine characteristics of the sediments and to be used for sub-bottom 5. J. Guilbot and M. Magand, Determination of the imaging and the detection of buried objects. Next step geoacoustical parameters of a sedimentary layer from is the application of the techniques described and surface acoustic waves: a neural network approach, validated in tank to the seabed and to real applications Conference on Full Field Inversion Methods in Ocean and Seismic Acoustics, O. Diachock, A. Caiti, P. (in situ). Gerstoflt and H. Scmidt Eds, Kluwer Academic Publishers, pp.171-176, 1995.

6. J. Guilbot and M.E. Zakharia, Tank experiments on a ACKNOWLEDGMENTS sediment small-scale model. Shear wave velocity profile inversion via Stoneley-Scholte waves, in The work was achieved in LASSSO laboratory Proceedings of The Second European Conference on (Laboratoire d’Acoustique, Systèmes, Signaux et Underwater Acoustics (ECUA), Lyngby (Denmark), Sonar, at CPE, Lyon.) and was partly supported by the July 1994, L. Bjørnø Ed., European Commission, European Commission and by the French MOD. vol. II, pp. 979-984.

Stationary hydroacoustic methodology to determine diel activity of fish biomass in the artificial habitats

Sala A.(1), Fabi G. (1)

(1) Istituto di Ricerche sulla Pesca Marittima (IRPEM), Consiglio Nazionale delle Ricerche Largo Fiera della Pesca, 1 – 60125 Ancona, Italy

The biomass of fish assemblage, inhabiting the Senigallia artificial reef (central Adriatic sea, Italy), was evaluated in the period July–November 1996. Density and biomass were assessed through a stationary hydroacoustic methodology using an appropriately adapted SIMRAD EY500 system. A part of the system was placed inside the reef and it was linked by radio- modem to the remaining part installed ashore in the Institute. The experimentation gave useful information about the daily behaviour of the fish assemblage living at the reef: during the whole period the lowest densities were generally recorded in the early afternoon, whilst the highest abundances were commonly observed late in the night and in the early morning. Acoustical records confirmed that in late summer–early autumn most of the reef fishes migrate from the coastal shallow waters to offshore. Throughout the study period the fish abundance was higher inside the reef and decreased significantly at a distance of about 80 m from the structures.

The equipment operated continuously and the EXPERIMENTAL DESIGN acoustic data, received from the echo-sounder, were An acoustic fixed technique was applied for the stored on the computer’s hard-disk in a telegram-based valuation of the fish biomass at an artificial reef structure. Contemporarily, every 60 seconds, the deployed along the coast of the central Adriatic sea system transferred through the radio-modem the data (1.2 nmi offshore, 12 m depth). integration to the Institute, to allow the control of data The hydroacoustic equipment consisted of Simrad acquisition. EY500 echo-sounder and comprised: The whole study period was subdivided into 8 - 1 transceiver with Personal Computer; intervals, each having a duration of about eight days - 4 batteries (charged with solar panels); (Table 1). During each interval the system operated - 1 transducer-multiplexing (SIMRAD MP500); continuously 24 h/day until the hard disk saturation; - 1 timer, to periodically power the system. afterwards the system was reset. All the above equipment, was placed inside a waterproof case on a fixed buoy inside the reef area. Table 1. Study period (21 Jul 96 – 14 Nov 96). Duration of The EY500 system was linked by a radio-modem to the 8 sampling intervals another Personal Computer installed ashore in the 1 2 3 4 5 6 7 8 Institute, which through an appropriately developed Start 21/7 2/8 11/8 20/8 2/9 25/9 2/10 1/11 program automatically controlled the correct functioning of the EY500 system in real time. End 30/7 8/8 17/8 29/8 9/9 30/9 12/10 14/11 Four split-beam transducers (120 kHz) were settled to measure in situ fish Target Strength distribution and 50 density: T1 T2 T3 T4 - transducer 1 (T1), placed 4m-deep on the fixed 40 buoy’s pile, was horizontally oriented towards the 30 centre of the reef; ³ m - transducers 2 and 3 (T2, T3) were located on steel / frames placed on the bottom inside the artificial reef gr 20 and upward-oriented; 10 - transducer 4 (T4) was oriented towards the surface, as T2 and T3, but located on the bottom outside the 0 artificial reef, about 80 m far. 12345678 Every two hours the system was powered on by the Sam p lin g in t erv al timer for a period of 16 minutes and the echo-sounder started pinging immediately. Figure 1. Mean fish biomass recorded by the four transducers during the whole sampling period

100% Density Biomass 100%

80% 80%

60% 60%

NV (%) 40% 40%

20% 20% T1 T2 0% 0%

100% 100%

80% 80%

60% 60% NV (%) 40% 40%

20% 20% T3 T4 0% 0% 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10121416182022 Hour of the day Hour of the day

Figure 2. Mean fish density and biomass recorded by the four transducers during different hours of the day. For each transducer the mean fish abundance (density and biomass) at each hour were normalized dividing by the maximum mean- value (NV%)

density was generally recorded during the early RESULTS AND CONCLUSIONS afternoon, while the highest abundance were The present study demonstrated the suitability of the commonly observed late in the evening, during the fixed hydroacoustic techniques to get ecological and night and early in the morning. A recent study [3] gave practical information on the fish assemblage living similar results on diel acoustic measurements, but the inside and around artificial structures. The values author correlated the 24-h acoustic fluctuations with recorded by the off-reef transducer (T4) were generally the hydrographic factors such as temperature, oxygen lower than those collected by the other ones (Figure 1), level and salinity, that have important influence on fish evidencing that the reef effect on the fish assemblage physiological state. Because Senigallia reef area has was already reduced at about 80 m from the structures. relative stable hydrography, the associated effects of This agrees with the results of other researches the previous factors have probably little influenced the indicating that the local area of influence of an conversion of acoustic data into fish abundance. artificial reef may range from 5-50m, depending on the local environmental conditions and on the reef size [2]. ACKNOWLEDGEMENT Moreover, the fish abundance did not appear The authors are in debt to Dr. Loris Fiorentini homogeneously distributed inside the reef: the highest (IRPEM-CNR Ancona) for his huge effort in the densities were recorded in the central part of the area experimental design, set-up of the echo-sounder (T2), where there is a higher concentration of system and his support in the field work. structures. In harmony with a previous study [1], the acoustical records also confirmed that in late summer– early autumn most of the reef fish species migrate REFERENCES from the coastal shallow waters to offshore where, 1. G. Fabi and L. Fiorentini, Bull. Mar. Sci. 55, 538-558 during the winter months, the water temperature is (1994). about 10-12°C. Finally, the experimentation gave 2. F. Gerlotto, C. Bercy and B. Bordeau, Proc. Inst. Acoust. useful information about the daily behaviour of the 19, 79-88 (1989). fish assemblage living inside the reef (Figure 2). The 3. A. Orlowski, ICES J. Mar. Sci. 57, 1196-1203 (2000). current fish biomass measurements within diel period 4. R.E. Thorne, J.B. Hedgepeth and J.A. Campos, Rapp. P.- corroborated the earlier findings [4]: a minimum of v. Réun. Cons. Int. Explor. Mer. 189, 167-175 (1990).

Using Adaptive Algorithms in Indirect Fish Target Strength Estimation

M. Moszynski

Technical University of Gdansk, Department of Remote Monitoring Systems, ul. Narutowicza 11/12, 80-952 Gdansk

The typical approach to the problem of indirect fish target strength estimation from data collected using single-beam system is based on transforming probability density function (PDF) of measured fish echo level into fish target strength PDF estimate. In transformation algorithms, the PDF of beam pattern, which represents the kernel of transform, has to be known. Furthermore, the calculation of beam pattern PDF depends on assumed distribution (typically the uniform) of fish in the water column. However, due to ill-conditioning of most transformation algorithms, small errors in input data, and inaccuracies in the assumed form of the kernel, may result in large errors at the output. Therefore, most of modern inversion algorithms use sophisticated techniques to achieve satisfying results, but assuming fixed kernel in the inversion procedure.. The paper presents different approach, which uses adaptive construction of the kernel. As a result, the optimal beam pattern PDF is obtained which leads to more reliable estimate of fish target strength PDF, than in “fixed kernel” methods.

INTRODUCTION [5] the authors investigated some of the earlier methods and introduced some novel inverse techniques. Indirect fish target strength estimation when using Generally, two kinds of inverse methods direct and single-beam echosounder data leads to the inverse indirect (iterative), described below, are used. problem in which the probability density function Direct inverse techniques using regularization are based on pseudo-inversion in which Moore-Penrose (PDF) of target strength is estimated from fish # echoes. Mathematically the problem is described by matrix K derived from the kernel K is used. This matrix so-called single-beam integral equation , as a provides the minimum-norm least squares solution to the convolution-like integral of the following form [1]: problem of finding the unknown vector x, that simultaneously minimizes the equation error ||Kx–y||2.  0
p E (E) * p B (B) pTS (E B)dB (1) This pseudo-inverse matrix can be effectively computed Bmin using SVD techniques and some other modification where E represents echo level (E=TS+B) and Bmin is applied by introducing weighting factors w to singular the lower threshold of logarithmic beam pattern j values  , leads to solutions in the form [5]: function included in calculations. j ˆx
 w 
1 [ y,h ]e (2) Due to the hydroacoustic system characteristics WSVD j j j j j the reconstruction is based on incomplete data. This 2 where j and ej are, respectively, the eigenvectors and kind of problem is an example of a statistical linear eigenfunctions of K*K, normalized image is defined by inverse problem (SLIP), often presented as a linear h=K/||K||, and [.,.] is the standard inner product in L2 operator equation y=K x , where observation y is space. represented by echo level peak values PDF pE(E), The EMS technique is an example of indirect inverse linear operator K (kernel) is constructed from technique. The method constrains estimates to be logarithmic beam pattern PDF pB(B) and x is positive and reduces the time needed to converge by unknown function representing fish target strength smoothing groups of estimates per iteration. Every PDF pTS(TS). Statistical linear inverse problems are iteration procedure performed during solution consists of typically ill-conditioned and can be solved using three steps called respectively: expectation, direct inverse techniques based on regularization (i.e. maximization and smoothing. Assuming that observation Windowed Singular Value Decomposition - WSVD) y results from a Poisson process we received equation [4] or iterative ones in which additional constraints describing first two EM steps in a form [5]: are specified (i.e. Expectation, Maximization, ( n
1)  t . ( n )  x y / (3) Smoothing - EMS) [3]. ˆxEMS
K /  K x( n 1)KT i ij 0 INVERSE METHODS SVD technique gives the solution with minimum squared error, which is typically used as a natural A number of references to the earlier work on measure of global goodness-of-fit test for an estimate. indirect target strength can be found in [2]. In [3] and However, due to sine-like nature of eigenfunctions ej of linear operator K, SVD often leads to the artifacts Lake, Idaho survey [4] were used. Over 10000 echoes when interpreting obtained estimate as a probability were acquired by a dual-beam system operating on density function. The EMS estimate represents more 420kHz and post-processed by the sounder software. smooth class of functions than those obtained by Narrow beam data were used for indirect estimation. SVD and can be treated as a good estimate for a class Data from both beams were used to construct the of probability density functions, although resulting estimate only for comparison purposes. Fig.2a. shows mean square error is much larger. This error results pTS estimate obtained after EMS step, its verification in from inappropriate estimate of kernel K and can be the form of actual pE and pE obtained by convolution of minimized preserving smoothness of solution by the pTS estimate with assumed pB estimate is presented in changes introduced in the kernel K of integral Fig. 2b. Fig. 2c shows reconstruction of beam pattern equation Eq.(1). This is particularly relevant for the PDF pB from the actual pE and pTS estimated just before. case of fish target strength estimation as the Fig. 2d presents next two estimates of pTS obtained in construction of the kernel is based on heuristic successive adaptive steps. Table 1 reports the value of assumption made on angular distribution of fish in root-mean-square error for WSVD and three first calculation of beam pattern PDF. Thus, the adaptive EMS steps. Application of AEMS reduces rms estimation algorithm may adaptively change kernel K error and simultaneously represents good estimate for by solving another inverse problem in which the class of probability density functions. Additionally, as a beam pattern PDF pB is reconstructed from echo level result of kernel modifications, more adequate beam PDF pE and target strength PDF pTS estimated just pattern PDF is obtained which leads to more reliable before. New estimate of pB PDF allows calculating estimate of fish target strength PDF, than in new kernel matrix K, which is used in the next step of conventional methods based on heuristic approach. such adaptive algorithm. The process can be terminated comparing the difference between two Table 1. Root-mean-square error of WSVD estimate and successive estimates. successive adaptive EMS (AEMS) estimates. WSVD EMS AEMS AEMS AEMS RESULTS (n=1) (n=2) (n=3) RMS 0.0231 0.1288 0.0477 0.0426 0.0419 To verify the idea of adaptive EMS technique the error data provided by Parkinson from Coeur d’Alene

a) p (TS) b) p (E) c) p (B) d) p (TS) TS E B TS 1000 700 1000 0.14 600 800 800 0.12 500

600 0.1 600 400

0.08 300 400 400

200 0.06 200 200 100 0.04

0 0 0.02 0 -70 -60 -50 -40 -30 -20 -10 0 10 20 -40 -30 -20 -10 0 -70 -60 -50 -40 -30 FIGURE 2. a) First EMS reconstruction of the target strength PDF compared with estimate obtained from dual-beam data (thin line), b) verification of first EMS reconstruction with actual echo PDF (thin line), c) reconstruction of beam pattern PDF compared with assumed one (thin line) d) two successive adaptive EMS estimates (thin line – dual-beam estimate).

smoothing approach for indirect acoustic estimation of REFERENCES fish size and density. ICES J. Mar. Sci., 56: 36-50, 1989. 4. Parkinson, E.A., Rieman, B.E., Rudstam, L.G., A 1. Clay, C.S., Deconvolution of the fish scattering PDF comparison of acoustic and trawl methods for from the echo PDF for a single transducer sonar. J. estimating density and age structure in kokanee. Acous. Soc. Am., 73: 1989-1994. Trans. Am. Fish. Soc., 123:841-854, 1994. 2. Ehrenberg, J.E., A review of target strength estimation 5. Stepnowski A., Moszynski M., Inverse problem techniques. Pp. 161-175 In Y.T. Chan, ed. Underwater solution techniques as applied to indirect in situ Acoustic Data Processing. Kluwer Academic estimation of fish target strength, J. Acous. Soc. Am., Publishers, 1989. vol. 107, No 5, pp. 2554-2562, Fig. 11, Ref. 28. 3. Hedgepeth, J.B., Gallucci, V.F., O’Sullivan F., Thorne, R.E., An expectation maximization and The Three-frequency Method for Classifying the Species and assessing the Size of two Euphausiids (Euphausia superba and Euphausia crystallorophias).

M. Azzali, J. Kalinowski, G. Lanciani, I. Leonori

Institute of Fisheries Research, Research National Council, 60125 Ancona, Italy

In this paper an acoustic method for identifying two euphausiid species and estimating their length is described. The approach is in fact an outgrowth from both the fluid sphere and Bayes rule methodologies. Some practical results of the method are presented.

THE PROBLEM complexity of the acoustic processes that they generate, it would become extremely complicated or The fundamental problem of ecology in Antarctic is even impossible to include them in the fluid sphere the conservation biology of krill Euphausia superba. In model, that is quite effective in the classification of the Ross Sea two krill species dominate the biomass E. species per size. This kind of difficulty can be superba (E.s.) and E. crystallorophias (E.c.). Therefore overcome using both statistical methodology and fluid target species (E.s. and E.c.) identification and their sphere model in an "hybrid approach" to species size estimation is the basic problem in krill assessment recognition. by hydroacoustic methods. A three-frequency method In Ross Sea there are two euphausiid species or for euphausiids discrimination and size estimation has target classes ω1=E.s and ω2=E.c. We assume that the been developed. This paper explores applications of set of acoustic samples, taken in each expedition can the multi-frequency method using data from three be correctly assigned to one of two possible classes on expeditions to the Ross Sea (1980-90; 1997-98 and the basis of net samplings. All the samples that could 1999-2000). be misclassified (mixed hauls, hauls with other scatters) were attributed to a class ω0 and were not THE CLASSIFICATION METHOD considered in the classification process. Samples corrupted by noise were discarded. Therefore the set The fluid model. Theoretical considerations [1,2,3] of the selected measurements s(ωh) h=1,2, acquired in each expedition, was partitioned into two independent demonstrate that the ratio (rfj/fi) of the Mean Volume Backscattering Strength measured at two different sets: s(ω1) acoustic samples assigned to E.s; s(ω2) acoustic samples assigned to E.c. frequencies (fj/fi) from non resonant marine organisms can be used to calculate the spherical radius of their The measurements of each set can be represented by backscattering cross sections. The mean length L of an random vectors. The three components of a random euphausiid with equivalent radius (a) can be calculated vector s v are the outputs from the transducers working approximating its trunk with an equivalent cylinder [4] respectively at 38, 120 and 200 kHz. and equating the volume of the scatterer to the volume The range of each component was divided into a of the equivalent sphere: L = 12.11 *(a) in mm. fixed number (n = 40 Log fj/fi) of equal intervals (1
The hybrid model. The above model assumes a dB). The MVBS calculated for each pair of frequency (fj, fi; fj>fi)) are: ∆fj/fi (∆ fj/fi=10Log(rfj/fi) = 10(Log svfj - deterministic dependence of rfj/fi parameters on the volume of the body of a non resonant animal up to Log svfi). The number of ∆fj/fi in the bins bm (m=1,2 … several centimetres, independently from its species. n) belonging both to the class ω1 and to the class ω2 This is an evident idealization of the reality. define the histogram estimate of the unconditional p.d.f: p(∆fj/fi). The three histograms provide a realistic Differences in the rfj/fi parameters for individuals with similar size but belonging to different euphausiid picture of the dependence of ∆fj/fi on the classes ωh, of species were observed [5] and may be generated by mutual class overlap, of class separability and of class differences in the physical parameters, in acoustic probabilistic structure. behaviour and in shape. These differences are essential The normality test was applied to the conditioned in species recognition, but for the amounts and p.d.f.s. of both classes. The test enabled us to assume that the histograms p(∆fj/fi/ωh), tend to gaussian was also compared with the "single-frequency method" distributions, when the number of observation becomes in the estimation of the biomass of E.s. and E.c. in the large (samples from all the expeditions). We assume surveys carried out in December 1997 and in January each class can be adequately represented by the three 2000. In the single-frequency method the size and the gaussian or prototype p.d.f.s., estimated from the species are deduced from the catches. relative histograms of correctly classified samples. The unconditional probability density functions governing RESULTS the distributions p(∆fj/fi), for each pair of frequency fj, fi (fj>fi) were calculated. Because it is only scarcely The discrimination criteria used to discriminate the known the probabilistic distribution of E.s. and E.c. two species for the three pairs of frequencies are and it can continually change as a result of, perhaps, reported in Table 1. geographical location and environmental conditions we assume that P1=P2= 0.5 (Ph= a priori probabilities Table 1. Threshold levels in dB of the classes ωh; with h=1,2). Using Bayes Theorem, x1 x2 x1 x2 E(
fj/fi)
fj/fi the a posteriori probabilities p(ωh/∆fj/fi) of the classes E. superba E.crystallorophias % ωh, were found, for each pair of frequency fj, fi (fj>fi).
200/120 -0.61 5.23 3.85 8.80 <17 It was assumed that the classifier assigns to the class
200/38 6.51 18.84 18.12 27.66 <12
ωh, each component of a vector generated by a layer m 120/38 6.12 14.08 12.94 20.16 <15 and belonging to an unknown class x (∆ m(x) = (∆200/120; ∆200/38; ∆120/38) m(x)), using the Bayesian decision Using the discrimination thresholds shown in table 1 criterion: decide to assign ∆fj/fi to the class ω1 if the 91.3% of the 103 E.s. aggregations and the 96.6% p(ω1/∆fj/fi) > p(ω2/∆fj/fi) or to the class ω2 if p(ω1/∆fj/fi) < of the 59 E.c. aggregations sampled by the net, were p(ω2/∆fj/fi). Each component of the vector ∆ m(x) is correctly classified. The correlation between the classified independently. The error incurred in biological and acoustical mean length for E.s. data was classifying a component ∆fj/fi, using the above criterion highly significant for 1997-98 (Pearson=0.67; and the divergence (Dfj/fi) between the classes ω1 and p<0.001) and significant for 2000 (Pearson=0.54; ω2, for each pair of frequency fj, fi (fj>fi), was p<0.05). Also for E.c. the correlation resulted calculated to test their separability. The upperbound on significant (Pearson=0.53; p<0.05). As an example of application of the method, the estimations of krill the error E(∆fj/fi) can be expressed in terms of
fj/fi (Table 1). The Bayesian decision criterion classifies biomass were made by multi-frequency method. They st the individual components of a vector ∆ differ from -5% (1 echosurvey: from 12 to 17) up to m(x) nd independently. The final decision rule is to assign one 32% (2 echosurvey: from 19 to 26) from those class to the vector, given the decisions on each of its obtained from single-frequency method. Similar results were obtained from the echosurvey of 2000. components (∆200/120; ∆200/38; ∆120/38)m(x). We used the "majority vote rule": the class assigned at least to two components out of three is assumed as the correct class REFERENCES of the vector. If no majority is got, "no decision" is taken. Three distributions of the equivalent radii are 1. Johnson R.K. (1977). Sound scattering from a fluid sphere re- obtained from a classified vector. The largest members visited. Journal of the Acoustical Society of America, 61:375- of the scattering layer are detected from the couple of 377. frequency (120, 38 kHz), the mean members from the 2. Greenlaw C.F., Johnson R.K. (1983). Multiple frequency couple (200, 38) and the smallest members from the acoustical estimation. Biological Oceanography, 2:226-242. couple (200, 120). The weighted mean (weight = 0.5 (svfj*svfi) ) of the three equivalent radii obtained from 3. Mitson R.B. Simarad Y. Goss C. (1996). Use of a two- the layers sampled by the net were correlated with the frequency algorithm to determine size and abundance of mean length of the relative haul. The obtained plankton in three widely spaced locations. ICE Journal of Marine Science. relationship (a regression line for each species) was used to estimate the value of the length that it occurs 4. Clay C.S., Medwin H. (1977). Acoustical Oceanography: when the mean equivalent radius was calculated. The Principles and Applications. A Wiley-Interscience Publication: classification method was tested using the 544 p. "resubstitution error-count estimator". The same sets s(ω1) and s(ω2) of acoustic and biological data, used to 5. Madureira L.S.P., Everson I., Murphy E.J. (1993b). design the method, increased with the set s(ω ) of Interpretation of acoustic data at two frequencies to 0 discriminate between Antarctic krill (Euphausia superba Dana) mixed data, was used to estimate the performance of and other scatterers. Journal of Plankton Research, Vol. 15, no. the method. The three-frequency classification method 7: 787-802. Simulation of 3D Seafloor Mapping from Multibeam Sonar Data Using Electronic Chart Bathymetry Background

M. Moszynskia, Z. Lubniewskia, J. Demkowiczb and A. Stepnowskia

aRemote Monitoring Systems Department, Technical University of Gdansk, 80-952 Gdansk, Poland bC-MAP Poland Ltd., Narutowicza 11/12, 80-952 Gdansk, Poland

The paper investigates 3D mapping of seafloor, which uses modelling of multibeam sonar echoes reflected from seafloor 3D images, reconstructed from the bathymetry of electronic navigational charts. In the first stage, the 3D relief of seabed surface was derived from bathymetry soundings data contained in vectorised digital navigational charts. Second stage constitutes the simulation of the set of hypothetical multibeam sonar echoes scattered on the bottom surface. Finally, the bottom surface is reconstructed from acoustic data and compared with the images extracted from the charts. The performance of the applied procedure was evaluated and discussed.

INTRODUCTION Also, if possible, the rule of taking the point z only from this cell, which zi or zj belonged to, was satisfied There are known applications of multibeam sonars during that process. D in enhanced bathymetry measurements and seafloor Such prepared triplets of points constitute 3 triangulated irregular network (TIN) describing the relief mapping etc. The paper presents the simple D procedure of seabed mutibeam echoes modelling along bottom surface relief. The example of 3 seafloor relief with application of simulated signals for reconstruction image obtained from bathymetry data and processed of bottom relief. The 3D seafloor images used in the using TIN is presented in Fig. 1. procedure describe real scenes and were reconstructed from navigational charts.

3D SEABED RELIEF RECONSTRUCTION FROM NAVIGATIONAL CHARTS

The 3D seafloor images were reconstructed from the vectorised World Wide Electronic Chart Database CM- 93. In this process, the Delaunay triangulation method was used [1]. The input of this procedure was the set of points - soundings described by co-ordinates in 3D space. The FIGURE 1. The 3D bottom relief reconstructed from first step was to apply the adaptive tree approach to navigational chart data using trianglated irregular divide the data points set into cells of varying sizes, network (TIN) each of which contained no more than m points. D The second step was to obtain the 3 surface by SIMULATION AND 3D BOTTOM constructing the Delaunay triangulation. In this step, RELIEF RECONSTRUCTION the algorithm started from a given point and created the first edge connecting it with the nearest neighbour. FROM ACOUSTIC DATA Then, the successive triangles were created by The second stage was to simulate the acoustic assigning the third point z to a given edge zizj, using multibeam echoes scattered on 3D relief of seafloor the criterion of minimum distance f(z) from z to zizj: surface. The set of M ´ N echo signals corresponding (z - z i )× (z - z j ) , (1) f(z) = to N beams in each of M scans of multibeam sonar

2×(z - z o )× n system over the seafloor surface was generated (see r Fig. 2a). The echoes were simulated using the where n is the unit vector normal to zizj. following formula for i-th echo waveform ei(t) proportional to the signal intensity I, assuming the acoustical simulation algorithm, where a number of domination of incoherent scattering: usually important effects was neglected, e.g. the

2 -4 influence of refraction in water column, the effect of ei(t) = ò e0sbs(qinc ) bi(j) R ds, (2) shading etc. Si (t) In the next stage of the investigation, more advanced acoustic model will be applied, as well as the where Si(t) - bottom surface insonified by i-th beam, e0 experimental validation of proposed methods with use - transmitted signal value, ss(qinc) - seafloor of actual multibeam sonar data will be carried out. backscattering coefficient for incidence angle qinc, bi(j) - i-th beampattern value for transmission angle j, R - distance to transducer array. For the seafloor backscattering coefficient angular 1 dependence, the following formula was used [2]: ... s q A exp aq 2 B cosb q2 , (3) s ( inc ) = (- inc ) + inc M with A, B, a and b values evaluated using the results of N research shown in [2]. Hypothetical sonar was modelled using parameters of EM3000 multibeam sonar having operating 1 frequency 300 kHz and 80 beams with resolution of z 1.5°. The sonar was assumed to operate 300 meters above the seabed surface. The distance between y consecutive scans was approx. 6 m. x The next step was to reconstruct the bottom relief from simulated data. It was performed using the delay a) times ti, i = 1, 2, ..., N evaluated for each echo in one scan. This time was estimated as a point, where the signal exceeds firstly the 75% of maximum value. This set of delay times ti was used to reconstruct the geometric relief of bottom z(x) along the vertical crossection corresponding to the single scan, by interpolating (xi, zi) points, where: ct ct x = i sinq , z = H - i cos q , (4) i 2 i i 2 i c - sound speed in water, H - bottom depth, qi - angle of i-th beam acoustic axis. Assuming that the ship moves steadily along the y b) axis (Fig. 2a) and consecutive multibeam scans correspond to successive values y the relief z = f(x, y) i FIGURE 2. a) The bottom surface with indicated of the whole investigated seabed surface was geometry of simulated mutlibeam data acquiring reconstructed by interpolation. procedure; b) the seafloor 3D image obtained from sonar data using reconstruction algorithm. RESULTS AND CONCLUSION REFERENCES The geometry of the experiment and the reconstructed 3D bottom from simulated multibeam 1. Demkowicz J., Stepnowski A., 3D imaging of seabed echoes are presented in Fig. 2a and b respectively. from electronic chart bathymetric data, Hydroacoustics 4, It is easy to seen while comparing the pictures from 37-42 (2001). Fig. 2a and b, that the reconstructed image is quite 2. Lurton X. et al., "Shallow water seafloor characterization consistent with the original one obtained from for high-frequency multibeam echosounder: image soundings. This justifies the practical utility of the segmentation using angular backscatter", in method and confirms adequacy of the algorithm. SACLANTCEN CP-45, La Spezia, 1997, pp. 313-322. D However, it must be pointed out that similarity of 3. Lubniewski Z., Moszynski M., Modelling the seafloor 3 obtained images might be also due to simplicity of relief and its reconstruction from multibeam sonar data, Hydroacoustics 4, 153-156 (2001). Jet Noise At High Reynolds Numbers Using Large-Eddy Simulation and Lighthill's Analogy

D. B. Scheina,b and W. C. Meechama

aDept. of Mechanical & Aerospace Engineering, Univ. of California, Los Angeles, California, USA bNorthrop Grumman Corporation, Integrated Sys. Sector, Air Combat Systems, El Segundo, California, USA

A computational fluid dynamics model for free, heated jet flow and resultant far-field sound has been developed, which uses large- eddy simulation (LES) and Lighthill’s acoustic analogy. The procedure involves no adjustable parameters. A deductive, subgrid scale (SGS) model (based on a Taylor series expansion of the filter function is used for the large eddy simulation. The model can be run on a Personal Computer, and simulations have been tested using published experimental mean flow field and RMS fluctuation data for a turbulent, free jets. We have addressed large Reynolds number, high subsonic (compressible) flow with realistic geometries. In our simulation, Gaussian random velocity fields are introduced at the jet exit to excite the turbulence. The far-field sound and directivity are computed using the time-derivative form of Lighthill’s source-integral result which is integrated in time. Simulations for two power settings of a WR19-4 turbofan engine exhaust (Ma=0.45 and Ma=0.78) were performed, and propagated jet noise results compared with experimental acoustics data. The agreement is within 2 dB. The experimental agreement shows that the computed turbulence intensity has an error of but 3%. Other research applications of this approach include the automobile tire noise due to small jets of air from tread row gaps, background noise in blowdown wind tunnels, and more.

R  ~ R  ~ ~ R R R GOVERNING EQUATIONS AND ( U k ) ( U k U 1 ) P kl kl + = - + + (2) R R R R R FILTERING t x xk xl xl The LES decomposition is represented where the stress tensor is given by > as F = F + F . The filtered variable is defined by the V  ~ ~ ~
kl (UQ - U U) with UQ UkU. . The filtered momentum convolution integral, k equation is solvable (closed) if we provide a model F( x ) = * F i ( z )Gi ( x - z,
i )d z (1) D for kl . The full system of equations can be found in [2].
where ( x,
) is a suitable spatial filter, F can be Gi SUBGRID SCALE TURBULENCE AND termed the large-scale part of F while the residual portion, F’, is the small-scale, or subgrid part. A ACOUSTIC MODELING Smagorinsky [1] was the first to propose a model for Gaussian filter was used in this study, the SGS stresses, assuming that they follow a gradient- 2 3/2 (-6 r 2 /
2 ) G( r ) = (6 /
) e with diffusion process analogus to heat conduction. His eddy

2 
2 
2 1/ 2 ( 1 2 3 ) where ∆i is the filter width in th 2 ~ ~ the i direction. R  R  RU RU k  [  k 1 R R R R Turbulent flow field equations are derived by x  x1 12 x m x m decomposing the dependent variables in the conservation ~ ~ ~ 2  R 2 U R 2 U 2 1 RU R R equations into time mean and fluctuating components. ( )2 k 1  ( )2 k R R R R  R R R Here mass-averaged variables are defined according to 12 2 x m x n x m x n 12 x m x m x n ~ ~ ~ ~ ~ 2 R 2 RU RU R RU RU F = F /  in terms of the ordinary filtered variables, ( )2 1 k ] [ T ( k  1 )  ~ 12 Rx mRx nRx nRx m Rx1 Rx1 Rx k where the decomposition is given by F  F  F> and ρ is R 2 ~ R 2 ~ U k  U1 the fluid density. Filtering, as defined above, is denoted T ( )] R 2 Rx Rx by the overbar and mass weighted averaging by the tilde. x1 1 k Only velocity components and thermal variables are viscosity is made up of terms quadratic in the mass-averaged. Fluid properties like density and pressure T gradients of the filtered velocity. are treated as usual. The Smagorinsky model produces too much dissipation. All theories of turbulence are faced with the closure A second model is needed, in addition. Lee and problem arising from the basic nonlinearity of the Meecham [3] propose a model based on a Taylor series governing equations. Modeling of some statistical expansion. This model, using the first two terms, added quantities is essential to close the problem. to that of Smagorinsky, yields for the needed SGS term Direct filtering of the momentum equation yields, of (2). The last term is the Smagorinsky model. Lee and Meecham tested their deductive model by Overall Sound Pressure Level (OASPL) was comparing with a near Gaussian probability distribution calculated, and normalized to a reference radius of 0.30 for the velocity field. Their results showed that important meter. Experimental acoustic data were acquired for correlation coefficients for the deductive model were using a radial arc of ground plane microphones. 0.88 and 0.97 in the case of truncations up Measured data were normalized to 0.30 meter free field, to
2 and
4 ,respectively, deemed excellent. far field levels by correcting for spherical spreading, atmospheric propagation and absorption, and ground effects. Comparisons of measured and simulated OASPL’s versus radiation angle are made in the Figure. OASPL’s for measured data include only frequencies which contain significant jet mixing noise energy. Agreement is within 2 dB. It is emphasized that there are no adjustable parameters. The simulation results are in excellent agreement with the measured data in the aft quadrant (O90L) where the engine noise is dominated by the jet mixing source. Lesser angles, where internal engine noise dominates, are not simulated. SUMMARY AND CONCLUSIONS The computational structure is such that it can be carried out on a PC in manageable time. Large Reynolds number, Mach 0.46 and Mach 0.78 jet flow from a small turbofan engine have been computed. Computed far field Lighthill [4] developed the standard theory of aerosound jet mixing noise levels are in close agreement (within 2 for free turbulence. For large x, the sound field is dB) with those measured during engine field-testing. The

( x,t) - A = sound intensity is proportional to the sixth power of the fluctuation velocity. A 2dB variation between the R2 ( y, ) xi x j T ij -5/2 computed sound using LES and the measured sound * [ ](1 - M c cos ) d y 4 3 R 2 4 cA| x - y| V t means the LES of the fluctuation intensity has an error of using the spatial variation of the retarded time, less than about 8%. The aerosound measurement, because of the sixth power leverage, is extraordinarily where  = t - (| x - y | / c A ) is the retarded time. Here demanding for the turbulence model. The computational Mc is approximately 0.5Mj and procedure presented here shows great promise for noise  ~ ~  2   reduction without the need for massive experiments. T ij = U iU j - ij + ( p - c ) ij - ij.

TURBULENT FLOW FIELD AND ACKNOWLEDGMENTS The first author is grateful for support provided by MIXING NOISE RESULTS Northrop Grumman Corporation’s Fellowship Program, The computational model was incorporated into the ANSWER software package developed by Analytical and REFERENCES Computational Research, Inc [5]. The basic turbulence [1] Smagorinsky, J., 1963, Mon. Weather Rev. 91, p. 99. model is the time-honored k-ε. [2] Schein, D.B. and Meecham, W.C. 2001, Am. Soc. of Numerical simulations were performed for a Mechanical Engineers, New Orleans, May 29-June 1. subsonic free heated jet with operating parameters based [3] Lee, C.P., and Meecham, W.C., 1984, "A Deductive on experimental data for a WR19-4 mini-turbofan engine Model for Subgrid-Scale Reynolds Stress," UCLA report, during static operation. The circular exit area is 0.02 School of Engineering and Applied Science. meter2, The engine was operated with exhaust Mach [4] Lighthill, M. J., 1962, Proc. Roy. Soc. A267, p. 147 number 0.46 and 0.78 based on nozzle exit conditions. [5] Runchal, A. K., 1994, “ANSWER User’s Manual,” Turbulence quantities within the flow domain were Analytical and Computational Research Inc. [6] Lau, J. C., Morris, P. J., and Fisher, M. J., 1979, Journal calculated for each of the 2048 time steps by subtracting of Fluid Mechanics, Vol. 93, pt. 1, pp. 1-27. the mean field values from the time-dependent values. Normalized rms axial velocity fluctuations on the jet centerline compare well with cold jet data measured by Lau, et al [6]. Lifting Surface Hydroacoustics At High Reynolds-Number

D. A. Bourgoyne, C. Judge, J. M. Hamel, S. L. Ceccio, and D. R. Dowling

Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48105, USA

The unsteady turbulent flow on or near lifting surfaces is often a source of hydroacoustic noise. Turbulence in these regions may produce noise directly and hydrodynamically-forced structural motions may radiate noise as well. Interaction between a hydrofoil's unsteady vortical wake and its own structure may produce undesired self-sustaining vibrations. This paper reports on a series of recent experiments focused on these phenomena high Reynolds numbers. The tests were conducted in the United States Navy's William B. Morgan Large Cavitation Channel at flow speeds up to 18.3 m/s on a two-dimensional test-section- spanning hydrofoil (2.13 m chord, 3.05 m span, 0.171 m maximum thickness) at angles of attack between –1° and +1°. The measurements include foil surface static and dynamic pressures, foil vibration, LDV-determined average flow velocities and turbulence quantities, and PIV flow fields in the immediate vicinity of the foil's trailing edge.

INTRODUCTION turbulence (0.2% nominal) water tunnel with a 6:1 contraction ratio and a 3.05 m by 3.05 m by 13 m test Flow-induced noise may be generated when wall section (Fig. 1). bounded turbulence interacts with the trailing edge of a flow-control surface or lift-generating hydrofoil. At low mach number, two mechanisms dominate hydrofoil noise production. (1) Broadband turbulent- boundary-layer surface pressure fluctuations may scatter from the foil’s trailing edge. (2) Vortical flow oscillations may form in the foil’s near wake leading to narrowband quadrapole sound sources whose near- field pressures scatter from the foil as dipole sound. If FIGURE 1. Schematic of the WBM-LCC. the near-wake vortex-shedding frequency coincides with a vibrational resonance of the foil structure, a The test model was a cast two-dimensional self-sustaining flow-induced vibration may occur. hydrofoil made of Ni-Al bronze with a 2.134-m chord This condition, known as “singing”, typically enhances (c) and 0.171-m max thickness (t) which spanned the noise radiation and may cause structural vibration WBM-LCC test section where it was centered problems. This paper reports the findings from a new vertically and longitudinally. The foil cross section high-Reynolds number experimental effort focused on was generic for Naval propellers having moderate these phenomena. A thorough review of prior research thickness and camber (f). The shape is that of a in this area is provided in [1]. NACA-16 (t/c=0.08, f/c=0.032) with two The goal of these experiments is to provide modifications. First, the bottom (pressure side) of the fundamental insight into the fluid mechanics of foil is flat aft of 28% chord. Second, the foil terminates trailing-edge noise generation in marine propulsion with rounded bevel starting near 97% chord. The systems at Reynolds numbers typical of actual ship 8 second modification leads to a compact region of flow propellers (~10 ). In addition, these experiments will separation in the vicinity of the trailing edge. provide a unique high-Reynolds-number experimental Experiments were conducted at angles of attack of database for testing and development of turbulence –1°, 0°, and +1° (measured from the flat side of the and computational fluid dynamics (CFD) models. foil). The primary test speeds were 3.0, 6.0, 12.0 and 18.3 m/s yielding chord-based Reynolds number EXPERIMENTS values of 6-10 million, 16 to 20 million, 29 to 39 million, and 46 to 61 million, respectively, when water The experiments were conducted at the US Navy’s temperature variations are taken into account. The William B. Morgan Large Cavitation Channel (WBM- maximum foil lift load was measured at 730 kN. LCC) in Memphis, TN. The WBM-LCC is a low Measurements of the flow on and near the foil were made with an external two-component laser-Doppler velocimetry (LDV) system, static pressure taps, of in rad./s, q is the free-stream dynamic pressure, and dynamic pressure transducers, foil-internal Uinf is the free stream speed. accelerometers, and a two-component particle imaging As expected, the spectra depend on transducer velocimetry (PIV) system. Additional experimental location. The spectrum with the highest frequency details and results from the first phase of testing are content and least low frequency energy occurs farthest available in [2]. upstream under the attached part of the suction side boundary layer. The spectrum showing the least high frequency content and the greatest low frequency RESULTS & CONCLUSIONS energy occurs closest to the trailing edge, well aft of the suction side boundary layer separation point. Normalized temporal power spectra of foil surface Results are comparable at the other test speeds. pressure fluctuations, Spp, in the vicinity of the Overall, the surface pressure fluctuation spectra are hydrofoil’s trailing edge are shown on Figure 2 for smooth and do not display a peak near (omega)yf/U’ four suction-side transducers that span the location of of unity that would indicate organized near-wake boundary layer separation. The normalizations of both vortex shedding. This suggests a lack of vortex axes are taken from [1] (see Fig. 11-26). Each structure in this foil’s near wake in spite of its presence spectrum has been truncated when the transducer noise in prior wind tunnel investigations of a geometrically level begins to corrupt the signal. Here, the wake similar foil at lower Reynolds number [3]. The thickness, yf, is approximately 2 cm; omega has units

FIGURE 2. Pressure fluctuation spectra from four flush-mounted transducers located at 93% (A), 94.5% (B), 95.75% (C), and 98.8% (D-1) chord at a flow speed of 3.0 m/s and 0° angle of attack. vibration measurements support this contention. REFERENCES Acceleration spectra were not remarkable and root- mean-square acceleration levels were less than 0.1 g. 1. W. K. Blake, Mechanics of Flow Induced Sound and Additional tests are planned in the near future with Vibration, Vols. I and II, Orlando, FL, Academic Press, different foil trailing edges. 1986. 2. D.A Bourgoyne., S.L. Ceccio, D.R. Dowling, S. Jessup, J. Park, W. Brewer, and R. Pankajakshan. "Hydrofoil ACKNOWLEDGMENTS turbulent boundary layer separation at high Reynolds numbers," 23rd Symposium on Naval Hydrodynamics, This research is sponsored by Code 333 of the Val de Reuil, France, September 2000. United States Office of Naval Research. Significant 3. J. Gershfeld, W.K. Blake, C.W. Knisely “Trailing Edge technical assistance was provided by personnel from Flows and Aerodynamic Sound,” Paper no. 88-3826-CP, the United States Naval Surface Warfare Center – AIAA Thermophysics, Plasmadynamics, and Lasers Carderock Division. Conference, San Antonio, Texas, June 1988.

Acoustic Turbulence in a Rectangular Channel

S. Nomura, Y. Hayashi and K. Murakami

Department of Mechnical Engineering Ehime University, 3 Bunkyo-cho,Matuyama, Ehime,Japan

The effect of ultrasonic vibration in the Reynolds number (Re) range of 1500 to 6000 on fluid flow in a square channel was investigated experimentally. By applying ultrasonic vibration to laminar flow which was produced by the agitation or distubance of cavitation bubbles, the transition to turbulent flow from laminar flow was promoted downstream. Turbulence intensity by ultrasonic vibration is larger at the sound pressure antinode due to the influence of a standing wave. Since it is possible to control the fluid flow from outside due to the easy transmission of ultrasonic vibration in liquids, this technique can be applied to fluid flow in various channels as a non-contact turbulence promoter.

1. INTRODUCTION transducers are bolted Langevin PZT-type vibrators with resonance frequencies of 25kHz, and ultrasonic In generally, since ultrasonic energy is small power in the range of 10W to 50W. Tap water was compared to the kinetic energy of fluid flow, it is used for the test liquid and the water flows by the difficult to generate large-scale motion of the fluid potential head difference of the tank installed on the directly by the ultrasonic energy, and to control the upstream side and the downstream side of this channel fluid flow itself. However, the direction and the flow respectively. The water temperature is limited to pattern of the fluid might be greatly changed by very 12 ± 2℃. The mean velocity and turbulence intensity small perturbations, as is made clear from the (rms of the instantaneous velocity deviation from the phenomenon of separation of boundary layers or the mean velocity) were measured by a laser-Doppler turbulent transition from laminar boundary layer. In velocimeter. It is set to be x=0mm on the central axis this study we propose the promotion of turbulence by of the transducer in the channel, and the downstream ultrasonic vibration as a basic method of fluid control direction is taken to be positive. The streamwise method using ultrasonic energy. The turbulence velocity in the channel central section was measured in intensity by ultrasonic vibration was measured, and the the range of 0mm

Fig.１ Experimental apparatus larger at the sound pressure antinode due to the

influence of a standing wave. The acoustic turbulence decreases when it is over 2500. intensity of the power output 40W is 6mm/s on In high Re regions, turbulence can be restrained average, with a maximum of 11mm/s, and the mean by the ultrasonic vibration near the wall. Acoustic turbulence intensity without ultrasound is 2mm/s. cavitation near the wall causes the reduction of Consequently, there is a three-fold increase in turbulence intensity under turbulent regions. These turbulence intensity on the average, and locally, a five- results suggest the possibility of reduction of the wall fold increase was obtained. friction loss by acoustic cavitation. This seems to be an Turbulence intensity at the channel center ( y=25 analogous mechanism to the fact that turbulent mm) is plotted as a parameter of Re as shown in Fig.4. intensity is reduced when air bubbles exit along the Turbulence intensity becomes greater than that for no wall[1]. In the transition region anywhere in the vibration under both the Laminar flow region of less channel, the ultrasonic vibration has the effect of than Re=1500 and the turbulent flow region of more restraining the turbulence of the channel flow. than Re=4000. On the other hand, turbulence intensity Figure 6 shows the result of the comparison of with ultrasound is smaller than without ultrasound in the effects of ultrasonic vibration at the downstream the range of Re=2500 to 3000. region of x=800mm. Downstream, the velocity profile Figure 5 shows the turbulence intensity measured approaches the turbulent velocity distribution where at y=6mm where Urms took the local maximum within the flow has a rapid velocity gradient near the wall. the range of Re=1500-2500. Urms with ultrasound Turbulence intensity hardly changes before and after increases when Re is less than 1500, whereas it ultrasonic vibration, however, the transition to turbulent flow from laminar flow is promoted in downstream. Re=1500 0.1 Without ultrasound We also investigated 45kHz vibration. The result With ultrasound ( P=40W ) was that, when the ultrasonic output power is the same, a large disturbance is locally generated by 25kHz 0.05 vibration, and by 45kHz vibration, a small disturbance is generated within the wide range in the channel. Velocity [m/s] 25 0 Without ultrasound 0 10 20 30 20 With ultrasound ( P=40W ) t [s] Fig.2 Local variation of the velocity with time, y=38mm 15 [mm/s]

0.06 rms 10 U 0.05 Re=1500 5 0.04 0 [ m/s ] [ m/s 0 2000 4000 6000 0.03 U rms Reynolds number Urms 0.02 U ( P=40W ) Urms ( P=40W ) Fig.5 Variation of Urms with Re, x=0mm, y=6mm U , U U , FIDAP 0.01 0.06

0 10 20 30 40 50 0.05 Re=1500 y [ mm ] 0.04 Fig.3 Velocity profile and Urms in the channel, x=0mm [ m/s ] [ m/s 0.03 U

25 rms Urms 0.02 U ( P=40W ) Without ultrasound Urms ( P=40W ) Laminar 20 With ultrasound ( P=40W ) U U , 0.01 Turbulent 15 0 10 20 30 40 50 [mm/s] y [ mm ] rms 10 U Fig.6 Velocity profile and Urms , x=800mm 5

0 REFERENCES 0 2000 4000 6000 Reynolds number 1. N.K. Madavan, S. Deutsch and C.L. Merkle, J. Fluid Fig.4 Variation of Urms with Re, x=0mm, y=25mm Mech., 156, 237-256(1985).

On the Study of Theory and Application of the Coupled–Mode Parabolic-Equation Method Based on the WKBZ Theory Zhaohui Peng, Fenghua Li and Renhe Zhang

National Laboratory of Acoustics, Chinese Academy of Sciences, P.O.Box 2712, Beijing, 100080, China

An efficient, numerically robust algorithm for calculating sound propagation in the range-dependent waveguides, which is called CMPE (Coupled Mode-Parabolic Equation), is introduced, on the base of the generalized phase integral (WKBZ) theory. The CMPE is a hybrid model expressed in terms of the normal modes and mode coefficients. CMPE uses a PE approach in a radial direction and normal modes in the depth direction. The Numerical calculation of a typical problem shows that CMPE has high accuracy and fast speed. And numerical example of the broadband pulse propagation is also presented.

1 ∞ INTRODUCTION − − 1 = 2 [] φ p(r, z) r ∑ kn (r) 2 un (r) n (z;r) (1) = Combined with the coupled mode theory and n 1 where the local eigenvalues k and normal modes parabolic equation method, a new model has been n [1] φ developed by Abawi, Kuperman and Collins ,thatis n (z;r) satisfy the coupled-mode parabolic-equation (CMPE) method. ∂ 2φ (z;r,θ ) ~ CMPE solutions are expressed in terms of the normal n + []k 2 − k 2 φ (z;r) = 0 (2) ∂ 2 n n modes and mode coefficients, which satisfy coupled z ∞ horizontal wave equations and can be solved with PE and φ φ dz = δ (3) ∫ n m nm method. It is practical to apply the coupled-mode 0 parabolic-equation to large-scale problems and ~2 = 2 + 1  1 ∇2 ρ − 3 ()∇ρ 2  where k k  2  . possibly even global scale problems at low frequency. 2  ρ 2ρ  On the progress of resolving sound propagation In this paper a fast and accurate algorithm [3] for problems with CMPE, the computation of local modes solving the characteristic equation Eq.(2) is used based and coupled coefficients is one of the most difficult on WKBZ theory. aspects, and takes most computer time. Therefore, an The u in Eq.(1) is the mode coefficient, which efficient algorithm of the computation of local modes n and coupled coefficients is the key to improve the satisfies ∂! efficiency of the coupled-mode parabolic-equation. u = − " + ! Ar u iKu (4) Based on WKBZ [2] theory, a new eigenvalue finding ∂r ! algorithm is presented, which can calculate the = T where u [u0 u1 u2 # uM ] is the mode eigenvalues efficiently and accurately. By combining coefficients vector. The diagonal matrix K is the the improved WKBZ theory and the coupled-mode eigenvalues matrix. The coupling coefficients parabolic-equation theory, a new range-dependent matrix A is defined by propagation model (CMPE) is presented. r ∂φ The numerical results show that CMPE has high j A = φ dz .(5) accuracy and fast speed. The effect of boundary r,i, j ∫ i ∂r variability on the transmission loss is studied in this Eq.(4) can be solved with numerical method. paper. And numerical example of the broadband pulse propagation is also presented. EXAMPLES

CMPE METHOD BASED ON WKBZ An example on sound propagation in slope bottom oceans is solved in this section. In Fig.1, the numerical The series solution of CMPE method based on solutions of transmission loss calculated by CMPE, WKBZ can be written COUPLE [4] and RAM [5] are compared. The results are in good agreement with each other. And CMPE is much faster than COUPLE and RAM. FIGURE 1 Transmission losses for an example on sound propagation in slope bottom oceans. The water depth is 200m at range of 2 km decreasing linearly to 80 m at range of 8 km. The point source with 100 Hz is placed at 30m and the receiver depth is 30m. The water sound velocity is 1500 m/s, and the bottom velocity is 1600 m/s. the density ratio between bottom and water is 1.6 and the bottom attenuation is 0.5 dB/wavelength. The solid line is transmission loss calculated by CMPE taking 101 seconds computational time. The dashed line is calculated by COUPLE with 4518 seconds, the dotted line is calculated by RAM with 5489 seconds.

FIGURE 2 Stacked time pulse vs. range. Note the splitting up of the signal with range in three distinct wave packets corresponding to the first three modes of the waveguide. The lines are pulses at ranges of 5, 10, 15, 20, 25, 30 km form the source placed at 10 m with bandwidth of 450 ~ 550 Hz, respectively. The water sound velocity is 1480 m/s, and the bottom velocity is 1587 m/s. the density ratio between bottom and water is 1.6 and the bottom attenuation is 0.3 dB/wavelength. (a) Slope bottom. The initial water depth is 30m at a range of 2 km increasing linearly to 50 m at a range of 30 km. (b)Plane bottom. The water depth is 40 m.

Another numerical example on broadband pulse ACKNOWLEDGMENTS propagation is also presented in this section shown in Fig.2. The work was supported by the National Natural Science Foundation of China.( Grant No. 10074070) CONCLUSIONS EFERENCE On the base of the generalized phase integral (WKBZ) theory, a new approach of coupled-mode 1. Abawi, A. T., Kuperman W. A. and Collins M. D., J. Acoust. Soc. parabolic-equation (CMPE) method is studied in the Am., 102(1), 233-238 (1997) range-dependent waveguides. Examples of 2. Zhang, R., Liu, H., and He, Y., Chinese Jour. of Acous. (in Chinese), 13(1), 1-12 (1994) transmission loss and pulse propagation have indicated 3. Peng,Z.andLi,F.,ScienceinChina(SeriesA)(inChinese), that CMPE is efficient and high precision for 31(2), 165-172 (2001) range-dependent waveguides. 4. Evans, R. B., J. Acoust. Soc. Am. 74, 188-195 (1983) 5. Collins, M. D., J. Acoust. Soc. Am. 93, 1736-1742 (1993) Transfer Function of Structure-borne Noise to Underwater Radiated Noise

J.-S. Kim, H.-S. Kim, H.-J. Kang and S.-R. Kim

Acoustics Research Group, Korea Institute of Machinery & Materials, Daejeon, Korea

A comparison between theoretical and measured transfer function, which relates structure-borne noise source level to underwater radiated noise, of a naval ship is presented. Transfer functions are obtained by dividing underwater radiated noise by the value of structure borne noise source strength below machinery mounts. In prediction, Statistical Energy Analysis (SEA) of the whole ship structure is used to get vibration levels of hull plates. Then, far field radiated noise is calculated by summing up radiated sound from all wetted hull plates below water line. In addition, underwater sound pressures at the distance of 1 m away from the hull were measured to get experimental transfer functions. The two transfer functions are compared to show reasonable agreements in spite of the subtle physical differences between each other.

INTRODUCTION in which W is the acoustic power of radiated sound,  and c are density and speed of sound in the   2
seawater. rad ,i , Ai , and v i are the radiation Underwater radiated noise (URN) prediction from efficiency, the area and the space-time averaged naval ships under construction is of great importance. velocity squared of plate i , respectively. Finally, N is It is known that structure-borne noise is a predominant the number of plates. Note that equation (1) holds noise source of URN at low speed in general. under the assumption of individual plate vibrating One approach to estimate URN due to structure-borne incoherently. noise is based on the equivalent hull forces that transmitted from the feet of equipment to the hull plate When applying SEA to noise prediction of ship, one through mountings and its seating. URN is the result of constructs the so called SEA model of the whole ship the sound radiation from hull subjected to the point structures. Structural plates such as hull, deck and forces, i.e. equivalent hull forces obtained above. The bulkheads together with compartments such as cabins other approach is to use the result of SEA, i.e. the are idealized by thousands of inter-connected sub- average vibration levels of SEA elements, in systems. Fig. 1 shows an example of SEA model as combination with radiation efficiencies of hull seen from the bottom of the vessel. plates[1].

We used the second approach to calculate URN. The URN was divided by the value of structure-borne noise source strength of equipment considered to yield the transfer function. In addition, similar transfer function was obtained experimentally by utilizing the data from overside ship acoustical survey performed during sea trials. The comparisons between the two transfer functions are presented in this paper.

Prediction of urn

The total acoustic power of radiated sound into water FIGURE 1. A SEA Model of Ship Structures. by vibrating plates is given by SEA computes energy densities of each sub-system by N     2
solving SEA equations. The energy densities are W c rad ,i Ai v i (1) i
1 closely related with the space and time averaged velocity squared, appeared in (1), for plate members. Therefore, we can use the SEA solutions directly to obtain the acoustic power radiated into the water from the wetted hull surface by using (1). As for radiation efficiencies of plates in contact with water like ship’s hull, one can use the formula suggested by Uchida et al. [2].

Once the acoustic power is obtained, the far field pressure is given as follow under the assumption of spherical spreading from point source.

W  2 r 2 p 2 / c (2) FIGURE 2. Transfer Function : Diesel Generator. After some calculations, the radiated pressure at 1 m apart from the source is given as   
L p Lw 10 log f 54 (3)

where, L p is the URN level (dB ref 1 Pa, 1 m, 1 Hz), -12 Lw is the acoustic power level (dB ref 10 W), and
f is the bandwidth.

Structure-borne noise transfer function

We defined the structure-borne noise transfer function FIGURE 3. Transfer Function : F.O.T. Pump. as exact definitions, i.e. prediction for far field and TF  L
L (4) measurement done near hull plates. This phenomenon, p a although not conclusive, could be utilized when analyzing noise transmission path of machinery with in which TF is the transfer function of structure-borne relatively small source levels or refining URN noise, Lp is the URN as described in (3), and La is prediction method in an effective way. the 1/3 octave band acceleration level (dB ref 10-5 m/sec2) at the position below the equipment mount.

During overside acoustic surveys, a hydrophone was ACKNOWLEDGMENTS placed 1 m apart from the hull to measure the underwater sound pressure induced by the operation of This work was partially supported by a grant from the individual equipment. At the same time, accelerations Critical Technology project of the Ministry of Science below the equipment mount were also recorded. and Technology, Korea.

We calculated the experimental transfer functions using these two measurement results, and compared REFERENCES with those of theoretical values in Fig. 2 and 3 for a diesel generator and a pump, respectively. 1. Hyun-Sil Kim, Jae-Seung Kim, Hyun-Ju Kang and Sang Ryul Kim, An application of SEA to ship noise prediction, NOVEM International Conference, Lyon Concluding remarks Congress Center, 31 Aug. – 2 Sep., 2000.

It is an interesting fact that the two transfer functions 2. S. Uchida, Y. Yamanaka, K. Ikeuchi, K. Hattori and K. compare reasonably well in spite of the differences in Nakamachi, Prediction of underwater noise radiated from ship’s hull, Bulletine of the Society of Naval Architectures of Japan, No. 686, 36-45, (1986). Flow Noise and Functional Models of Wall-Turbulent-Pressure

E. B. Kudasheva, L. R. Yablonikb

aSpace Research Institute of Russian Academy of Sciences, 117997 Moscow, Russia b I.I.Polzunov Scientific and Development Association on Research and Design of Power Equipment (NPO CKTI)., S.-Petersburg, Russia

Two models of spatial characteristic functional of near-wall-turbulent pressure fluctuations are suggested and analyzed in view to develop the method of experimental investigation of characteristic functional of near-wall turbulent pressure field. They are a Gaussian model for jet flow and a Poissonian model for turbulent boundary layer. The functional approach permits to reduce experimental investigation of turbulent pressure field characteristic functional to measuring a limited number of parameters and dependencies typical of the studied turbulent flow- against type.

EXPERIMENTAL METHOD OF and the characteristics functional is transformed into a CHARACTERISTICAL FUNCTIONAL characteristics function of n-dimensional distribution of probability: From Haddle and Skudrzyk paper[1] the response of a flush-mounted transducer to the pressure field in a φs (λ 1 ….. λ n ) = φ x1 ... x n (γ1..… γn) (4) turbulent boundary layer is known as the flow noise.The most comprehensive are continual statistical models of spatial structure of wall-pressure-fluctuations р(x) described by characteristic functional Φ[υ(x)] = THE CHOICE OF FUNCTIONAL and representing full statistical MODELS OF TURBULENT PRESSURE description of a random pressure fluctuation field p(x,t). The present work analyses forms of analytic Turbulent flows different in statistical nature of turbulent representation of characteristic functional of turbulent fluctuations generation are described by different pressure fluctuations. The functional approach developed functional models. In the case of a jet flow when pressure by the authors of the present work [2] makes it possible fluctuations are caused by external turbulence and the to obtain an exhaustive description of random field p(x) distance between the sources and the point of based on experimental investigation of characteristic observation is not too small, the well-known integral functional of wall-turbulent pressure field. Estimated description of pressure fluctuations allows to represent experimentally, characteristic functional Φ[υ (x)] is near-wall fluctuations as a sum of great number of defined on a set of functional arguments υ = λ K(x), statistically independent components related to different where λ is the sensitivity of a transducer, K (x) is its zones of the turbulent flow. Random values of the kind, pulse characteristics. Because of the sensor averaging as a rule, have asymptotically normal distribution that effects the characteristic function of the transducer signal implies a model of a Gaussian field whose characteristic φs(λ) = gives the characteristic functional: functional has the form

φs (λ) = = Φ[λK(x)]. (1) ΦG [υ (x)] = exp {-½ ∫∫ υ(x1)υ(x2)R (x1, x2)dx, dx2} (5)

For probes with rather small reception surfaces so that Here R(x1, x2)is the correlation function R=. pulse characteristics is reduced to delta-function λ K (x) The characteristic function of the signal of transucer: λ γ0 δ (x – x0), the characteristic functional is : φs (λ) ≈ = φp (λ γ0) (2) 2 φ sG = ΦG [ K(x)] = exp [- λ / 2 ∫ (
) R (
) d
] , (6) corresponding to single-point probability distribution of (
) = ∫ K(x) K(x+
)dx is the impact function of the pressure. In the case the receiver is an array of n point receiver. The wave form of the characteristic function is probes with sensitivity λ, pulse characteristics : 2 φsG = exp [- λ /2∫S(k)E(k)dk] , (7) where S(k) = ∫ (
)exp(-ik
)d
is the wave characteristic λ K (x) ∑ λ i γ0 δ (x – x i) ≡ ∑ γ i δ (x – x i) , (3) of the receiver, E(k) is the frequency-wave spectrum of functional models studied in the work, or their the turbulent pressure fluctuations. modifications. In the case Gaussian and Poissonian components can be considered statistically independent, Another functional model can be suggested for pressure the structure of characteristic functional Φ [υ (x)] is nearfield in the turbulent boundary layer. In contrast to obviously, within the given reasoning, a product of jet flows, near-wall pressure fluctuations in turbulent expressions (5,8): boundary layer is essentially determined by spontaneous splashes accompanied by surges of the liquid from the Φ [υ (x)] = exp{-½ ∫∫ υ (x1) υ (x2) RG (x1, x2) dx, dx2}* wall towards external area of the flow. Assuming statistical independence of the splashes and their uniform exp { P ∫ [χP ( ∫ gP(x-y) υ (x) dx) –1] }. (14) probability distribution over the surface, one can roughly describe the turbulent pressure fluctuations using the It has been show that the suggested functional approach Poissonian statistics : permits to reduce the task of experimental investigation of the characteristic functional to measuring a limited set of parameters typical of a given type of turbulent flows. Φ [υ (x)] = exp { ∫[χ(∫g(x-y)υ(x)dx)–1]}. (8) P They are correlation function of turbulent pressure fluctuations R (x ,x ) for Gaussian field and the Here is mean number of splashes for the unity of area, G 1 2 dependencies P, χ(µ); gP(ρ) for Poissonian component of χ(µ) is the characteristic function of the probability turbulent pressure field. distribution of pressure fluctuations P in the kernel of a splash, g (r) is impact function of the splash (g (0) = 1) defining spatial correlation links: REFERENCES

2 1.Haddle G.P. and Skudrzyk E.J.,J. Acoust.Soc.Amer., 46, 130-157 R (r) = <Ρ > ∫ g ( ρ) g (ρ + r) dρ . (9) (1969).

The signal characteristic function of homogeneous 2. Kudashev E.B. and Yablonik L.R., Phys. Acoustics, 45, 467-471 (1999). receiver of the area of S0 (in this case, at the aperture K(x) = K0 = const) in a Poissonian field is presented as

φsP() = exp { ∫ [χ (λ K0 ∫S0 g (x – y) dx) –1] dy}. (10)

For small S0, when receiver can be considered point- shaped, we get from the above expression

φsP1() = exp { ∫ [ χ (λ γ0 g ( ρ) ) –1] dρ }. (11)

If in homogeneous field, the area of the receiver surface S0 dramatically exceeds the area σ0 = ∫ g(ρ)dρ of the impact zone of the spontaneous splash, then, in line with (10),

φsP()J exp { S0 [ χ (λ γ0 σ0 / S0) –1] }. (12)

Since <Ρ>=0, it follows that for large area of the receiver surface S0/σ0 >>1 and the corresponding distribution approaches Gaussian distribution :

2 2 2 2 φsP2() = exp [-½ λ γ0

νσ 0 /S0 ] . (13)

CONCLUDING MODEL

One can assume that the variety of stochastic regimes of wall-turbulent pressure fields, including those in boundary layer with turbulence at its external limit taken into account, may be represented by a superposition of The Measurement of Fluctuations of the Signal Propagation Time over Long Acoustic Paths

A.Stromkova, I.Didenkulova, Ya.Karlikb, A.Kazarovaa, L.Lyubavina, E.Pelinovskya

aInstitute of Applied Physics, Nizhny Novgorod, Russia bMorphispribor, St. Petersburg, Russia

Acoustic waves can propagate in the ocean over basin scales and therefore can serve as a unique tool for diagnostics of ocean processes including internal waves, tsunami etc. The present paper describes results of the experiment on long- range sound propagation at the 4800 km acoustic path Hawaii-Kamchatka. Pseudo-random signals (M-sequence) at the carrier frequency of 75 Hz were radiated by a source deployed near Hawaii. The signal frequency band was 37.5 Hz, which allows one to measure travel time with accuracy of about 30 ms. In our measurement the phase-difference algorithm was used. The accuracy of travel time measurement with this method was significantly improved. It was observed that tides produce strong oscillations of travel time The results of experiments were found to be in good agree with theoretical calculations based on the ТХРО5 model for tides.

1. INTRODUCTION spectrum, and can be compressed in time by the use of correlation technique. The M-sequence signal allows Acoustic waves can propagate in the ocean over one to measure travel time variations for different basin scales and therefore can serve as a unique tool paths or path-groups separately. In the present paper for diagnostics of ocean processes: tides, eddies, we describe results of experimental study of sound currents, tsunami, internal waves, etc [1]. The main propagation over 4800 km. For detection of travel-time physical reason of the influence of ocean processes at variation the novel approach is used based of acoustic signal is water motion, which results in the difference phase method applied to the M-sequence change of the acoustic signal travel time. Therefore, signals. the measurement of acoustic travel time changes and their identification are the main problems. Acoustics is 2. METHOD OF ACOUSTIC TRAVEL widely used for ocean study. It is evident that for high TIME MEASUREMENT spatial-time and frequency resolution high frequency acoustic waves would be used. But sound attenuation Let a signal, which is the harmonic carrier modulated rapidly increases with frequency that makes in phase with the M-sequence is emitted by the sound impossible the use of high-frequency sound (above source. The receiving acoustic signal is the sum of several hundred Hz) for monitoring over distances of signals arrived by different paths. To analyse the about several tens km. Only very low frequency sound received signal one need to divide the signal by parts (below 100 Hz) can propagate over basin scales. of duration equalled to the M-sequence duration T. For Traditional way to measure the travel time variations is each of these parts defined as the k-th part of the based on the use of the phase method and harmonic received signal the cross-correlation function with the acoustic signals. The phase is related with the travel replica of the radiated signal is time through the sound velocity and the propagation path. However, sound propagates in the ocean by + many paths – rays, which produce many acoustic tTk ττ=− arrivals at the receiver. Such arrivals usually overlap bytMtdtkk()∫ () ( ) , (1) each other and therefore can not be separated. tk Consequently, the acoustic phase can give only information on ray-averaged fluctuations for travel where yk(t) is the k-th part of the received signal, M(t) time. Relatively recently more complex signal began is the replica of the emitted signal, and T is the M- to use in the ocean acoustics – so called M-sequence. sequence duration. The M-sequence signal being long in time has wide One can introduce the function ρk (m) as follows experimental data. The coefficient of correlation between them achieves 0.6-0.7. ρτττ==∗ (mbb )∫ ()+ () d kkkm -5 x 10 T , (2) 1 ωτ− τ Experiment = i 0 ()kkm+∗τττ ehhd∫ kkm()+ () 0.8 Calculation

T 0.6

0.4 where τk is the travel time, hk (τ) – the pulse response of the acoustic channel during k-sequence. The phase 0.2 0 of the function ρk (m) is ϕk(m)=ω(τ k-τk+m). For correct measurements of the travel time it is necessary that the -0.2 phase ϕk (m) for the time interval mT does not exceed -0.4

π time travel of Derivation (s s) per . -0.6 θ If is the travel time, an approximate expression for -0.8 the derivation of the travel time is -1 0 5 10 15 20 25 30 35 Experiment number dkθϕ() (,) nk ≈ . (3) FIGURE 1. The derivative of the travel time over π dt2 f0 nT acoustic path. Experiment and calculation based on the tide model. Thus, the phase difference method is based on measurements of the phase ϕk(m) that is the phase variations between acoustic transmissions. The travel time variations are directly related to the phase 4. CONCLUSION variations. In this paper the phase-difference algorithm based on 3. EXPERIMENT AND RESULTS the use of M-sequence signal for detection of small fluctuations of the acoustic travel time was described. The experiment on long-range sound propagation The accuracy of travel time measurement with this was conducted at the 4800 km Hawaii-Kamchatka method was significantly improved. Experiment on acoustic path. Pseudo-random signals (M-sequence) at long range sound propagation from Hawaii to the carrier frequency of 75 Hz were radiated by a Kamchatka revealed that tides produce travel time source deployed near Hawaii. The signal frequency oscillations having amplitude of up to 0.2 s. The main band was 37.5 Hz, which allows one to measure travel physical mechanism of influence of tides on travel time with accuracy of about 30 ms. The sound receiver time is tidal currents. The proposed method can be was installed near Kamchatka. Travel time fluctuations used for detection of water motion due to tsunami. were studied for the time period of about one year. It is worthy to note that the signal to noise ratio at the hydrophones in the experiment was below -10 dB measured in the signal frequency band. ACKNOWLEDGMENTS Measured fluctuations of the travel time were compared to calculated fluctuations. The calculation This work was supported by RFBR (01-05-64426, was made with the use of the global inverse tidal 01-05-64162, 00-15-96741). model TXPO5.1 [2]. The acoustic path was divided into 10 segment of about 500 km length. For each segment tidal current was supposed to be constant, which was calculated with the PXPO5.1 model for REFERENCES middle point of the segment. We neglected multipath acoustic propagation in calculations. The travel time 1. Munk W. and Wunsch C., Deep Sea Research, 26A, 123- derivative was estimated by calculation of the travel 161 (1979). time in 6 minute interval. The experimental and 2. Egbert G. and Erofeeva S., “Efficient inverse modelling of calculated data are shown in Figure 1. barotropic ocean tides. Global Inverse Solution TPXO.5.1.” As it is seen from Figure 1, even such a simple in http://www.oce.orst.edu/po/research/tide model for acoustic travel time fluctuations due to tidal currents demonstrates good correlation with the Granular-Hydrodynamic Model for the Convection in a Vibrating Granular System Guoqing Miao and Rongjue Wei

State Key Laboratory of Modern Acoustics and Institute of Acoustics, Nanjing University, Nanjing 210093, P. R. China

we present a granular-hydrodynamic model that capture the essence of convection in a vibrating bed. A set of hydrodynamic equations including the “heat” transfer are used to describe the motion of the system. The boundary condition at the bottom of the container is taken as that the power flow equals the power dissipated by granular system during the collision complete inelastically with the container. The solution of steady state with no flow in gravitational field is obtained. With this steady- solution (or a static distribution of “temperature”), a numerical simulation of a complete set of free convection equations was performed, the result is in agreement with experiments.

The vertically vibrated granular materials exhibit fluidlike behavior and show a variety of phenomena, ∂ m 2 m 2  m 2  including convection, heap formation, size segregation ( v ) + u ⋅ ∇( v ) = −∇ ⋅ χ∇( v ) − I (2) ∂t 2 2  2  and surface wave [1]. Among these phenomena, convection has attracted particular interest, because not only can convection happen alone, but also convection where χ is the thermometric conductivity, and I happens simultaniously together with (or within) other the rate of energy dissipation due to the inelasticity of phenomena (e. g. heap or wave), and might even be the grain-grain collision. The coefficients ν , β , χ and source for the wave and heap formation. The I play a more important role here than they do in theoretical approaches for explaining the convection hydrodynamics, they depend upon v [4], then have mainly been the continuum hydrodynamic theory according to ref.[5], they depend upon driving acceleration of the plate. Then Eq.(1) and Eq.(2) are [2] and large scale molecular dynamic simulation [3]. 2 In this paper we model vibrated granular system as coupled not only through the term mv / 2 , but also ''thermal'' granular fluid. Each grain takes part in through four coefficients. We take the density is simultaneously macroscopic flow motion and approximately constant, then the continuity equation is microscopic random or ''thermal'' motion, and they have simultaneously the mean macroscopic flow ∇ ⋅ u = 0 (3) velocity and thermal velocity, which corresponds to certain ''temperature''. Because granular system is a It is well known that in a normal fluid in a strongly dissipative system, the motion of the system is gravitational field free convection could happen if the governed by momentum equation, heat transfer externally applied vertical temperature gradient is equation, and continuity equation similar to that in ref. directed downwards and its magnitude exceeds a [4]. certain value. In a granular system we consider that We denote macroscopic flow velocity by u , and this temperature gradient is applied by the vibrated mean thermal velocity by v . The momentum equation plate. So the problem is under what temperature is analogous to the Navier Stokes equation, it reads gradient the convection will happen. Then we will solve first the problem of steady state with no macroscopic flow (or convection) in gravitational field, ∂u p η m 2 + (u ⋅ ∇)u = −∇ + ∆u − βg( v ) (1) and try to find a critical temperature distribution under ∂t ρ ρ 2 which the convection will happen. For the steady state the energy equation (2) is reduced to [4] where p is pressure fluctuation, ρ the density of layer, and g the gravitational acceleration. η can be d 2 v 1 dv called dynamic viscosity coefficient, and β the 2 + − v = 0 (4) thermal-expansion coefficient, The term mv 2 / 2 dz z dz represents the temperature of the system. The heat transfer equation is where z is a dimensionless variable given by z = (h ' − x) / λ as in ref.[4]. The boundary condition at free surface ( z = h ) is taken as 1/ 2  dI 0  A =  p /(mKI 0 ) (9) d  dx  K v 2 = 0 (5) dx x=h Principle, we could use the general theory for free this means that the energy flux vanish at free surface. convection with the temperature distribution (8) to As to the boundary condition at bottom of the container obtain the condition under which the convection could (0x = ), we consider as follows. In ref.[5] we use a happen. But in fact it can hardly be done so. We can model of a single sphere colliding completely inelastic only use numerical method to calculate it. The results with a massive sinusoidally oscillating plate to describe are shown that nothing happens for Γ < 1 . For Γ > 1 the motion of a layer of vibrated granular material, and and reaches some critical value the convection does obtained the power input P by the plate for each grain happen. Figure 2 shows a convection pattern of two as a function of acceleration of the plate (shown dimensional system, which are in quality agreement graphically in figure 1). So here we use with experiment.

12 10 10 8 8

J/s) 6 -7

Y 6 4

P (10 4 2 2 0 2 4 6 8 0 Γ 0 5 10 15 20 X FIGURE.1: (a) The mean power input, P , for each grain, as a function of dimensionless acceleration of the plate. FIGURE.2: A convection pattern of two dimensional system. this P as power input for each grain from the boundary x = 0 , and obtain the boundary condition This work is supported by the Special Funds for Major State Basic Research Projects and National Natural Science Foundation of China through Grants d 1 2 K (mv ) = P (6) No. 10074032, 19834040 and 19874029. dx 2 x=0 REFERENCES The solution to (4) are the modified Bessel function of zero order, I 0 (z ) and K0 (z) . 1. H. M. Jaeger, S. R. Nagel and R. P. Behringer, Phys. Today, 49(4), 32-38 (1996). 2. Marc BourZutschky and Jonathan miller, Phys. Rev. Lett. v = AI 0 (z) + BK0 (z ) (7) 74, 2216-2219 (1995); Hisao Hayakawa, Su Yue, and Daniel C. Hong, ibid 75, 2328-2331 (1995). Here x → h corresponding to z → 0 . As the function 3. Rosa Ramirez, Dino Risso, and Patricio Cordero, Phys.

K0 (z) is singular here, v(z) must be given by I 0 (z ) Rev. Lett. 85, 1230-1233 (2000). alone, i. e. 4. P. K. Haff, J. Fluid Mech. 134, 401-430 (1983). 5. Guoqing Miao, Lei Sui, and Rongjue Wei, Phys. Rev. E 63, 031304 (2001). v = AI 0 (z ). (8)

Insert this into the equation (6), we have The Use of an Equivalent Medium for a Coupled Inversion in Underwater Acoustics J.C.Le Gaca, M.Aschb

a Centre Militaire d’Océanographie, EPSHOM, B.P.30316, 29603 Brest Cedex, France b Laboratoire ANAM/MNC, Institut des Sciences de l’Ingénieur de Toulon et du Var, B.P.56, 83162 La Valette du Var Cedex, France

Abstract : The paper describes a shallow water geoacoustic inversion scheme based on a model based matched impulse response. The concept is derived from a previous paper [2] which presents an equivalent medium approach. The technique exploits the most stable part of the impulse response of the acoustic channel, giving robust estimates of geometrical and geoacoustic parameters.

INTRODUCTION sound speed profile. The second part, quite stable, Bottom properties are essential in the frame of exhibited a multipath structure of bottom-surface shallow water acoustics, especially for very low reflected rays supposed to carry most of the frequencies. Several approaches have been developed information on the bottom properties. in the last few years leading to good estimates of geoacoustics properties. A new trend lies in the use of Inversion of Geometrical Parameters broadband signals received on sparse arrays, possibly The exact geometry of an experiment (distance reduced to a single hydrophone[1]. Bottom properties source/receivers [D], source [Zs] and receiver [Zr] have shown to be robustly and efficiently retrieved depths, water column depth [H]) is often unknown from measured impulse responses. precisely due to operating modes and the lack of direct This paper presents an approach following previous measurements of these parameters. For example, the work explained in [2]. The aim of this previous work localization of the source is quite difficult when it is was to determine an equivalent medium and it was towed by a boat. Geometrical parameters must be well applied to the INTIMATE96 sea cruise data set [3]. known in order to perform a reliable geoacoustic The idea of “equivalent medium” means that the inversion. Thus, we applied a generalization of a bottom thus determined behaves as the true medium in linearized inversion scheme presented in [4] in order to the limit of an acoustical application (a kind of refine the a priori knowledge of the geometrical “through the sensor” approach). The equivalent parameters. This scheme, not explained in detail in our medium concept consists in assessing the main paper, exploits the travel times of 12 identified bottom- parameters when using a sonar system rather than surface reflected rays from the 115m depth finding relevant physical parameters. The frame of this hydrophone. It takes advantage of the fact that these geoacoustic inversion method, mainly based on an rays are slightly deviated by the sound speed profile. analytical development of the reflection coefficient, csurf=1520 m/s was limited to simple equivalent media (semi infinite zr1~35 m H~135 m fluid half spaces) and questionable in the case of high zs~93 m D~5.5 km zr2~105 m frequency dispersive true media. We propose in this cbot=1508 m/s zr3~115 m article a generalization of this previous approach. cp = 1650m/s ρ = 1.8 kg/dm3 αp = 0.36 dB/m/kHz silty sand h1 = 2m

cp = 1750m/s ρ = 2. kg/dm3 shell,gravel and sand GEOACOUSTIC INVERSION SCHEME αp = 0.28 dB/m/kHz h2 = 0.5m The INTIMATE96 data set consists of broadband 3 signals (300 to 800 Hz chirps, 2s duration, repeated cp = 3000m/s cs = 1600 m/s ρ = 2.4 kg/dm limestone αp = 0.07 αs = 0.25 dB/m/kHz every 8s) received on a 4-hydrophone vertical array. We exploit a range-independent phase shown on Figure 1. Matched filtering (cross correlation of the received FIGURE 1. Experimental set up and environmental parameters for the range independent leg and the emitted signals) was applied. The envelopes of the estimated impulse response thus obtained were then Results of the inversion from 20 starting models are extracted (Figure2). The received sequences were summarized in Table 1. Travel times obtained with divided into two parts. The first spikes are direct paths mean parameters are plotted on Figure 2 (black bars). refracted in the thermocline with a small number of They match the measured travel times very well. bottom reflections. They were highly sensitive to the the true medium. Furthermore, the inversion was quite Table 1. Inversion of geometrical parameters stable in comparison to the one performed in [2] (for Parameter Mean (m) Standard Deviation (m) α). Except for very few localized frequencies, the D 5629.5 0.4 comparison of simulated coherent losses at 5.63 km H 134.82 0.13 with the true medium and the equivalent one proved to Zs 90.95 0.14 be in very good accordance between 200 and 800 Hz Zr 115.91 0.20 (not shown in this paper). Between 50 and 200 Hz, the same comparison showed greater differences due to the Geoacoustic Inversion interaction of the basement (Figure 4). The accordance for the phase was not as good which is not surprising: Once geometrical parameters have been precisely the half space fluid medium is unable to render the determined, the geoacoustic inversion can be same complexity of phase shift as the true medium at performed. The geoacoustic model we looked for was bottom reflections. a simple fluid half-space in order to compare it to the 1850 0.9

0.8 results given in [2]. 1800

0.7 1750 0.7

0.6

1700

0.6 Cp (m/s) 0.5 Alpha (dB/m/kHz)

1650 0.4 0.5

1600 0.3 0.4

1550 0.2 0 20 40 60 80 100 0 20 40 60 80 100 Number of function evaluation Number of function evaluation 0.3 Impulse response envelope FIGURE 3. Geoacoustic inversion from 20 starting models 0.2

0.1 Equivalent medium True medium 50 50 −55 0 3.75 3.8 3.85 3.9 3.95 4 4.05 4.1 4.15 Time (s)

FIGURE 2. Typical INTIMATE96 envelope of the impulse 100 100 response on the 115 m depth hydrophone −60

150 150 The algorithm is based on the comparison of the peak

levels of each bottom-surface reflected path between Losses (dB) Frequency (Hz) 200 200 the measured impulse response and a simulated −65 impulse response. The stable part of the impulse 250 250 response envelopes (after 3.75 s) is normalized to unit energy, giving the following objective function to be 300 300 −70 20 40 60 80 100 120 20 40 60 80 100 120 minimized : Depth (m) Depth (m) FIGURE 4. Coherent losses for true and equivalent media α ρ = m − s = L f (c p , , ) å pl (i) pl (i), i 1 n i m s where pl is the level of a measured peak, pl is the CONCLUSION level of the corresponding simulated peak, n is the A simple and robust approach for geoacoustic inversion number of paths taken into account (for t>3.75s). has been presented in this paper. It is shown to be more The simulated impulse responses were computed stable than previous work in [2]. It shows that the with the GAMARAY ray model [5] which provides equivalent medium is relevant in the case of good estimates of the impulse response in comparison INTIMATE96 context for coherent losses between 200 with normal mode models, even near the critical angle and 800 Hz. (which is essential for our inversion scheme), for this kind of environment. A parametric study showed that REFERENCES the objective function had only a single global 1. Hermand, J.P., IEEE J.Oceanic Eng. 24,41-66 (1999) minimum, which allowed us to use the Nelder and 2. Demoulin, X., et al., Estimating equivalent bottom Mead simplex optimization algorithm in order to find geoacoustical parameters from broadband inversion in Proc. ECUA2000, M.E.Zakharia et al. eds. , cp and α [6]. Lyon,France,2000,pp.191-196 The mean estimates of the most sensitive geoacoustic 3. Stephan, Y., et al., Acoustical effects of internal tides on shallow water propagation : an overview of the INTIMATE96 parameters were : 1678.8m/s for cp, 0.35 dB/m/kHz for α 3 ρ experiment, in Exp. Ac. Inv. Methods for Exploration of the , and 1.86 kg/dm for . The density was deduced Shallow Water Environment, Caiti et al. eds, Kluwer from cp. Twenty starting values were chosen Academics Publishers (2000), pp.19-38 stochastically within the following bounds 4. Dosso, S., et al, J.Acoust.Soc.Am. 104,846-859 (1998) {[1550 ;1850]m/s and [0.3 ;0.7]dB/m/kHz} (Figure 3). 5. Westwood, E.K., et al., J.Acoust.Soc.Am. 81,1752-1761 (1987) These mean estimates were in good accordance with a 6. Press,W.H., et al., Numerical Recipes in C, Cambridge University Press (1992) weighted sum of the superficial layers parameters of Proposal of FDTD-PE Method with Slow Varying Envelope Approximation for Underwater Acoustic Pulse Propagation

Tetsuo Anada, Takenobu Tsuchiya, Nobuyuki. Endoh (Kanagawa University) Toshio Tsuchiya and Toshiaki Nakamura (JAMSTEC) 3-27 Rokkakubashi Yokohama, 221-8686 JAPAN E-mail: [email protected] In this paper, a new finite-difference time-domain PE method based on the slow-varying envelope approximation is proposed for the analysis of forward wave propagation, diffraction and reflection and given for short pulse propaga- tion problems in underwater acoustics. The advantage of the FDTD-PEM proposed here is its simple numerical implementation and can be analyzed within a reasonable cpu-time and memory. In order to improve the accuracy and efficiency of the method, the computational spatial discretization of second-order differential is replaced by the Douglas operator scheme, which the truncation error of O(∆x)4 is ensured in the depth direction.

1. Introduction where t is time, n(r,t)=c0/c(r,t) is the refractive index, c0 Parabolic Equation Methods (called Beam Propagation and c is the reference sound speed, the sound speed in Method in branches of optics), in which an acoustic field underwater acoustics. In comparison with a carrier fre- solution can be determined by solving the one-way op- quency, the signal frequency will be regarded as a slow erator equation for the forward-propagating field, are pow- wave propagation. By applying SVEA to the time term erful design tools for underwater wave propagation prob- of the wave equation and substituting a solution of the −ω lems. A great number of PEM’s have been proposed by form ψϕ(,)rt=⋅ (,) rt e jt into Eq.(1), the following time- pioneers since its inception [1,4,5]. The advantage of the domain wave equation is obtained: FD-PEM is its simple numerical implementation and can n 2 ∂ϕ2 ω∂ϕn 2 ω22n be simulated within a reasonable cpu-time and memory. − −2j =−∇×∇×ϕ + ϕ (2) On the other hand, the method has several drawbacks be- ct2 ∂2 ct2 ∂ c2 cause it is approximation to the Helmholtz’s wave equa- where ω is the angular frequency (carrier frequency) . tion neglected backward-wave. In order to overcome The first term on the time is much smaller than the sec- above drawbacks, a new approach for developing the PE ond term. With above first-order approximation, the re- method in time-domain is to use the wave equation based duced wave equation may be written as: on slow-varying envelope approximation and is called 2 22 22 the TD-Beam Propagation Method in optics field [2,3]. ω∂ϕn ∂ϕ ∂ϕ ωn −=++2j ϕ (3) Based on this SVEA, an acoustic short pulse propaga- ctxz2∂∂ 2∂ 2c2 tion has been applied to the wave equation simultaneously While, for acoustic wave propagation confined in the leading to an algorithm suitable for studying forward wave x-z plane with z-propagating beam, the paraxial wave propagation, wide angle of diffraction and reflection. In equation is rewritten as follows: this paper, we propose a very efficient time-domain para- ∂ϕ ∂ϕ2 ∂ϕ2 bolic equation method (TD-PEM). The aim of this paper −=++−22() 2ϕ 2jk0 n bbknn0 (4) are: (1) to derive a new formalism for the pulse propaga- ∂zx∂ 2∂ y2 tion in time domain; and (2) to demonstrate its possibil- where nb is the reference index, k0 is the wave number. ity to treat underwater acoustic pulse propagations. Comparing with Eq.(3) and (4) , Eq.(3) and Eq.(4) have the same form. It is quite obvious that PE codes 2. Basic formalism can be extended to solve the time-domain PE equation For the sake of simplicity of formulation of TD-PEM in the slow-wave approximation. Therefore, we can be based on SVEA , we restrict ourselves to the two dimen- solved by using Alternating Directing Implicit Method sional problem and the wave equation in Cartesian coor- (so called operator splitting method) for obtaining the dinates. For underwater acoustic wave propagation prob- solution of Eq.(3). lems, the wave equation is given by The ADI method can be applied to lead to a program suitable for Eq.(3). The principle is to use two different n2 ∂ψ2 ∇×∇×ψ + =0 (1) equations that are used in turn over successive time-steps ct2 ∂2 each of duration ∆t/2. That is, the first equation is solved implicitly only in the x-direction and the second only in second order derivative in depth direction. In addition, the z-direction. In addition, to improve the accuracy of by combining the higher-order Pade series expansion the simulation, the Douglas operator scheme firstly used with the Douglas operator scheme, it is possible that the by Lee, et al is applied. As a result, we can easily obtain present method satisfies the accuracy required. In the the high accuracy six-point scheme (then, the truncation near future, the algorithms proposed here will be applied error is the fourth-order) and lead to a tridiagonal system in practical range dependent 3D problems. of complex linear equations[6]. Reference ∂ 11∂δ2 ρ()x =x (1)M.A Leontovich and V.A. Fock, Zh. Eksp. Teor. Fiz. 16, pp.557- 2 2 ∂ρxxx()∂∆ x1+αδ 573, 1946. x (2) P.L. Liu, Q.Zhao, and F.S. Choa,”Slow-wave finite-difference beam 1 propagation method”, IEEE Photon., Technol. Lett. vol.7, pp.890-892, α = (Douglas case) , 0 (Crank - Nicolson) 1995 12 (3) G.H. Jin, et al, “An improved time-domain beam propagation 2 method for integrated optics components,” IEEE Photon. Technol. Lett. δρφρφρφ=−+−− ++ , xi10 ii 1 vol.9, pp.348-350,1997. 2(,)ρ xz (4) M.D.Collins, “A higher-order parabolic equation for wave propa- ρ =, ρρρ=+ gation in an ocean overing an elastic bottom”, J.Acoust.Soc.Am. ±+−0 ρρ(,)xz+± ( x∆ xz ,) 86,pp.1459-1464, Oct.1989. (5) D. Lee, A. D. Pierce, “Parabolic Equation Development in Recent Decade”, J. of Computational Acoustics, Vol.3, No.2, pp.95-173, 1995. 3. Results of simulations (6)J. Yamauchi, et al, “Improved finite-difference beam propagation In order to demonstrate the validity and usefulness of method basd on the generalized Douglas scheme and its application to SVEA based TD-PEM, we studied the underwater acous- semivectoral analysis,”, J. Lightwave Tecnol., no.10, pp.2401-2406, tic pulse propagation in the upsloping seafloor model. Oct. 1996. The depth of the channel is 100 m and the sound speed 0 and density in the underwater are c=1500 m/s, ρ=1.0 g/ 3 ρ 3 cm , those of sediment layer are 1700 m/s, =1.2 g/cm , 100 the frequency is 500Hz. An acoustic wave packet with a Release boundary condition

Depth[m] at top and bottom. depth profile corresponding to the gaussian beam is 200 launched at t=0 and z= 50 m. The longitudinal profile of 0 1000 the wave pocket along +z propagating direction is also 0 Gaussian beam with full-width of 10 m. The computa- tional window is 200 m by 1000 m which is discretized in a 200 by 2000 grid. The time step used is 0.1E-3 [sec]. 100

The total duration simulated is 1 [s]. The multimode chan- Depth[m] 200 nel waveguide is supported some guided modes, and is 0 1000 reflected in the boundary of top and upsloping-bottom, and the interference will be occurred. Figure 1 shows the 0 pulse propagation characteristics of the incident-pulse with Gaussian profile. It is observed that the pulse is con- 100 fined in the underwater column, while the part of the pulse Depth[m] wave is radiated into the upsloping sediment layer. The 200 validity of the present method is confirmed by the coin- 0 1000 cidence with our physical image. 0

4. Conclusion 100 In this paper a recently developed FDTD-PE method based on the SVEA has been given for predicting the pulse Depth[m] 200 propagations in underewater acoustics. In stead of Crank- 0 1000 Nicolson scheme, the Douglas operator scheme has been Range[m] implemented for the program in order to reduce a trunca- Fig.1 Pulse propagation in shallow water tion error of the finite-difference approximation of the with upsloping bottom. An Evaluation of Information Transmission Using Underwater Auditory Sensitivity in Actual Sea Area

S. Kuwaharaa, K. Oimatsua, K. Kuramotoa, S. Yamaguchib and H. Matsuia

a Japan Coast Guard Academy, Kure-city, 737-8512 Japan b Faculty of Engineering, Yamaguchi University, Ube-city, 755-8611 Japan

Usual divers searching and rescuing in a sunken ship have no communication apparatus because of complexity of the hull construction. In such cases, the most simple and effective way for transmitting information is to emit acoustic signals directly by using an underwater loudspeaker. At this time, it is fundamentally important to research in advance the following measurements: masking of objective signals by ambient noise in the actual sea, evaluating of clearness of voice signal and tone signal coded by Morse sign. In our research, the above measurements are examined in the actual sea area, using scuba divers with normal hearing. Measurements of masking effects of auditory ambient noise provide data from which the excitation pattern of the masking stimulus is derived. Evaluations of clearness of voice signal and coded-tone sign are measured by changing output power levels of the underwater loudspeaker and distances between the loudspeaker and divers.

INTRODUCTION units are depicted in Figure 1 consisted on equipment common to audibility threshold experiment. The research was carried out on two subjects who were Many people now enjoy marine sports such as skin experienced in taking hearing test in air and in water. diving and scuba diving in sallow water area. Acoustic Each diver was lowered to an ear depth of 2 meters. signals through an underwater loudspeaker can be used The divers, wearing open circuit scuba equipment and as a simple and effective way of preventing diving a wet suit, descended and fixed himself from the accident. At this time, it is fundamentally important transducer by means of a chest bar and a lead-weighted to research in advance the following measurements: belt. Twenty runs were made on each of the two masking of objective signals by ambient noise in the subjects, and an average of the ascending and actual sea, evaluating of clearness of voice signal and descending runs was made for subjects. tone signal coded by Morse sign. In our research, the above measurements are Evaluation of Acoustical Signal examined in the actual sea area, using scuba divers with normal hearing. Measurements of masking effects As using voice signals is effective for transmitting of auditory ambient noise provide data from which the information to divers, acoustic signals of male and excitation pattern of the masking stimulus is derived. female voice sound are emitted directly by using an Evaluations of clearness of voice signal and coded- underwater loudspeaker. Divers were lowered to an ear tone sign are measured by five phases of intelligibility depth of 2 meters, and distances between diver’s head evaluation referring the Radio Regulation (RR) of the and the projector separated were of 20, 50, 100, 160 International Telecommunication Union (ITU). and 200 m. Output levels of the projector were adjusted adequately by using attenuators EXPERIMENT The clearness of voices at each distance were evaluated by six phases which are referred to the The research was carried out on actual sea area nearby following intelligibility based on RR of ITU: 5 Japan Coast Guard Academy, which is usual recreation excellent, 4 good, 3 fair, 2 poor, 1 bad and 0 no signal. area of sea. The subjects consisted of two divers who had Measurement of Minimum Audible Field Underwater experienced as a radio operator on Japan Coast Guard vessel. The Minimum Audible Field (MAF) underwater was It had been predicted that voice signals were masked determined by means of the method of limits. easily by ambient noise of experimental sea area. Sinusoidal stimuli at 500, 1000, 2000 and 4000 Hz Therefore, tone signals coded as ‘QF’ of the Morse were gated ON and OFF with a period of 500 msec. sign were adopted because coded tones were not The stimulus-generating equipments and response comparatively masked by noises. Signal Analog DAT 5 l=20m Generator Gate l=50m 4 Sound l=100m e Power Level fitting Attenuator 3 Amp Meter

2 Evaluation of Cl

Underwater 2m 1 Loudspeaker Mic 0 80 100 120dB 140 160 (a) Female Voice Signal. 5

l=20m FIGURE 1. Setup for underwater hearing-acuity measurement. l=50m 4 l=100m e fitting

RESULTS 3

Minimum Audible Field Underwater 2

In Table 1, some experimental results estimated for Evaluation of Cl minimum audible field (MAF) underwater are reported 1 (dB re 1 Pa). The MAF levels in quietness measured 0 by the previous research [1] are expressed in this table. 80 100 120dB 140 160 (b) Tone Signal Table 1. Levels of minimum audible field (dB re 1 Pa). FIGURE 2. Evaluation of Clearness (Intelligibility) of Frequency No Hood With Hood In Quietness (a) Female Voice Signal, and (b) Tone Signal. 500 Hz 99 dB 100 dB 78 dB 5

1 kHz 100 dB 99 dB 86 dB freq (source pow er) 2 kHz 88 dB 100 dB 89 dB 4 500Hz (145dB ) e 4 kHz 94 dB 119 dB 91 dB 1 kH z (150dB) 2 kH z (150dB) 3 4 kH z (150dB)
As low frequency ambient noises are large (about 95 dB/Hz) in the experimental sea area, measured MAF 2 levels rise compared with quietness levels [2,3]. On the other hand, in high frequency region, MAF levels do not Evaluation of Cl 1 rise very much. Furthermore, it is predicted that MAF levels increase clearly in high frequency region when a 0 diver was wearing a diving hood [4]. 10 100Distance (m) 1000 FIGURE 3. Clearness of Tone Signal with Fixed Source Evaluation of Clearness of Acoustic Signals Power Levels. In Figure 2 is shown the evaluation of clearness REFERENCES (intelligibility) of the female voice signal and tone 1. K. Oimatsu, K. Kuramoto, S. Kuwahara and signal varying signal levels at diver’s head points S. Yamaguchi, J. Marine Acoust. Soc. Jpn, 21, 103-109 (1994) received. The clearness of voice signal is rapidly (in Japanese). dropped with the signal level (slope of dashed line is 2. J. E. Hawkins and S. S. Stevens, J. Acoust. Soc. Am., 22, 6-13 steep). On the other hand, the clearness of tone signal (1950). 3. S. S. Stevens and H. Davis, “Auditory Masking, Fatigue, and is slowly dropped (slope is gentle). Persistence,” in Hearing, New York, Acoust. Soc. Am., 1938, In Figure 3, the evaluation of clearness of pp.208-224. tone signal varying distances is presented. Data from 4. S. Kuwahara, K. Oimatsu, K. Kuramoto and four frequency sequences of increasing distance with S. Yamaguchi, J. Marine Acoust. Soc. Jpn, 25, 86-91 (1998) (in fixed source power are shown. Japanese). Estimated Model of the Dynamic behaviour of a Cavitating Valve

V. Villouvier EDF/DRD/AMV 1 Avenue du Général de Gaulle 92141 Clamart France

The Residual Heat Removal circuit for the French PWR sections comprises two valves which regulate the hot and cold flow rates. These valves operate according to a cavitation system when the circuit is functioning under certain conditions, and the considerable excitations which are then created may lead to the cracking of certain small lines located nearby. In order to limit the circuit's vibrations, optimal operating conditions are being sought which would make it possible to reduce the pressure fluctuations at source-level. The valve vibration model which has been developed links the internal acoustic energy to the vibrations measured over the valve body using local hydraulic variables. Measurements taken on-site in respect of a great many control configurations have made it possible to readjust the different modelling parameters. Within the context of the RRA circuit, the local valve vibrations are estimated according to the variables governing the global operation of the circuit, by using the hydraulics calculation and taking into account the different hydro-acoustic sources simultaneously present.

CONTEXT The hydraulic balance in that case involves the Control description and parameters aperture angle of 13VP (see Figure 1).

On-site tests The vibratory measurements were carried out with the aim of analysing the behaviour of the circuit and the Z control components in regard to a large number of operating conditions and, in particular, the sensitive

Y X cases of “vacuum” conditions (low upstream pressures).

12VP THE VIBRATION MODEL The sources of excitation 13VP 01DI Hydro-acoustic identification tests carried out in the 26VP laboratory made it possible to study the equivalent internal sources responsible for the vibrations observed on the valve casings. FIGURE 1. Diagram of part of the RRA circuit

The RRA circuit of the French PWR units includes two hot and cold flow rate control valves. Under certain operating conditions, these valves, together with the bypass diaphragm, are in cavitation operating mode and the considerable excitations which are then generated may lead to the cracking of certain small lines situated nearby. We are seeking optimal operating conditions enabling the pressure fluctuations at the level of the sources of excitation to be reduced in order to limit the circuit vibrations. The circuit's main operating parameters are: the total FIGURE 2. Example of acoustic sources for a cavitating flow rate, the upstream pressure, opening of the 12VP valve valve and the open or closed state of the 26VP valve. The Thoma number enables the degree of cavitation The vibrations of the 13VP valve result from Pav Psat superimposing the different excitations present: to be estimated:   3 2 Pam Pav dA
  ' Ph i
1 i i Description of the model On the whole, it suffices to readjust five coefficients Two physical phenomena were highlighted which are the constants for the vibration model S The turbulence noise, especially low frequency (Figure 3). energetic, is represented by a spectrum in the form

of: Vacuum operation
qturb= A St 13 VP displacements 2000 with A: proportionality coefficient 1500 Model 3 ts  n 13VP measurement fD x()1 x e St: Strouhal number: St
m

ace 1000 l

4Q sp Di : constant x= h/D 500 S The cavitation noise is shown by the appearance of a plateau beyond an fc frequency which varies 0 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13 - 14 - 15 - bf10910 bo0910 vo01b vo008b vo006b vo004b vo002b vo10700 vo10800 vo1082 vf10700 bar1 b31210 b3120 b25 with the intensity of the phenomenon. N° essai  The weaker , the weaker fc and the greater the FIGURE 3. Example of calculated, measured vibrations amplitude of the plateau. Estimated calculations Adimensionalisation Estimated calculations made it possible to highlight Excitation is adimensionalised by the head loss, the the effect of the circuit's operating parameters on the cross-section of flow of the fluid in the valve, the vibratory levels of the valves and helped in the search flow rate in the valve and the ratio of upstream and for new control rules that would stress the circuit as downstream pressures. little as possible (figure 4). These rules are at present We finally obtain the expression:
being successfully applied by the operators. If > lim f>1 Hz q= A’ Qadim St   
If < lim If 1fc q= A’ Qadim St (fc) 800

With Qadim= F(P, x D/D0, Q, Pam/Pav) 26VP closed 700 26VP open Displ measured O 600 Integration 500 The hydro-acoustic power Ph emitted is obtained by frequency integration of the q curves, i.e.: 400 300 f 2 Displacements 2 *
qdf 200 f1 The vibration model is based on the hypothesis that 100 0 the internal acoustic power is proportional to the root 0 5 10 15 20 25 30 mean square value of the displacements measured Angle 12VP directly at right angles with the excited valve. FIGURE 4. Vibrations calculated on 13VP, according to the opening of 12VP, in regard to two circuit configurations APPLICATIONS Hydraulic and vibratory comparisons REFERENCES Initially, the hydraulic conditions corresponding to 1. A. BOYER - J.F. LAURO Etude hydroacoustique d’une vanne the test configurations were calculated and compared papillon en régime cavitant - Colloque d’hydrotechnique with the measurements taken at different points of the 156ème Session du comité Scientifique et Technique de la SHF circuit. In fact, the sources of excitation depend on - Chatou, 19 et 20 /11/97 2. J.P. TULLIS Hydraulics of pipelines Pumps, valves, cavitation, the local hydraulic conditions which must be transients – Wiley Interscience accurately estimated. Underwater Acoustic System with a Small Antenna for the Detection and Bearing of Sound Sources

R. Salamon, J. Marszal, M. Rudnicki

Technical University of Gdansk, Department of Acoustics, Narutowicza 11/12, 80-952 Gdansk, Poland

Today's passive sonars usually detect and determine the direction of sound sources using linearly placed hydrophones. In practice, however, there is a need for passive systems whose antenna is as small as possible. To meet these requirements the array will usually consist of four ultrasonic transducers located in the corners of a square and the additional fifth one placed in the centre of the square. The subtractions of the signals from the opposite transducers are proportional to the sine and cosine of the angle of the arrival wave. The accuracy with which the angle of the coming wave is determined is low because taking away the signals reduces the useful signal more than does noise. The output signal to noise ratio is worse than the input one, which has a negative impact on the accuracy of the bearing. Because the signal to noise ratio gets worse as the system's operating frequency band gets wider, the narrow band filtration as a way of improving the accuracy of the bearing is used.

SYSTEM OPERATING PRINCIPLE For long M series and the above conditions, the spectra determined have the following form: The system's antenna is built of four hydrophones Sc( k ) @ 4 jp( d / lk )U( k )cosq + Nc( k ) located in the vertexes of a square with a 2d diagonal S ( k ) @ 4 jp( d / l )U( k ) sinq + N ( k ) (3) [1]. The signals received by the hydrophones can be s k s written as: S( k ) @ 4U( k )+ N( k ) where k=0,1,2,...,M-1 is the number of the spectral s1 ( t ) = u( t +t c )+ n1 ( t ) line, l =cM/kf . s ( t ) = u( t +t ) + n ( t ) (1) k s 2 s 2 The last steps of signal processing consist of the s3 ( t ) = u( t -t c )+ n3 ( t ) following operations:

s4 ( t ) = u( t -t s )+ n4 ( t ) * Y( k ) = Im[ S c ( k )× S ( k )] where t c = ( d / c )cosq , t s = ( d / c ) sinq * X( k ) = Im[ S s ( k )× S ( k )] (4) and n1(t),...,n4(t) are the hydrophone output noises and c is the acoustic wave velocity. The points that determine the direction of wave Following subtraction in pairs and summation, arrival concentrate around the point determined with three signals are obtained: no noise present. The scattering of the points and consequently the measuring error are more or less sc ( t ) = s1 ( t )- s3 ( t ) attributed to the noise in the system. The particular

s s ( t ) = s 2 ( t ) - s 4 ( t ) (2) noise spectrum lines are represented by points located s( t ) = s ( t )+ s ( t )+ s ( t )+ s ( t ) around the centre of the co-ordinate system. (Fig. 1). 1 2 3 4 For constant amplitude of the sinusoidal signal, the The signals are filtered in low-pass filters which area denoted by these points gets smaller as the signal limit their spectrum to the fM, frequency. Next, they are to noise ratio grows. Also, as the signal to noise ratio sampled at frequency fs to be processed in an ADC goes up the spread of the points determining the converter into numerical series sc(m), ss(m) and s(m) at direction of the arrival wave gets smaller. When the M length. These series are used to determine discrete S/N ratio is greater than 10 dB the area of the noises is Fourier transforms in the computer, further denoted as practically the same as that of the co-ordinate system Á{×} . The system assumes the following inequalities: centre. Through the system it is possible to determine the direction of sinusoidal arrival waves of various fs tc<<1 and fs ts<<1. The inequalities mean that distance 2d between the hydrophones is much smaller frequencies. For the same signal amplitudes, the points that correspond to lower frequencies are located nearer than the smallest wavelength l =c/f in the spectrum M M the co-ordinate system centre. The system also works of the signal being received. correctly for periodical signals of varying periods. 2 ERRORS IN MEASURING THE WAVE value and variance sDq (ki), we can assume that the INCIDENCE ANGLE variance of the error's mean value computed from formula (9) is approximately equal to: To estimate the measuring error of the arrival wave s 2 ( k ) s 2 ( k ) @ Dq i (10) angle let us substitute relations (3) to formulas (4). L i L Following transformations, we obtain: 2 Variance sDq (ki) can be determined using formula 2 * (8). If we assume that the random variables in this Y( k ) = 4 p k |U( k )| cosq + 4 Im[ N c ( k )U ( k )] + formula are not correlated, we get: + p Re[U( k )N * ( k )] cosq + Im[ N * ( k )N ( k )] k c s 2 ( k ) N ' 2( k ) N ' 2( k ) 2N ' 2( k ) 2s 2 (11) Dq i @ y i + x i = x i = ki 2 * X( k ) = 4 p k |U( k )| sinq + 4 Im[ N s ( k )U ( k )] + (5) The standard deviation of the bearing error can be * * + p k Re[U( k )N ( k )] sinq + Im[ N ( k )N s ( k )] finally given in the following form: where p =4pd/lk. 2 2s k s ( k ) @ (12) L i p U LM The first components of the sum in both equations ki i describe the components of the useful signal, while the where s - standard deviation of noise. others describe the interference components responsi- ble for the wrong measurement of the angle q. With no The standard deviation of the bearing error diminishes as the S/N ratio, the number of spectral noise present (Nc=Ns=N=0) for any signal the distance between the points is Z (k)=4p |U(k)|2, and the angle lines and averaging increase. The formula derived s k above was proved both in detailed studies and q ( k)= q. By denoting the useful components as U (k) y measurements taken in the field. and Ux(k), and noise components totals as Ny(k) and Nx(k) formulas (5) can be reduced to:

Y( k ) = U y ( k )+ N y ( k ) , X( k ) = U x ( k )+ N x ( k ) (6)

The bearing error Dq [rad] can be written as:

' ' N y ( k ) sinq - N x ( k )cosq tg[ Dq( k )] = ' ' (7) 1+ N x ( k ) sinq + N y ( k )cosq where ' ' N x( k ) = N x( k ) / Z s ( k ) , N y ( k ) = N y ( k ) / Z s ( k )

' 2 ' 2 Values 1 / N x ( k ) and 1 / N y ( k ) describe the signal to noise ratio for the particular spectral lines. The system in question works correctly only for a relatively high S/N ratio, i.e. for ki spectrum lines those correspond to the basic frequencies of the useful ' 2 ' 2 signal spectrum: N x ( k i ) << 1 and N y ( k i ) << 1 . For those spectrum components formula (7) is reduced to this form: FIGURE 1. Example of a bearing signals representation.

' ' Dq( ki ) @ N y( ki ) sinq - N x( ki )cosq [rad] (8) The mean bearing angle error is: 1 L (9) REFERENCES Dq( ki ) = å Dq( ki ,l ) L l =1 Because of insufficient data, the error denoted with 1. R. Salamon, J. Marszal, A. Raganowicz, M. Rudnicki., Application of Fourier Transformation in a Passive formula (9) is merely a weak estimate of the mean th value in a statistical sense. Sonar with Gradient Hydrophones., Proc. of 5 ECUA, Lyon 2000, Vol. 2, pp. 1115-1120. The bearing error Dq( ki) can be estimated using formula (8) and by including the averaging operation 2. T. K. Moon, W. C. Stirling, Mathematical Methods and given in formula (9). If we assume that the bearing Algorithms for Signal Processing, Prentice-Hall, Inc., error is a Gaussian random variable with a zero mean New Jersey 2000. Krill vertical migrations in the Ross Sea Kalinowski J., Azzali M., Godlewska* M., Lanciani G.,

Sea Fisheries Institute, Ancona, Italy * International Centre for Ecology PAS, Dziekanów Leœny, Poland

Krill vertical migrations in the Ross Sea were studied on the basis of three expeditions during the Antarctic summers of 1989/90, 1994/95 and 1997. Although there was no light change in the area during this time of a year krill did change its depth within a 24 hour cycle. The depth of krill swarms was averaged over two hour time intervals and approximated by the function H(t)=Ho + A1 cos (2πt/T1+Φ1) +A2 cos (2πt/T2+Φ2), where Ho is the mean depth of krill, A1 and A2 are the amplitudes of migrations with periods of T1=24 hour and T2=12 hour accordingly, Φ1 and Φ2 are the phases of the migration, determining the time when migrations start. The migration pattern differed between years, with no migration in 1989/90, inverse migration in 1994 and classical migration in 1997. The amplitude of migrations was increasing with the length of individuals and with the latitude. The 24 hours periodicity was best pronounced for large adult krill, while for juvenes 12 hours cycle dominated.

INTRODUCTION Depths of krill swarms were determined from the The phenomenon of zooplankton diel vertical echo charts as the lower plus the upper limit of a swarm migrations is one of the most common and best divided by two. These values were averaged over two documented types of animal behaviour. The hypothesis hour time intervals and approximated by the function: that changes in animal’s vertical distribution are caused H(t)=Ho+A1cos(2πt/T1+Φ1)+A2cos(2πt/T2+Φ2) by the rhythmical changes in incident solar radiation had been put forward already in the last century [1] and where Ho is the mean depth of krill occurrence, A1 and documented many times since than. A2 are the amplitudes of migrations with periods of Data on migrations of Antarctic krill are T1=24 hour and T2=12 hour,Φ1 and Φ2 are the phases contradictive. Some authors have observed migrations of the migration, determining the time when migrations [2, 3, 4, 5], others did not [6, 7]. start. In the present work krill vertical distributions were The 24 and 12 hours periodicities were assumed as studied in situation when there is no change of light typical for krill migrations [5, 8]. Parameters of this during the 24 hour period, as observed in the Ross Sea model Ho, A1, A2, Φ1, and Φ2 were estimated with during the Antarctic summer. The aim was to check if nonlinear regression using Marquard’s [9] iterative krill are migrating in such conditions, and if they do, algorithm to minimize the residual sum of squares. what are the factors influencing the migration pattern. RESULTS AND DISCUSSION MATERIALS AND METHODS During all three expeditions to the Ross Sea krill Data on krill swarms vertical distribution were were distributed mainly in the upper 70 m, with very collected during three Italian Expeditions to the Ross few swarms below 100 m (Fig.1). Sea, in 1989/90, 1994/95 and 1997. Animals were performing diel vertical

1990 1994 1997 num ber of sw arm s num ber of sw arm s number of sw arms 0 100 200 0 100 200 0100200 10 10 10 30 30 30 50 50 50 70 70 70 90 90 90 depth interval depth interval depth depth interval depth 110 110 110 130 130 130

Figure 1. Krill vertical distributions during three Italian expeditions to the Ross Sea migrations with different pattern each year. In 1997 it Small krill was so called „normal” migration, i.e. krill were time (hour) closer to the sea surface during the night and deeper in 04812162024 the water column during the day, in 1994 it was 0 „reversed”, krill were closer to the surface during the day and deeper at night, while in 1989/90 season no 20 clear difference in krill distribution between day and 40 night was detected (Fig. 2). depth (m)

December 1997 60 time (hours) 0 4 8 12 16 20 24 Large krill 20 time (hour) model depth 30 mean depth 0 4 8 12162024 experiment 0 40

depth (m) 50 20

60 40 depth (m) depth December 1994 time (hour) 60 0 4 8 12 16 20 24 30 Fig. 3. Migration pattern for small and large krill.

40 The different cycle of migration for large and small

50 krill is in agreement with data received by Godlewska model depth [5] for the Western Antarctic. She also found that the depth (m) 60 experiment mean depth mean depth of krill occurrence and the amplitude of 70 migrations depend on food availibility. Unfortunately only in 1994 data on phytoplankton were collected January 1990 [11]. The lowest concentrations of chlorophyll were time (hour) observed at most northern stations, and they increased 0 4 8 12 16 20 24 20 southernly. This increase of food availability with the latitude was accompanied by increase in the intensity 30 of krill migrations. 40 model depth REFERENCES depth (m) 50 mean depth experiment 60 [1] Cuvier G. La regne animale 2 (Poissons), Paris, 532 pp. (1817) Figure 2. Krill migration pattern in different years [2] Kalinowski J. Pol. Arch. Hydrobiol., 25: 573-583 (1978) Only during the 1994 and 1997 expeditions [3] Tomo P. Ber. Polarforsch. 4: 191-195 (1983) representative net samplings were received for the area [4] Godlewska M, Klusek Z. Polar Biol. 8: 17-22 of acoustic survey. In 1994 the sizes of krill were (1987) similar in the whole area [10] while in 1997 a distinct [5] Godlewska M. Pol. Arch. Hydrobiol. 43: 9-63 (1996) separation of adult and young animals in different areas [6] Daly K.L., Macaulay M.C. Mar. Ecol. Prog. Ser. 79: was observed, thus enabling comparison of migration 37-66 (1991) patterns for strongly different size of animals. There [7] Loeb V.J.,Shulenberger E. Polar Biol. 7: 363-373 (1987) was a cluster of juveniles with the lengths of 20-29 [8] Pavlov V.Ya. Tr VNIRO 66:104-116 (1974) mm, and a cluster of adults, where the length class of [9] Marquardt D. J. Soc. Ind. Appl. Math. 11: 131-136 40-49 mm predominated. Comparison between (1963) migration patterns for these two areas (Fig. 3) shows [10] Azzali M., Kalinowski J., In: Ross Sea Ecology that small juveniles migrated with 12 hour periodicity, Faranda F., Guglielmo L., Ianora A. (eds). while large adults within a 24 hour cycle. The mean Springer-Verlag, Berlin Heidelberg 433-455 (2000) depth of krill occurrence and the amplitude of [11] Saggiomo V., Carrada G.C., Mangoni O., Ribera migration did not differ significantly. d’Alcala M., Russo A., J. Mar. Sys. 17:115-127 (1998) Comparison of Decision Tree and Multistage Neuro-Fuzzy Classifiers of Seafloor using Wavelet Coefficients of Acoustic Echoes T. V. Dung, M. Moszynski and A. Stepnowski

Department of Remote Monitoring Systems, Technical University of Gdansk ul. Narutowicza 11/12, 80-952 Gdansk, Poland

In recent years the neuro-fuzzy expert systems have been successfully applied to seabed classification from acoustic echoes [1]. In particular, implementation of multistage Incremental Fuzzy Neural Network architectures (IFNN) demonstrate good performance and high classification rate, especially when the number of input parameters was reduced by Principal Component Analysis (i.e. to the wavelet coefficients extracted from sea bottom echoes only). The paper presents the comparison of the IFNN system with the other approach, utilising most recently developed decision tree classifier, which constructs classification models by revealing and analysing patterns found in seabed echo records. INTRODUCTION from stage k as well as the input to stage k+1. The decision is later fine-tuned by considering more and more In last decade the advanced swath-beam techniques factors until the final decision, corresponding to the using multibeam sonars have been successfully output variable, are undertaken [3]. introduced for imaging and classifying the seabed. However, the conventional methods of normal incidence – utilising bottom backscatter from a single- beam echosounder – are still in use, due to their simplicity and versatility. Among these methods the application of expert systems and neural networks have been justified its practical usefulness [1]. The paper investigates the neuro-fuzzy classification system in comparison with decision tree FIGURE 1. Basic structure of the Sugeno IFNN adopted classifier, using the same input data i.e. wavelet for a multistage system coefficients extracted from sea bottom echoes. DECISION TREE CLASSIFIER INCREMENTAL FUZZY NEURAL The decision tree algorithm generates a classifier in the NETWORK CLASSIFIER form of a decision tree structure. This is either a leaf, The first classifier analysed in this paper is based on indicating a class or a decision node that specifies some ANFIS – artificial neural network fuzzy inference test to be carried out on a single attribute value, with one system model [3]. ANFIS is able to derive the optimal branch and subtree for each possible outcome of the shapes of membership functions and number of fuzzy test. A decision tree can be used to classify a case by rules from the given data sets. The structure of fuzzy starting at the root of the tree and moving through it until inference subsystem is “hidden” in the neural a leaf is encountered. At each nonleaf decision node, the network, therefore the system adapts its parameters in case's outcome for the test at the node is determined and the learning process. attention shifts to the root of the subtree corresponding The ANFIS was implemented in the multistage to this outcome. When this process finally reaches a leaf, incremental architecture IFNN, as seen in Fig. 1.The the class of the case is predicted to be that recorded at input variables have been divided into M sets and the leaf. each of them is fed to an individual reasoning stage. If any algorithm can be said to have fundamental Therefore there are totally M single-stage ANFIS importance in this software, it is the process of models in serial structure and the fuzzy inference is generating an initial decision tree from a set of training carried out stage by stage [3]. Variable y(k) (k

gain(X) = info(T) – infoX(T) where info(T) represents in fact the entropy of the set

T, infoX(T) is defined as Si(|Ti|/|T|) info(Ti) and |T| is number of cases in set T. The gain criterion prefers the attributes, which have higher gains. Although the gain criterion gave quite good results, it has a serious deficiency, as it generates a strong bias in favour of tests with many outcomes. By analogy with the definition of info(T), we have split info = -S (|T |/|T|) log (|T |/|T|). This i i 2 i FIGURE 3. Decision tree obtained from C4.5 program. represents the potential information generated by dividing T into n subsets, whereas the information For comparison, concurrently to decision tree gain measures the information relevant to algorithm (where all input parameters were processed classification that arises from the same division. Then, simultaneously) the incremental fuzzy neural network gain ratio(X) = gain(X) / split info(X) was also investigated [1]. In the IFNN architecture, all expresses the proportion of information generated by input parameters were processed sequentially and their the split that is useful, i.e., that appears helpful for sequence was determined by the PCA. The final classification. The gain ratio criterion selects a test to percentage of correctly classified echoes obtained was maximise the ratio above, subject to the constraint 93.02% as seen in Fig. 5. Results achieved from both that the information gain must be large – at least as investigated methods are similar. However, decision tree great as the average gain over all tests examined. computation time is shorter in comparison with IFNN.

100% RESULTS 80% 60%

In both investigated classification methods the 40%

Wavelet Transform have been used, as the Principal 20%

0% Component Analysis (PCA) showed, that its Type1 Type2 Type3 Type4 coefficients demonstrate higher degree of importance FIGURE 4. Testing results of the decision tree algorithm; in seabed classification performance than the other echoes correctly classified: 92.22%. echo parameters [1]. The wavelet coefficients Ci are defined by Discrete Wavelet Transform (DWT): 100% 80% C = C( j, k) = x(n)y (n) i ån j,k 60% -j/2 -j 40% where yj,k=2 y(2 n-k) wavelet filter constructed from wavelet function y(· ). Experimental data was 20% 0% acquired from acoustic surveys carried out in the Type1 Type2 Type3 Type4 Southern Baltic using a single-beam digital FIGURE 5. Testing results of the IFNN system; echoes echosounder DT4000 of operating frequency 200 correctly classified: 93.02% kHz. Four types of sediments were represented in the collected data: type1 - mud, type2 - fine and medium- REFERENCES grained sand, type3 - medium-grained sand and type4 - gravel, hetero-grained sand and rock. A set 1. Moszynski M., Dung T.V., Stepnowski A.: "Analysis of the following parameters was extracted from each of the Influence of Wavelet Coefficients ... ", Proc. of digitised bottom echo: eight first wavelet coefficients the Fifth, ECUA'2000, Lyon, 2000, vol. 1, pp.301-306. Ci and sums of the absolute values of wavelet 2. Ross Quinlan J., "Program for machine learning", th coefficients of j level Sj . The program C4.5 [2] was Morgan Kaufmann Pub., London, 1988. used to build up the decision trees. Decision tree 3. Dung T.V., Stepnowski A., ACUSTICA - acta was trained on a learning set of 182 bottom echo acustica, 86, 830-837 (2000). Impulse Radiation of a Sound at Condensation of a Vapor Cavity in a Liquid N.A.Pribaturin, M.V.Alekseev Institute of Termophysics SB RAS , Novosibirsk, 630090, Russia

The experiments of pressure impulse radiation under collapse of vapor cavity in liquid under drastic direct contact between cold water and vapor are suggested.

INTRODUCTION

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There are a plenty of hydroacoustic problems where

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one of the sources of nonlinear acoustic perturbation is ¥

a vapor cavitation. It is well known [1] that a collaps

    

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of vapor cavity produces in liquid a pressure pulse. 

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The asymptotic case of such behavior of a vapor cavity "$# N ¢

is the solution of Rayleigh problem [2]. For these

   

problems, the initial perturbation for cavity collapse is 

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the difference between pressure in the vapor and the )$*

    

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ρ ! liquid. It was proven experimentally [3] that even a

slight pressure disturbance in the equilibrium system ¡ liquid plug - vapor plug can generate a high-amplitude FIGURE 1. Scheme of pressure impulse radiation and wave pressure pulse in liquid. Moreover, this process may diagram.

have a resonance character [4]. +

The objective of this work was an experimental If the liquid has some additional load- P , (e.g., study of possibilities for generation of a powerful hydrostatic load), then the liquid velocity may be pressure pulse after drastic direct contact between even higher. After disappearance of vapor (complete cold water and vapor (with equal initial pressures). condensation on drops and fragments of cold liquid The factor of disturbance in this case must be the penetrating into the vapor zone), the initially accelerated temperature drop. liquid column (Figure 1) slows down abruptly on a solid wall. This, according to the momentum conservation EXPERIMENTAL SETUP law, produces a pressure wave. To perform that kind of experiments, a special setup Let us consider a volumes of a cold liquid and hot was designed and assembled according to a scheme of vapor divided heat-insulated membrane (Figure 1). The a shock wave tube. This is a vertical tube which divide pressure in both volumes is identical, and temperature on two part by means thin diaphragm. Its scheme are of a liquid is much less than vapor temperature. In turn shown on Figure 1. The bottom and top parts are lled the temperature of vapor corresponds to temperature the vapor and cold liquid accordingly. For maintenance of saturation at the given pressure. Let’s imagine, that of constant initial temperatures of vapor and liquids the membrane sharply disappears so, that a cold liquid was used liquid thermostats. A loading of a free level and vapor instantly enter into contact with each other. of a liquid made the break of membrane. Experiments According to thermodynamic equilibrium condition, the were performed for water, with temperature in the

vapor pressure begin to decrease to the saturation level, range from 35 to 96 - , the vapor temperature was

which corresponds to the initial temperature of the 103 - . The initial pressure in vapor and liquid was 0.01 liquid. Our tests demonstrated that this is true : just MPa. During experiments, the pressure and temperature after contact a rarefaction wave penetrates into vapor. It for both liquid and vapor, and liquid velocity were has two zones: the zone of slow pressure decrease and measured. the zone of a drastic pressure fall. During that, the vapor temperature falls below the saturation temperature and RESULTS OF EXPERIMENT small droplets emerge in the vapor. Due to the produced pressure drop, liquid follows to the vapor side. The A typical pressure prole measured directly on the liquid velocity is the higher, the higher the temperature solid wall is shown in Figure 2. We see that after drop between liquid and vapor. membrane rupture it takes some time for liquid to be accelerated and vapor to condense. After that, at the moment of liquid deceleration, a powerful shock wave ∆ R 6 $CT

emerge. It penetrates with the sonic velocity upward ]  2¥3¥3 the liquid column. After reection from the free surface ]  7,7+8 P

(Figure 1), it transforms into a rarefaction wave. Thus, 01¥1 a powerful pressure pulse emerges: its shape is depicted in Figure 3. The maximal duration of the pulse is determined .¥/¥/

by the double time of run for the pressure wave along (¥( the liquid column. At that, we observe conservation * of momentum - the further it is from the deceleration

point, the less the pulse width and higher its amplitude. (

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The amplitude of pressure pulse depends on the length 45

U

of the liquid plug- l , length of the vapor cavity - l , FIGURE 3. Typical prole of pressure impulse.

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" , temperature of liquid -T and pressure on the free Water, P =0.1 MPa, T =60 C, T =103 C, l =0.45 m,

surface. l =0.37 m

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u¤v w

A considerable role in dynamics of the cavity t

x y

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J K

belongs to mass transfer between vapor and liquid. M

hi¤j k l m n

By simultaneous measurement of the pressure and g

K K the volume of vapor cavity, we measured the heat M

ux on the liquid-vapor interface. This value is by

J K thousand times higher than for the equilibrium state. L

The possible reasons for this effect are a strong K K L

development of interface, cooling of vapor fragments, K strong turbulization of liquid and cooling of vapor J

cavity due to formation of droplets of cold liquid.

I

y { |

: ; < = > ? @ A B C D E F G E F H

Behavior of vapor being compressed by moving 9

p q r s

o ∆

z

~  € liquid cannot be described by adiabatic law. Moreover, } the vapor pressure falls below the initial level. The FIGURE 4. Dependence of pressure amplitude from liquid velocity and amplitude of pressure pulses can parameters of experiments reach 20 - 40 m/s and 20 - 40 MPa correspondingly and

the specic energy of a pulse can be about 500 - 900 using of momentum conservation law. The calculation ¡ kJ/m . The dependency of the maximal pressure pulse is a good description of experimental points. amplitude (at the wall) on a dimensionless complex is plotted in Figure 4. The complex includes the lengths of ACKNOWLEDGMENTS This work was supported by Russian Foundation for the liquid and vapor plugs, and the total pressure drop Basic Research (grant No. 00-02-18004-) for the liquid plug. Here we present also the amplitude calculated from a simple equation of liquid motion with REFERENCES [1] I. Aya and H. Nariai, Occurrence threshold of pressure

∆R  $CT oscillations induced by steam condensation in pool

¥ 

woter, Bul. of JSME, Volume 29, No. 253 (1986) ¥

 [2] R. T. Knapp, J. W. Daily and F. G. Hammitt, CAVITATION, McGRAW-HILL BOOK

COMPANY(1970)

¥  [3] V.E. Nakoryakov, B.G. Pokusaev, N.A. Pribaturin,

GLDSKUDJP S.I. Lezhnin, Behaviour of a vapor-liquid medium in ¥ GHVWUDFWLRQ non-stationary dynamic conditions, Heat Transfer 1994: Proc. 10th Int. Heat Transfer Conf., Brighton,(1994),

 Volum 3, 389-393.

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 [4] N.A. Pribaturin, M.V. Alekseev, V.A. Fedorov, U

FIGURE 2. Pressure prole on solid wall. Resonanse phenomena in complex vapour condensation

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" Water, P =0.1 MPa, T =60 C, T =103 C, l =1.1 m, in cooling tube, Letter J. Techn. Physics,(2000), Volum

l =0.37 m 26, No.14, 13-16.