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% T iatffi# ff#3ffi Fm*n.jigv i3.*!lq-*-'.tu' €,)/ #f ili:8ffiEffi&@ *#riroq*Lnf FOREWOR.D t, The ltt Young Scientist Intemational Conference of Water Resources Development and Environmental Protection 2015 (ICWRDEP 2015) water Resources Engineering Department, Faculty of Engineering' IJniversity of Brawijaya was conducted on 5 - 7 June 2015. The Conference was organized by Faculty of Engineering and collaborated with Intemational university of Malaya (uM), universiti sains Malaysia (IJSI\4) and universiti Tun Hussein onn Malaysia (UTHM).

The participants of the Conference af,e about 60 participants come fiom more than 20 higher institutions, such as; Sepuluh Nopember Institute of Technology, surabaya (ITS), Bandung Institute of Technology (ITB), Bogor Agricultural university (IpB), The university of Lampung, , University of Malang (UMM), university of Brawijayar (uB), Padjajaran University, State university of Malang (uM), National trnstitute of Technology (ITENAS), , State Polyechnic of Malang (politeknik Negeri I\{alang), , state Polytechnic of Padang (politeknik Negeri padang), Malang National Technology Institute (Institut Teknologi Nasional Malang), BBWS Mesuji Sekampung, Bengkulu university, (trNDIp), Nusa cendana university, , Bantara University, University of Jember, State polltechnic of Samarinda (Politeknik Negeri Samarinda), uM (university of Malaya), universiti sains l\{alaysia (usM) and Universiti Tun Hussein Onn Malalrsia (UTHM), and others, which reflect the importance water resourcc s engineering development and environmental protection.

The topics conference of are Envirorunental Engineering & Water Technology, Integrated water system & Governance and water science & Engineer.ing. The conference provide platform for researchers, engineers and aoademician to meet and share ideas, achievement as well as experiences throqgh the presentation of papers and discussion. These events are important to promote and encourage the application of new concept of water resources development and techniques to practitiouers as u,ell as enhancing the knowledge of environmenlal protection with the current requirements of analysis, design and construction of any engineering concept. As Head of Water Resouroes Engineering Deparhnent, we would like to express our deepest gratitude to the Rector , Keynote Speakers (prof Satoru Oishi & Prof Tsuyoshi Imai from Japan, Assoc. Prof Faridah othman and prof Amir Hamzah from Malaysia), Intemational Advisory Boald members, organizing committee and also to all participants.

We would like to express our deepest gratiturle to the Faculty of Engineering conducted such conference. This is the first Intemational conference for the Department and we expect that this is will become 2od annual activity for our Department.

Malang, 5 June 2015

Ir. Mohammad Shoiichin, MT., ph.D Head of Water Resources Engineering Department Faculty of Engineering University of Brawijaya

a

rulr Editorial Boards

Dr. Ery Suhartanto, ST.,MT. (University of Brawijaya)

Dr.Ir. Pitojo Tri Juwono, MT. (University of Brawijaya)

Anggara WWSj S'['.,M.Tech. (University of Brawijaya)

Dr.Ir. Ussy Andawayanti, MS. (University of Brawijaya) t ? Fahmi Hidayat, ST.,MT. (Perum Jasa Tirta 1)

Gatot Eko Susilo, S'I.,M.Eng.Env.,Ph.D (The University of Lampung)

Editorial Reviewers

ProflDr. Ir. Mohammad Bisri, MS. (University of Brawijaya)

Prof. Ir. Dr. Amir Hashim bin Mohd Kassim (UniversitiTun Hussein Onn Malaysia)

Prof. Tsuyoshi Imai (Yamaguchi I.Jniversity, Japan)

Proi. Dr. Ir. Suhardjono, M.Pd.Dipl.HE (University of Brawijaya)

Prof. Satoru Oishi (Kobe University, Japan)

Prof. Dr. Nor Azazi Zaknia(Universiti Sains Malaysia)

Dr.Ir. Rispiningtati, M.Eng. (University of Brawijaya)

Ir. Moh. Sholichin, MT.,Ph.D (University of Brawijaya)

Prof. Dr. Ir. Nadjaji Anw.ar, M.Sc (Sepuluh November Institute of Technologl')

Ir. Dantje KardanaN, M.Sc.,Ph.D (Bandung Institute of Technology)

Dr.Eng.'Iri Budi Prayogo, ST.,MT. (Uhi.r'ersity of Brawijaya)

Dian S isinggih,ST.,MT.,Pt .D (University of Brawijaya)

Ir. Abubakar Alwi, Ph.D (University of Tanjung Pura)

Dr. Eng. Andre Primantyo Hendrawan, ST'.,MT. (University of Brawijaya)

Assoc. Prof. Dr. Faridahbinti Othman (Universiti Malaya)

Dr. Ir. Widandi Soetopo, M.Eng. (University of Brawijaya)

Dt. Eng. Donny Harisuseno, ST.,MT. (University of Brawijaya) Table of Gontent

Page

Editorial Boards ...... iii Editorial Reviewers...... iii

THEME I Environmental Bngineering & Water Technology

Circulation Effect Of Coffee Wastewater FIow In Water Hyacinth Phytoremediation...... A-1 ElidaNovita, Sri Wahyuningsih, Siswoyo Soekarno, Betty Siska Rukmawati

Potential Greywater Quantification For Reuse In Newton Residence Apartment Bandungr lndonesia .....:...... A-2 Dyah Asri Handayani Taroepratjeka, Yulianti Pratama, Devi Ayu Putrianti

Analyzing Water Quality Changes Due To Agriculture Activities In Seputih Irrigation Area, Lampung Provinc€, . A-3

Eka Desmawati, Rusdi Effendi, Yudha Mediawan, Gatot E. Susilo

Evaluation of Environmentally F'riendly Flushing in Wlingi and Lodoyo Reservoirs .... A-4 Fahmi Hidayat

Dynamic of Dissolved Oxygen At Inlet Zone Of Fish Cage Area In Cirata Reservoir,

West Javar lndonesia .... A-5

Fanny Novia, Priana Sudjono, Arief Sudrajat Intensive Agriculture of Peat Land Areas To Reduce Carbon Emission And Fire Prevention (A Case Study In Tanjung Jabung Timur Tidal Lowland Reclamation

Jambi)...... ,{_6 Momon Sodik Imanudinl, and R.H Susanto

Mikro-Nano Activated Charcoal from Ricestraw as Adsorben Heavy Metals Leachate, case Studies on "TPA JATIBARANG"; semarang Jawa t Tengah .. A-7 I pizki Januarita, Anis Ulfa W.A, Azka Azizah,Hilma Muthi'ah

Determination of Water Qualify Status at Karang Mumus River samarinda,

Indonesia. !...... , ..... A_g Sri lestari, Diana Arfiati, Aniek Masrevaniah, Moch. Sholichin

Efficiency Analysis ofCod And Bod Decline Wastewater Coffee On Phytoremediation ProcessUsingWaterHyacinth@ichorniaCrassipes(Mart.)Solms)

Sefyorini, Sri Wahyuningsih, Elida Novita

Green Roof: vegetation Response towards Lead and Potassium ...... A-10 Khairul Rahmah Ayub, Aminuddin AB Ghani, Nor Azazi Zakaia

THEME 2 Integrated Water Systems & Governance

Experience in Rainwater llarvesting Application tr'or Household Scale In Bandar Lampungr lndonesia ..... B-l Gatot Eko Susilo

Estimation of the Flood Using Data Modis to Support Integrated Water Resources Management ...... B,-2 Gusta Gunawan, Alex Surapati, Besperi

Alternative Selection for Water Resource Potential in Brantas Watershed For The Development of Hydroelectric Power Plant ...... B-3 Deviany Kartika, Miftahul Arifin Analysis Availability on the crean water Infrastructure at PDAM ... B_4 Nani Nagu

Rainfall Estimation using weather Radar and the Flood simulation at ciliwung . River rndonesia Analysis...... B_5 Ratih Indri Hapsari, Agus Suhardono, Reni Sulistyowati

Integrated coastar zoneManagement with watershed Managenent Based on co-Management: porong A case study River Arong sidoardjo-pasuruan Coastal Area...... Rudianro """"""" 8-6

The Evaluation of Song Bajul springs Potency f,'or Residentrs clean water suppty rn Desa Pucangraban Kecamatan pucangraban Kabupaten 2015_2030 Turungagung In ...... _...._...... B_8 Sam Yudi Susilo, Hendra Agus prastyo

Flow Analysis on Pipe Distribution Network Using Differential Evolution Algorithm (De) ...... sulianto """"" B-9

Hydroinformatics In vorumetric And Real rime r.rigation Discharge Monitoring...... --._:-...... B-10 Susi prihantoko, Hidayah, Aditya and Irfan Sudono

Multiple stacked Rure curves For Rese.oir operation of Medium Reservoir...... B_l r widandi Soetopo, Lily Montarcih Limantara, Suhardjono, ussy Andawayanti, Rahmah Dara Lufira water Balance Anarysis Due To the Human Live Requirements ..8-r2 Agus suharyanto, very Dermawan, Mustika Anggraeni, pudyono , Kurniawan sigit wicaksono, Diah Susilowati \. €--.-6.. *:- =-.a- -&9, - :'l:.+1

Optimization System Network Providing Water Study Blitar District Of Kademangan

East Indonesia .... B_13 Rahmah Dara Lufira, Suwanto Marsudi, Jadfan Sidqi F., Evi Nur Cahya

Safety Inspection of Prijetan Dam...... B_14 Runi Asmaranto

Analysis of Conditions Changes In Sumi Dam. t{ydrolory Farameters Design ...... B-15 Anggara WW. Saputra

THEME 3 Water Science & Engineering

Investigation of Marine Debris In Kuta Beach,8a1i...... C_1 Adli Attamimi, Noir P. Purba, Santi R.Anggraini, Syawaludin A. I{trahap

Design of Marine Propulsion System Based On Structural Vibration...... C-2 Asep Andi. Radite Praeko Agus Setiawan

Transmission and Wave Reflection on Double Subrnerged Breakrvater...... C-3 Bambang Surendro

Estimates of Time of Concentration in Rainfall, Runoff and Infiltration Application...... C-4 Dian Noorvy, Lily Montarcih, Donny Harisuseno

Calibration of Measurement on Modelling Stepped Spitlway ...... C-6 Denik Sri Krisnavanti, Soehardjono. Moch.Sholichin, Very Dermawan, Nina B.Rustiati

Comparing the Calculation Method of the Manning Roughness Coefficient in Open I Channe1s...... C-7 Hari Wibowo

GroupingWatershedsThroughHierarchicalClusteringApproach.....'...... '

Judi K. Nasjono, Mohammad Bisri, Agus Suharyanto, Dian Sisinggih =1:€

Study on the Effectivity of Decreasing Permeability and Increasing Shear Strength of Sandy Beach Soil And River Soil tsy Using Exoplysaccharide Biopolymer...... C-9 Emma Yuliani, Maytri Handayani, Ariska Desy Haryani

Heat Effect on Fluid Free Convection Flow Past A porosity Sphere...... c-10 Mohamad Tafrikan, Basuki Widodo, Chairul Imron

rncompressible and Steady Mixed Convection Flow Fast Over a Sphere...... C-11 Mohammad Ghani, Basuki Widodo, Chairul Imron

Viscoelastic Fluid Past a Flat Plate with the Effect of Magneto hydrodynamic ...... C-lz Putri Pradika Wanti, Basuki Widodo, Chairul Imron

Florv Measurement Under Sluice Gdte Model c-13 Rustiati, N.B., Suhardjono, Rispiningtati, Dermarvan, V., Krisnayanti, D.S

Kinetic l\{odeling of Domestic wastewater (Greywater Type) Using uasb

Reactor ."..... C-14

S. Syafuudin, P. Purwanto, S. Sudarno

An Imaging Technique for ldentifying Flow Structure and Magnitude In A Channe1...... C-15 Tommy E. Sutarto, Habir. S.S.N. Banjarsanti-

The Numerical Solution Of Free Convection Flow of Visco-Elastic Fluid With Heat Generation Past Over A Sphere . C-16 Wayan Rumite, Basuki Widodo, Chairul Imron

Assessment of Sedimentation Patterns and the Threat of Flooding due to Reclamation i in The Lamong Bay, Indonesia .... C-I7

Mohammari Sholichin, Tri Budi Prayogo, Sebrian Mirdeklis Beselly Futra, Rini Wahyu Sayekti

Design Improvements To The Physical Model Test Spillway Of Mujur Dam ln Lombok Tengah Region...... C-18 Dian Chandrasasi, Dwi Priyantoro, Anggara WW. Saputra ;i*:

Model Test of Physical Spillway In Lesti l)am, Malang District East Java .... C-19 Heri Suprijanto, Janu Ismoyo, Sumiadi, yuli Astuti

A Network Rain Station in Reviewed of the Topography on Watershed Widas District Nganjuk-EastJavaofIndonesia......

Eri Prawati, Suhardjono. Lily Montarcih, Rispiningati

Hydropower Plant using Pump storage at Cisokan Dam ...... C_21 Endang Purwati

6 Environmental Engineering & Water Technology The l$ Young Scientist International Conference of Water Resources Development and Environmental Protection Malang, Indonesia, 5-7 June 2015 An Imaging Technique for Identifying Flow Structure and Magnitude in a Channel Tommy E. SUTARTOT*, HABIR2, S.S.N. BANJARSANTIT

t Department of Civil Engineering, Samarinda trri:r;:!:ifnic,Jl. CiptoMangunkusumo, Samarinda 7 5 I 3 I, 2Doctoral Student Department of Civil Engineering, , Jalan Perintis Kemerdekaan KM-10,

* C orr esponding,,,r#{f#; :i:#*rr, o@gmait. c om "rt

ABSTRACT The goal of this study was in three folds, first, to rnap the flow structure in a charurel using a unique technique named Large Scale Particle Image Velocirretry (LSPN) Second, to test the sensitivity of LSPIV results to the LSPN parameters (e.g., Interrogation Area, and Searching Area). Third, to test the capability of LSPIV method in predicting the magnitude and clirection of flow velocity in a complex flow structure. An LSPN system was set to observe 14 (fourteen) runs of laboratory re-circulating trapezoidal open channel vdth a complex flow structure was developed at its sudden expansion-constriction reach. LSPIV technique was successfully mapping the core flow and the swirling motions near the wall in the e>lparrsion-constriction reach. In the core flow region, longitudinal velocity, U, estimate was sensitive to the interrogation qrea and searching alea parameters, while lateral velocity, V, estimate was insensitive to those parameters. The opposite condition occurred in the wall region. The maximum forward flow velocity was 0.56 m/s occurred in the core flow region. This value was closed to channel bulk velocity U=0.4 m/s measured using flow meters. The largest backward velocity was 0.068 mls, obtained in left wall region where the swirling motion occurred.

KEYWORDS Complex florv structure; LSPIV; seeding pa.rticles; interrogation area; searching area.

c-15 e #- ict.rnclepr*m CERTIFICATE

This certificate is awarded to: TOMMY EKAMITRA SUTARTO

State Polytechnic of Samarinda

as a: PRESENTER for paper titled:

"An lmaging Technique For ldentifying Flow Structure And Magnitude ln A Channel"

in The ft Young Scientist lnternational Confere ng€A|Water Resou rces Develo p ment a nd Envi ron me nqal'protectio n Malang, 5-7 June 2015

,flr$ Enginering 'r' -{r, {q (;

mad Bisri, M5. i Juwono, MT. {H€} PRtrSEf+{TER

TOMMV EKAMITRA

OrF ilit gt{ffi

$ecr*tariat: Water Res*urces Engi*eering Departm*nt Faculty af l*gineering, University of Srawij*ya Jelan MT. Haryono no- 167

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.mffijiffiilH$$ The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

An Imaging Technique for Identifying Flow Structure and Magnitude in a Channel Tommy E. Sutarto1*, Habir2, S.S.N. Banjarsanti1 1Department of Civil Engineering, Samarinda State Polytechnic,Jl. CiptoMangunkusumo, Samarinda 75131, Indonesia *Corresponding author’s e-mail: [email protected] 2Doctoral Student Department of Civil Engineering, Hasanuddin University, Jalan Perintis Kemerdekaan KM-10, Makassar, Indonesia

ABSTRACT The goal of this study was in three folds, first, to map the flow structure in a channel using a unique technique named Large Scale Particle Image Velocimetry (LSPIV). Second, to test the sensitivity of LSPIV results to the LSPIV parameters (e.g., Interrogation Area, and Searching Area). Third, to test the capability of LSPIV method in predicting the magnitude and direction of flow velocity in a complex flow structure. An LSPIV system was set to observe 14 (fourteen) runs of laboratory re-circulating trapezoidal open channel with a complex flow structure was developed at its sudden expansion-constriction reach. LSPIV technique was successfully mapping the core flow and the swirling motions near the wall in the expansion-constriction reach. In the core flow region, longitudinal velocity, U, estimate was sensitive to the interrogation area and searching area parameters, while lateral velocity, V, estimate was insensitive to those parameters. The opposite condition occurred in the wall region. The maximum forward flow velocity was 0.56 m/s occurred in the core flow region. This value was closed to channel bulk velocity U=0.4 m/s measured using flow meters. The largest backward velocity was 0.068 m/s, obtained in left wall region where the swirling motion occurred.

KEYWORDS Complex flow structure; LSPIV; seeding particles; interrogation area; searching area.

INTRODUCTION This study implemented the capabilities of Large Scale Particle Image Velocimetry (LSPIV) to predict the surface velocity vectors in a complex flow structure. The goals of the study can be divided in three folds, i.e. 1. Mapping the flow structure in a channel using LSPIV technique, 2. Testing the sensitivity of LSPIV results to the LSPIV parameters (e.g., Interrogation Area, and Searching Area), 3. Testing the capability of LSPIV method to predict the magnitude and direction of flow velocity in a complex flow structure.

A series of experiments were performed in a laboratory applying an imaging technique known as LSPIV introduced for the first time by Fujita et al. (1998). Basically, this technique has a capability to identify the flow structure and measure the surface velocity by recording and analyzing the movement of a group of particles (structure) moving with the flow. The surface velocity was measured by identifying the distance of displacement of moving particles and dividing it with the time interval between the two successive images. LSPIV is recognized as a very powerful technique in identifying the flow structure in the order of magnitude larger than turbulence scales in the large water body (Muste et. al., 2000). This capability cannot be found in equipments that can only measure local velocity such as ADV. The LSPIV can capture instantaneous velocity in a large number of points at once on the water surface (Fujita et al.,

1 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

1998, Muste et al., 2011). This capability allow us to map the flow structure in the stream such as the swirling motion around a hydraulic structure, reversed flow region, secondary flow near the outer bend, etc. at a lower cost. The other advantage, this technique is a non-contact /nonintrusive technique that can give more accurate result compare to the intrusive one. The detail on the theoretical approaches used for this method can be found in Fujita et al. (1998), Harpold et al.(2006), and Muste et al. (2008).

EXPERIMENT SET UP A re-circulating open channel with a trapezoidal shape was prepared to perform the study (see Fig. 1a) in the laboratory. This system was equipped with a pump (3HP) to re-circulate water and sediment. In the middle reach of the channel, a sudden expansion-constriction shape (Fig. 1a) was developed. This area of expansion and constriction would be the area of interest where the flow pattern was unique characterized with swirl flows near the channel wall, an ideal place for testing the accuracy of LSPIV technique in the case of mapping complex flow pattern. Sands were poured on the bed of the channel to mimic the natural bed roughness (Fig. 1b). However, the channel bed at the entrance of the flow was covered with cement blocks (Fig. 1c) to avoid headwater scour. A wooden trash rack was applied at the upstream end of the channel (Fig. 1c) to develop a quasi-steady uniform flow in the channel.

A high definition digital video camera (Sony HDR HC 1, see Fig. 1d) was used to record the flow in the channel. The suitable specification of camera recorder is dictated by the turbulence flow scales and the scale of any disturbances in the flow. This would be discussed later in the experiment run section. The camera was harnessed above the channel and adjusted with a tilting angle of 70 degrees such that it pointed perfectly upon the interested area. The best tilting angle of the camera is 90 degrees, in other words, the optical axis is perpendicular to the flow. Kim (2006) reported that 10 degrees is the acceptable limit. Small tilting angle results in highly distorted images which are difficult to correct with the image transformation algorithm (Muste et al., 2008; Kim, 2006).

Figure 1. Channel set up in the laboratory. (a) Open channel with an expansion-constriction in the middle reach. (b) Sands were poured on the channel bed to mimic the bed roughness. (c) A wooden trash rack and cement bed at the entrance of the channel. (d) A high definition digital video camera (Sony HDR HC1) used in this study.

2 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

The images produced by the camera must be clear, have a good contrast and no glare and shadow on the water surface. Such quality of image would allow the LSPIV software to detect the movement of particles on the water surface more accurately (Hauet et al., 2008) and therefore resulting in reliable flow field map. To obtain the required images, in this study, the interested area was illuminated using two halogen lamps (Fig. 2). The facility was surrounded with blue plastic curtains (Fig. 2) to protect the area from uncontrollable natural lights. All other lights not from those two halogen lamps were shut off to simply control the illumination intensity.

Six reference points (A, B, C, D, E, and F) were assigned exactly on the water surface around the defined area of interest (Fig. 3). The physical coordinate (X,Y) of these points were determined by measuring their distance (in meter) from point A (0,0) both in lateral and longitudinal directions. These coordinates were then required as input data in the image transforming process for removing image distortion due to the distance between lens and the targeted object, or due to the oblique angle between the camera recording axis and the imaged flow (Fujita, et al., 1998). Therefore, all these reference points must be visible in the recording window of the camera.

Figure 2. Two halogen lights were placed surround the interested area (expansion-constriction area) to obtain perfect illumination for the interested area. Curtains were used to protect the facility from uncontrollable lights coming from outside the experiment area.

Figure 3. Six reference points (A, B, C, D, E, and F) assigned exactly on the water surface. These point- coordinates were required as inputs in image distortion corrections.

3 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

EXPERIMENT RUN The open channel was a re-circulating system. To start the experiment, water was pumped into the channel with a constant discharge through the upstream end and collected in a basin next to downstream end of the channel. From the basin, water was pumped back to the upstream end through a pipe located under the bottom of the channel. The channel bed slope, , was 0.0001, the water depth, , was 0.37 m, and the bulk velocity, , was 0.4 m /s. These hydraulic conditions were maintained until the end of the experiments. ℎ

To trace the water movement, flow tracer particles (Eco-foam) were poured uniformly over the water surface (Fig. 4). The so called seeding was performed at the upstream of the interested area while the camera was recording the movement of the particles passing through the interested area. The tracer particles were biodegradable eco-foam peanut which are granular packaging materials made of predominantly corn syrup. These particles have low inertia and submergence, therefore they are able to follow accurately local flow movements. The size of the particles was about 2 cm, large enough to be detected by the camera (Muste, 2010). In this study, the seeding particles were uniformly dispersed over the water surface to avoid velocity-estimate errors caused by grouping (clustering) of particles and not having representation in all areas of the flow field. However, high velocity gradient in the flow and the particle-to-particle electrostatic force might induce the aggregation of the seeding particles (Muste et al., 2008). The density of the seeding material was in the range between 10 and 30 percent of the surface area. In this range, the error in surface velocity estimates was at the lowest level (Meselhe, et al., 2004).

To record particle movements on water surface, the camera on top of the interested area was turned on. As it was mentioned previously, framing frequency of the camera and total recording time where affected by the time and length scales of turbulence flow and any disturbances in the flow. Length scale of macro eddies, in streamwise direction at the free surface area was determined as follow: (1) where is water depth in meter, equals 1.0 for bed shear Reynolds number ≥ 1600, equals 1.1 for in the range of 600 and 1600. The Reynolds= 0.77 number, , was computed as : ℎ ∗ ∗ ∗ (2) ∗ -6 2 where is shear velocity in meter/ second, and∗ is water kinematic viscousity (1.1 x 10 m /s). The shear velocity, , can be calculated as =. Where is water density (998 kg/m3), is gravity acceleration∗ (9.81 m/s2), and is bed slope (0.0001). Given = 0.0001, = 0.37 m, and = 0.4 m /s, and using equation∗ 1 and 2, it was found that∗ = =ℎ0.36 m/s, = , = 1, = 0.285 m. ℎ Time scale of macro eddies, , can be approximated∗ as: ∗ 1.2 10 (3) that gives = 0.71 seconds. Based on the relation between time scale, , and total sampling time, T and assuming the level of turbulence was 20 %, the ≌required/ measurement time or sampling time, equals 150 seconds or 2.5 minutes at the level of error = 2.5 %. In this study, the total recorded time was decided to be 10 minutes.

The maximum frequency occur in the turbulence body is fm=(50/3.14)(U/h) = (50/3.14)(0.4/0.37) = 17.2 Hz. According to Nyquist criterion, sampling frequency fs should be larger than 2fm, this means the capability of the video recorder must be larger than 24.4 Hz or 25 frame per second. The camera used in this study has a capability of recording 30 frames per second, an ideal equipment for performing LSPIV technique (Muste et al., 2008).

The video recorded in the lab experiment was discretized into a sequence of images with a time step of 0.033 second and then analyzed in LSPIV. To analyze the sensitivity of Interrogation Area (IA) to results

4 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015 of LPIV (e.g., average longitudinal and lateral velocity vector U and V), seven runs (Run no. 1 to 7) were performed by assigning different values for IA, e.g. 90, 80, 60 50, 40, 30, and 20 pixels, respectively. IA= 90 means the area of IA is 90 pixels by 90 pixels. SA consists of four sizing components viz. Sim, Sip, Sjm, and Sjp (Fig. 5) where “i” denotes the longitudinal direction, “j” denotes the lateral direction, “m” stands for minus, and “p” stands for positive. Constant sizing component values were assigned for those seven runs, e.g. Sim = 10 pixels, Sip = 5 pixels, Sjm = 5 pixels, and Sjp = 5 pixels. Assigning Sim = 10 pixels means the IA will moves as far as 10 pixels forward from the center of SA. At the opposite, assigning Sip = 10 pixels means the IA will moves as far as 10 pixels backward from the center of SA.

Figure 4. The tracer particles moved following water movements in the interested area while a camera was fixed on top of this area recording the event. In contrast, the sensitivity of Searching Area (SA) to average longitudinal U were studied by performing seven runs (Run no. 8 to 14) with constant IA value (IA=90) and different Sim values for each run. In this study, only Sim were varied, viz. Sim = 1, 2, 3, 5, 7, 8, 9 pixels, respectively. The values of Sip, Sjm, Sjp were 5 (five) pixels for all runs.

The U and V were observed at 14 (fourteen) selected points A, B, C, D, E, F, G, K, L, M, and N (Fig. 6). Point A, B, C, D, E, F were assigned around the center line of the channel, the main flow could be observed in this zone. Point G, H, I, and J were located at the right side of the channel (facing to the flow direction) where the flow pattern is different from the core flow. Similarly, point K, L, M, and N were specified at the left side of the channel where the swirling motion can be observed trough these points.

Sip SA

Sjm IA Sjp

5 pixels 10 pixels Sim

Figure 5. Searching Area and Interogation Area.

RESULTS The results of the first 7-run (Run no. 1 to 7) were presented in Fig. 7a and b that shown a plot of IA versus U (Fig. 7a) and IA versus V (Fig. 7b) monitored at point A, B, C, D, E, F, G, H, I, J, K, L, M, and N. On the other hand, the results of the second 7-run (Run no. 8 to 14) were presented in Fig. 8 that

5 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

shown a plot of Sim versus U monitored at point A, B, C, D, E, F, G, H, I, J, K, L, M, and N. The dashed red lines represented the velocity vectors obtained from point A, B, C, D, E, and F (U (Fig. 9a and b) which were located at the core flow region (Fig. 6). The blue solid lines represented the velocity vectors predicted at point G, H, I, and J near the right wall (facing downstream). The black solid lines shown the velocity vectors predicted at point K, L, M, and N closed to the left wall (facing downstream). The LSPIV method was also successfully captured the flow structure in the interested area with swirling motions were present in the left and right wall regions as demonstrated in Fig. 9 that showed the flow streamlines.

U+ V- V+

U-

Figure 6. Observation Points. Inset: flow direction.

(a) (b)

Figure 7. Sensitivity of Interrogation Area IA to (a) longitudinal velocity U, and (b) lateral velocity V observed at point A to N.

6 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015

Figure 8. Sensitivity of Sim to longitudinal velocity U at Figure 9. Flow streamline in the wall- point A to N. expansion.

DISCUSSIONS

Sensitivity of U-estimate to IA In Fig. 7a, the solid blue lines for right wall region (point G, H, I, and J) and the solid black lines for the left wall region (point K, M, and N) depicted quite constant values of U with IA. This means that the longitudinal velocity, U, were not sensitive to IA at the wall regions. This can be associated with the fact that the magnitudes of U were very low at these regions. An exception was found for the solid lines of point L in Fig. 7a. This line demonstrated observable changes of U magnitudes with increasing IA. This might occur as this point was close to the main flow region such that the U magnitude at this point was quite large, close to the one in the flow region.

On the other hand, the dashed red lines for core flow region (point A, B, C, D, E, and F) demonstrated rather fluctuating values of U with IA. Thus, the velocity, U, were sensitive to IA in the core flow region. The reason that the U magnitudes were large in the core flow region can explained why U was sensitive to IA in this region.

Sensitivity of V-estimate to IA At main flow region (point A, B, C, D, E, F), the magnitude of lateral velocity, V, was apparently less sensitive to IA. The change of V was not significant (Fig. 7b), the plotted results constructed almost straight line (see red dashed lines in Fig. 7b). This may be caused by the fact that lateral velocity V is very small in the core flow region. The line of point B was an exception; this line was less constant compared to others, this may occurred due to the effect of longitudinal velocity U that might be also present at this point.

At regions near the left wall (point K, L, M, N) and right wall (point G, H, I, J), the magnitude of lateral velocity V is sensitive to IA. The plotted solid black lines and solid blue lines in Fig. 7b demonstrated the distinguished changes of V with IA. This occurred because the lateral velocities V were quite significant near the wall regions where the swirling motion appeared. Shortly, one could conclude that the flow pattern contribute to the sensitivity of velocity to IA.

The U and V magnitudes tend to be constant as the IA is larger than 80 pixels, based on Fig. 7a and b. Therefore, in this case, IA optimum is 80 pixels. Assigning values less than 80 pixels will results in

7 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015 wrong velocity prediction, while assigning values larger than 80 pixels will increase the computational time.

Sensitivity of U-estimate to Sim Fig. 8 showed the change of longitudinal velocity U with increasing Sim. At the wall region (represented by solid blue and black lines), the U magnitudes tended to be more stable with increasing Sim. Compare to the core flow region (represented by dashed red lines), the U magnitudes changed significantly while the IA was increased from 2 to 3 pixels. The predicted U magnitudes tend to be constant, both in the wall and the core flow region, as the Sim were not less 7 pixels. This means that the optimum value for Sim is 7 pixels, increasing the value more than the optimum value is meaningless, it could even increase the computational time and reduce the accuracy of results.

Predicted values for U and V In Fig. 7a, all the dashed red lines showed negative longitudinal velocities (U-), while most of the blue and the black solid lines showed positive longitudinal velocities (U+). This means that the core flow has moved forward while the flow near the right and red wall has moved backward indicating the presence of swirling motions near the left and the right wall. This was depicted clearly on the plot of flow streamline shown in Fig. 9. The longitudinal velocities were more dominant in the core flow region than near the wall regions. The largest forward velocity was 0.56 m/s (closed to bulk velocity U=0.4 m/s) observed in the core flow region (point F, Fig. 7a), while the fasted backward velocity was 0.068 m/s obtained in left wall region (point N, Fig. 7a).

Fig. 7b shown that lateral velocities, V, were in the range between 0.071 and 0.012 m/s. These values were much lower than longitudinal velocities (0.56 to 0.07 m/s, Fig. 7a). The red dashed lines tend to collapsed at V=0, the blue solid lines and the black solid lines tend to show negative lateral velocities (V-) and positive lateral velocities (V+), respectively. This means that the lateral flow was not present in the core region, the water near the right wall (facing downstream) has flowed toward the right wall with a maximum velocity of 0.065 m/s toward the right side recorded at point H. While, the water near the left wall has flowed toward the left wall with a maximum velocity was of 0.071 m/s toward the left side recorded at point L.

CONCLUSIONS The LSPIV method has successfully predicted the flow structure in the wall-expansion area. The velocity vector and the flow streamline developed by LSPIV could depict the core flow and swirling flow occurred near the left and right bank. In the main flow region, the longitudinal velocities, U, predictions were sensitive to the LSPIV parameters (e.g., Interrogation Area, IA, and Searching Area sizing components, Sim), while the lateral velocity, V, predictions were not sensitive. This was due to the fact that the longitudinal velocity was major while the lateral velocity was minor at this region. Oppositely, in the wall region, the longitudinal velocities, U, predictions were not sensitive to the LSPIV parameters, while the lateral velocity, V, predictions were sensitive.

Knowing the optimum value for LSPIV parameters could avoid wrong results and help saving the computational time. In this study, it was found that the optimum IA was 80 pixels and the optimum Sim was 7 pixels. Assigning values smaller than the optimum one will result in inaccurate velocity prediction, while assigning values larger than the optimum one will increase computational time unnecessarily.

In this study, the magnitude and direction of longitudinal and lateral flow velocities were successfully predicted using LSPIV method. The maximum forward flow velocity was 0.56 m/s occurred in the core flow region. This value was closed to channel bulk velocity U=0.4 m/s measured using flow meters. The largest backward velocity was 0.068 m/s, obtained in left wall region (facing downstream) where the

8 The 1st Young Scientist International Conference of Water Resources Development and Environmental Protection, Malang, Indonesia, 5-7 June 2015 swirling motion occurred. The largest lateral flow was 0.065 m/s toward the right side and 0.071 m/s toward the left side observed in the right and left wall region, respectively. The lateral flow velocities in the core flow region were closed to zero.

ACKNOWLEDGEMENT The autors would like to acknowledge the Fulbright Indonesia Presidential Scholarship Program, and IIHR-Hydrosience Engineering University of Iowa that has provided Paul C. and Sarah Jane Benedict Fellowship, Dr. Arthur R. Giaquinta Memorial Fellowship and laboratory fasilities for performing this study.

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