A New Through-Flow Analysis Method of Axial Flow Fan with Noise Models

A New Through-flow Analysis Method of Axial Flow Fan with Noise Models

Chan Lee and Hyun Gwon Kil Department of Mechanical Engineering, University of Suwon1, Republic of Korea1.

Summary The present paper provides a new through-flow analysis method of axial flow fan, which is coupled with discrete frequency and broadband noise models to predict fan's aero-acoustic performance. The present through-flow analysis method calculates pitch-averaged flow velocity, angle and blade surface boundary layer thickness distributions along blade span, and then the calculated flow results are used for noise models to compute the sound pressures of the discrete frequency noise components due to steady rotating and blade interaction forces as well as the spectral density functions of broadband noise sources due to inflow turbulence, blade boundary layer and wake flows. The present method is applied to several axial flow fan cases, and its overall noise level and noise spectrum prediction results are favourably compared with the measurement data within a few percent of relative error.

PACS no. 43.28+h

1. Introduction1 distribution of forward swept axial flow fan by using measurements and CFD simulations. Belami Axial flow fans are widely used in low pressure air et al.[4] compared aero-acoustic behaviours of two handling systems such as air-conditioning, axial flow fans with different sweep angles by ventilating or cooling equipment. In general, as using CAA technique. However, because the CFD shown in Fig.1, the internal flow phenomena of and the CAA techniques still require skilful expert, fan affects fan performance as well as noise a lot of mesh elements and long computation time, because fan noise is due to the aerodynamic they are being used in laboratories but may not be pressure fluctuation produced from the flow suitable for fan designer at actual design practice. phenomena in fan and then is strongly coupled Therefore, in the present study, a new through- with fan performance. Thus, in fan design process, flow analysis method of axial flow fan is it is very important for fan designers to examine developed for the predictions of fan performance the combined effects of blade design parameters and noise. The present through-flow analysis is on fan noise and performance. However, despite conducted by the streamline curvature the wide industrial application of axial flow fans, method(SCM) with flow deviation and pressure there is not proper design-analysis method which loss models. Internal fan flow field and overall fan can be used to predict fan noise and performance performances are computed by the through-flow simultaneously in fan industries. analysis results by SCM. Recent advances in computational fluid dynamics Based on the flow and the performance results by (CFD) and computational aero-acoustics(CAA) the through-flow analysis method, fan noise techniques give fan researchers the prediction models are constructed for blade passing capabilities for fan performance and noise. frequency(BPF) and broadband noise components. Bamberger and Carolus[1] optimized the blade BPF noise is calculated by applying classical design of forward swept axial flow fan with a acoustic theory to steady blade loading and blade modified sweep strategy by using CFD simulations interaction. Broadband noise is also calculated by and acoustic measurements. Hurault et al.[2,3] the spanwise integration of the fluctuating blade investigated the effect of blade sweep on three- surface pressure which is produced due to inflow dimensional flow and blade surface pressure turbulence, turbulent boundary layer and wake

flow. The fluctuating surface pressure is determined by combining correlation models and

Figure 1. Interaction between fan flow, performance and noise. through-flow analysis results. Overall noise level deviation and pressure loss models and then they and noise spectrum of fan are computed by are incorporated into the terms of SCM’s flow summing and integrating the predicted noise equation. Detailed mathematical formulation and components over entire frequency range. In solution procedure of SCCM are described in addition, in order to verify the prediction Novak’s paper[5]. accuracy of the present method, the present For the application of SCM to forward or method is applied to six existing fan models and backward-swept fan, all the design and flow its prediction results are compared with the test variables are defined in swept-blade coordinate. and the CFD results of six different fan models. For example, the swept value(β) for flow angle can be calculated frrom the unsswept one(βUSW) as tanβ 2 2. Throughh-flow analysis method =cosλ x tanβUSW[6].

2.1 Streamline Curvature Method(SCM) Through-flow analysis method assumes steady- state and axisymmetric fan flow field which can be represented as pitch-averaged and hub-to-tip flow surfaces and sets the corresponding streamlines on the flow surfaces(refer to Fig. 2). SCM is onne of the through-flow analysis methods, and is applied to the streamlines to calculate the flow field data such as flow velocity, flow angle and pressure at fan blade outlet. In solving pitch-averaged flow equation by SCM, the enthalpy increase, the relative flow angle and the entropy function of air Figure 2. Flow surfaces and streamlines in fan. are determined by using Euler work equation, flow

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2.2 Flow deviation and pressure loss models The present through-flow analysis method by SCM (1) requires flow deviation and pressure loss models. The present study uses two empirical correlation models, the McKenzie[7] and the modified where SPmB is peak sound pressure at mB mode( B: Creveling[8], for predicting on- and off-design no. of fan blade, m: 1,2,3 … ), N is fan rotating point flow deviations, and the predicted flow frequency, LT is total steady lift of fan blades which is determined by combining cascade theory deviations are also used to calculate flow angle at for section lift(l) and predicted through-flow field fan blade outlet. The present total pressure loss results for flow angle(), and velocity(V). Here, models are used to determine the actual pressure at and are chord and span spectrum functions, fan blade outlet, and are categorized into four and c, b and β are fan blade chord, span and setting components, the blade profile, the secondary flow, angles. In addition, a , R and σ represent the speed the end-wall boundary layer and the tip clearance o of sound, the measuring distance and the elevation flow. The magnitudes of the total pressure losses angle form fan blade tip respectively. The blade are calculated by using the corresponding interaction noise due to the secondary flow and the empirical correlations of Koch and Smith[9], Lee tip leakage flow within fan blades is produced also and Chung[10], Howell[11] and Fujii[11]. In at mutilpe BPFs( mB mode ), and its sound calculating blade profile loss, the correlation of pressure is expressed by Koch and Smith is applied to estimate blade boundary layer thickness at trailing edge, which is (2) also used to compute the pressure fluctuation of broadband noise. where Lsec is the lift fluctuation due to secondary and tip leakage flows, w and E are the width and 3. Fan noise models3 the number of load excursion, and , mean blade loading spectrum function, loading solidity. After fan’s flow distribution and performances are In calculating Lsec, the present study assumes the calculated by above-mentioned through-flow lift fluctuation due to secondary flow as 20% of analysis method, fan noise level and spectrum are steady lift, LT, and uses the Sarajona’s correlation predicted by the noise models for BPF(blade for the tip leakage flow[13,14]. passing frequency) and broadband noise components. 3.2 Broadband noise model 3.1 BPF noise model Broadband noise is produced over entire frequency range due to turbulent boundary layer on blade BPF noise is produced mainly due to rotating surface, inflow turbulence and blade wake. steady fan blade thrust and blade interacion[12]. In According to the theory of sound ration by the present study, the BPF noise component for Carlous[15], acoustic power spectral density rotating steady fan blade thrust is analysed by function(PSDw) is expressed as Gutin’s theory[12] where fan blades are assumed as compact moving sources, and its sound pressure level at BPF or its harmonics can be expressed by , the following equation: , (3) cosβ sin mBmBcosσ where r and r represent tip and hub radii of fan, t h subscripts IFT, TBL, TE mean inflow turbulence,

turbulent boundary layer and trailing edge wake vortex.

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The spectral density function of aerodynamic foforce method, the present analysis method gives the fluctuation on blade surface(PSDF) is obtained by prediction results which agree well with the test the following surface integral on the blade surface results[16]. as

PS , , , ,

, , (4)

where Ac(r) and c(r) are correlation area and chord length varied along blade span. Here, because chordwise variation of PSDsp is much smaller than spanwise one, the surface integral of equation (4) Figure 3. Computation prrocedure of the present can be approximated by spanwise integration. method. In the present study, the spectral density functtions 20 (PSDF) and the correlation areas due to inflow turbulence and turbulent boundary layer are : predcition( axial velovity,sweep = 25 deg, Q=Qd ) calculated by the correlations of Carlous[15], : predcition( radial velocity, sweep = 25 deg, Q=Qd ) : experiment( axial velocity, sweep = 25 deg, Q=Qd ) which are expressed in terms of air flow velocity, 15 : experiment( radial velocity, sweep = 25 deg, Q=Qd ) trailing edge boundary layer thickness, turbulence intensity, turbulence length scale and frequenccy. It is noted that all the flow data used in the Carlous’s 10 correlations are obtained from the through-flow analysis results. Velocity[m/s]

4. Prediction results and discussions4

After axial flow fans are designed, the present 0 through-flow analysis method with noise models is 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 applied to predict internal flow, performance and r/rtip noise level of designed fan. Fig. 3 shows the Figure 4. Flow velocity disttributions of fan. computation structure and procedure of the present method. 50 : prediction( sweep = 0 deg ) : predcition( sweep = 25 deg ) 4.1 Flow and performance prediction resultss : experiment( sweep = 0 deg ) 40 : experiment( sweep = 25 deg ) Fig. 4 shows the prediction results for flow velocities of air along blade span from hub too tip, 30 which are fairly compared with the measurement [2]. Fig. 5 also shows good agreement between the prediction and the test results on fan performance 20 curves.

Pressure[mmAq] Static Fig. 6 shows the effect of fan blade design method 10 with free, combined vortex(DR=0.82) or existing blading dessign concept on fan performance. As 0 shown in Fig. 6, regardless of fan blade deesign 10 20 30 40 50 60

Air flow capacity[CMM] Figure 5. Performance curves of fan.

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20 80 18 : H-Fan(Free vortex) : SPL[ dB ]- Prediction : H-Fan(DR=0.82) : SPL[ dBA ]- Prediction 16 : H-Fan(Existing) : SPL[ dB ]- Experiment : CFD(Free vortex) 60 : SPL[ dBA ]- Experiment 14 : CFD(DR=0.82) : Test(Existing) 12 40 10

8 Noise level[ dB, dBA ] dBA dB, level[ Noise 6 20 Static pressure[mmAq] Static 4 2 0 0 2000 4000 6000 8000 10000 0 0 500 1000 1500 2000 2500 3000 3500 Frequency[ Hz ] Flow capacity[CMH] (a) Straight blade fan Figure 6. Effects of blade design methods on fan performances. 80

: SPL[ dB ]- Prediction : SPL[ dBA ]- Prediction : SPL[ dB ]- Experiment 4.2 Noise prediction results 60 : SPL[ dBA ]- Experiment

Fig. 7 shows the noise prediction results of three fans with straight, forward-swept or backward- 40 swept blades. As can be seen in Fig. 7, the noise Noise level[ dB, level[ dBA Noise ] prediction results by the present method are in 20 good agreement with the measurement[16] for evaluating BPF and broadband noise components. 0 In addition, the comparison results also show the 0 2000 4000 6000 8000 10000 present method is suitable to predict the noise Frequency[ Hz ] reduction by blade sweep. (b) Backward-swept blade fan 5. Conclusions 80 The present study provides a new through-flow : SPL[ dB ]- Prediction method with noise models to predict internal flow, : SPL[ dBA ]- Prediction : SPL[ dB ]- Experiment performance and noise of fan. The present 60 : SPL[ dBA ]- Experiment through-flow analysis uses streamline curvature method combined with total pressure and flow 40 deviation models. Based on the predicted flow and performance results by the through-flow analysis Noise level[ dB, dBA ] method, fan noise models predict BPF and 20 broadband noise components. The present method is applied to eight fans with different design 0 specifications, and its prediction results agree well 0 2000 4000 6000 8000 10000 with the measurement and the test results. From Frequency[ Hz ] the comparison result, the prediction method is (c) Forward-swept blade fan shown to be suitable as design tool for high- efficiency and low-noise fan development. Figure 7. Noise spectra of three fans with different blade sweep designs.

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[16] Development of an integrated computer program for Acknowledgement the design, the performance and noise analyses of automotive cooling axial flow fan. Technical report. This work was supported by the Energy Hanon systems, 2016 Technology Development Program of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from . the Ministry of Trade, Industry and Energy, Republic of Korea(No.20172010106010).

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