This study aimed to employ artificial intelligence capability and computing scalability to predict the velocity field of the straining turbulence flow. Rotating impellers in a box have generated the turbulence, subsequently subjected to an axisymmetric straining motion, with mean nominal strain rates of 4s^-1. Tracer particles are seeded in the flow, and their dynamics are investigated using high-speed Lagrangian Particle Tracking at 10,000 frames per second. The particle displacement, time, and velocities can be extracted using this technique. Particle displacement and time are used as input observables, and the velocity is employed as a response output. The experiment extracted data have been divided into training and test data to validate the models. Support vector polynomial regression (SVR) and Linear regression were employed to see how extrapolation for the velocity field can be extracted. These models can be done with low computing time. On the other hand, to create a dynamic prediction, Gated Recurrent Unit (GRU) is applied with a high-performance computing application. The results show that GRU presents satisfactory forecasting for the turbulence velocity field and the computing scale performed on the JUWELS and DEEP-EST and reported. GPUs have a significant effect on computing time. This work presents the capability of the GRU model for time series data related to turbulence flow prediction.This work was performed in the Center of Excellence (CoE) Research on AI and Simulation Based Engineering at Exascale (RAISE) and the EuroCC projects receiving funding from EU’s Horizon 2020 Research and Innovation Framework Programme under the grant agreement no.951733 and no. 951740 respectivelyPeer Reviewe

Bouhlali, Lahcen

Costa, Pedro

Hassanian, Reza

Helgadottir, Asdis

Riedel, Morris

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ParCFD202233rd International Conference on Parallel Computational Fluid DynamicsMay 25-27 2022, Alba ItalyLAGRANGIAN PARTICLE TRACKING DATA OF ASTRAINING TURBULENT FLOW ASSESSED USINGMACHINE LEARNING AND PARALLEL COMPUTINGR. HASSANIAN∗, M. RIEDEL∗,†, A. HELGADOTTIR∗,P. S. COSTA∗ AND L. BOUHLALI‡∗University of IcelandThe Faculty of Industrial Engineering, Mechanical Engineering and Computer ScienceSaemundargata 2, 102 Reykjavik , Icelande-mail: seh@hi.is, web page: https://english.hi.is/staff/sehe-mail: morris@hi.is, web page: https://english.hi.is/staff/morrise-mail: asdishe@hi.is, web page: https://english.hi.is/staff/asdishee-mail: pcosta@hi.is, web page: https://english.hi.is/staff/pcosta†Juelich Supercomputing Centre52428 Jülich, Germanye-mail: m.riedel@fz-juelich.de - Web page: http://www.morrisriedel.de‡Reykjavik UniversityDepartment of Engineering, School of TechnologyMenntavegi 1, 102 Reykjavik, Icelande-mail: lahcen09@ru.isKey words: Lagrangian particle tracking, Parallel computing, Machine learning, Tur-bulent flowAbstract. This study aimed to employ artificial intelligence capability and computingscalability to predict the velocity field of the straining turbulence flow. Rotating impellersin a box have generated the turbulence, subsequently subjected to an axisymmetric strain-ing motion, with mean nominal strain rates of 4s−1. Tracer particles are seeded in theflow, and their dynamics are investigated using high-speed Lagrangian Particle Track-ing at 10,000 frames per second. The particle displacement, time, and velocities can beextracted using this technique. Particle displacement and time are used as input observ-ables, and the velocity is employed as a response output. The experiment extracted datahave been divided into training and test data to validate the models. Support vector poly-nomial regression (SVR) and Linear regression were employed to see how extrapolationfor the velocity field can be extracted. These models can be done with low computingtime. On the other hand, to create a dynamic prediction, Gated Recurrent Unit (GRU)is applied with a high-performance computing application. The results show that GRUpresents satisfactory forecasting for the turbulence velocity field and the computing scaleperformed on the JUWELS and DEEP-EST and reported. GPUs have a significant effecton computing time. This work presents the capability of the GRU model for time seriesdata related to turbulence flow prediction.R. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlali1 INTRODUCTIONTurbulence flow is observed in many industrial and natural phenomena [1][2]. Smokefrom the chimney, airflow around the car, airplane wing, wind speed, downstream flowin wind farms, and water flow in rivers and waterfalls are examples of the everydayturbulence around us [1][3][2]. The study of turbulence flow is essential because, firstly,to understand the phenomena in many applications. Secondly, it is necessary to knowthe turbulence flow in the next step and prediction in some cases. For example, the windspeed drives the wind turbine, and the output is electrical power. The generated forceis proportional to wind speed, and the power grid needs to have short-term and long-term predictions [4][5][6][7] according to the power demand in the grid. This makes windspeed forecasting important, and, in this case, there is sometimes high-speed wind as anextreme event called gusts which could have an impact on the power grid and electricityproduction [8][9].In fluid dynamics, many experiments have recorded the velocity fields. On the otherhand, from the Computational fluid dynamics view, Direct Numerical Solution (DNS),Large-eddy Simulation (LES), and Reynolds-Averaged Navier Stokes (RANS) are capa-ble methods to solve turbulence flow problems and extract the velocity field and otherproperties [10][11][12]. LES presents reliable result, and DNS dictate the exact solution,but these methods are costly, and it is necessary to run-on High-Performance Computing(HPC). The development in the HPC is rapid, but still, we are far from solving problemsof enormous size [11][12]. This limitation tends us to use other methods to create inter-polation functions from available data or predict the turbulent flow in the short or longterm. Artificial Intelligence (AI) involves methods for interpolation or prediction [13].The machine learning support vector machine (SVM) and linear regression can create afunction and hypothesis with available data and build an interpolation function.In some cases, we could use the created function for extrapolation. However, Deeplearning (DL) networks can forecast time series data via a recurrent neural network(RNN). Long short-term memory (LSTM) is a successful approach for prediction, andmany studies employed this method for time series, and there are combined hybrid meth-ods with LSTM. LSTM is a comprehensive method but costly for large time series, andit consumes extensive computing time because of its architecture. Gated recurrent unit(GRU) is a next-generation LSTM that has a few distinctions from LSTM, and it isreported faster[14].Turbulence flow is a high dimensional phenomenon, and all previous studies used DLability via many layers to find the main hidden feature among the high dimensionality.Then the model has been created for the prediction via extracted main hidden features[11][12]. This study uses time-series data, including 2D velocity field and position corre-sponding to the specified time. This data was extracted from the experiment conductedat the laboratory. This work aims to present spatial and temporal features of the tur-bulent flow dataset to create a GRU model that gives a satisfactory prediction withoutencountering high dimensionality of the turbulence flow. The computing time for theGRU model is scaled, and the report showed GPUs could improve the computing time ef-R. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlalifectively for this model. The results from SVM and linear regression models are mentionedand compared.2 DATA SET FROM EXPERIMENTFigure 1: A photo of the turbulence box taken at the Laboratory for Fundamental Turbulence Research(LFTR) at Reykjavik University [15][16].The turbulent flow is generated with the action of impeller rotors in the corners ofour box turbulence facility (Figure 1). The turbulent flow is then strained in the verticaldirection by the motion of flat circular plates. The fluid is seeded with buoyant (tracer)particles with median diameters 8-10 µm. The specific gravity for tracers was 1.1 gr/cm3(hollow glass). The flow field properties are obtained and studied through the LagrangianParticle Tracking (LPT) method. Equation (1) describes the mean flow field in the facility:⟨U⟩ = (Sx,−2Sy, Sz), (1)Where ⟨U⟩ is the mean velocity –2S is the primary strain rate in the Y-dir, and Sis the strain rate for the other two directions. The straining flow cases were created inthe experiment with mean strain rates, 2S = 4s−1. Equation (1) is based on the laminarflow; however, we know that velocity fluctuates in the turbulence flow[15]. The Lagrangianparticle tracking (LPT) measurements were carried out in 2D, so the velocity componentsin x and y direction is obtained. The turbulence in this project depends on the size of theparticles, on the rotation speed of the impeller in the tank and is applied at 1000 rpm.The mean strain rate 4s−1 was created using two circular plates - the plates driver motormoves according to an exponential velocity profile; the same as a particle would moveby if it were moving along the y axis towards the center of the coordinate system. Thedetection system was set at 10 kHz (10000 fps) for well-resolved particle velocity andacceleration statistics. This very high temporal resolution (0.1-0.2 ms) is considerablyR. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlalismaller than the Kolmogorov time τη (35 - 99 ms) of the smallest eddies present in theflow; therefore, the properties of the dissipation range in the flow are solved. In general,there were 80000 frames for buoyant particles per experiment, and an in-house LPT code.The particle velocity and displacement data are extracted and are used to train the model.These data are divided between test data and training data.3 MACHINE LEARNING MODELS3.1 GATED RECURRENT UNIT (GRU)The data series in this work involves velocity and position corresponding to specifiestime. We aim to employ GRU to create a model with this time series to predict thenext time step in a parallel computing setting. The empirical data is divided into 80%training and 20% test data. This modeling relies on the spatial and temporal features ofthe turbulent flow. We have not used a method to find specific features, and the practicalelements are positioned during the time extracted via LPT. The advantage of this methodis that we conduct the model for every velocity component separately and perform it for2D and 3D velocity components depending on the data availability. Figure 2 representsthe prediction result via GRU evaluated with the actual data.Figure 2: GRU model for turbulence flow velocity in y direction forecasts the next time step and assessedwith actual data3.2 REGRESSION AND SUPPORT VECTOR MACHINE (SVM)Support vector regression polynomial (SVR) and linear regression were employed tocreate the model in a parallel computing setting. Figure 3 shows a prediction fromlinear regression for the velocity during the straining time, and the forecast from SVR isillustrated in Figure 4. It is well-known regression and SVR consuming low computingR. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlaliand very fast than deep learning methods such as LSTM and GRU. But the result dictatesthis method cannot create a prediction model for the turbulence flow velocity field withposition and time. It may be necessary to use more features as input to provide a reliablemodel.Figure 3: A presentation is from the prediction model for particle velocity in y-dir were tracked duringthe straining flow time. (a) 3D view, (b) Y component velocity vs. time. Compared to test data from theexperiment; The blue circle is experiment data. The red line has been predicted via the linear regressionmodel.4 PARALLEL COMPUTINGIn this work to implement the GRU model training, two powerful parallel computingmachines were employed to assess the scale computing. The first one is JUWELS BoosterModule and the other one DEEP-EST, Data Analytics Module (DAM), both at theJülich Supercomputing Centre (JSC) in Germany. JUWELS represents the fastest EUsupercomputer with 122,768 CPU cores only in its cluster module. While JUWELS andmulti-core processors offer tremendous performance, the particular challenge to exploitingthis data analysis performance for ML is that those systems require specific parallel andscalable techniques. In other words, using JUWELS cluster module CPUs with RemoteSensing (RS) data effectively requires parallel algorithm implementations as opposed tousing plain scikit-learn15, R16, or different serial algorithms [17][18]. The computinginformation in this study was carried out based on these parallel machines as below[18][19]:• JUWELS Booster Module:- 1 node involves 2× AMD EPYC Rome 7402 CPU, 2× 24 cores, 2.8 GHz- 936 compute nodes- 3,744 GPUsR. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. BouhlaliFigure 4: An illustration is from the prediction models for flow velocity in the y direction that wereextracted from Lagrangian particle tracking (LPT) during the straining flow time. Compared to testdata from the experiment; The blue circle is experiment data. The red-line has been predicted via thesupport vector polynomial regression (SVR) model.• DEEP-EST, Data Analytics Module (DAM):- 1 rack with 16 nodes, Nodes- 2 x Intel (R) Xeon (R) CPU, 384 GB DDR4, 2 TB non-volatile DIMM (NVM), 1 Nvidia(R) V100 (R) 32GB HBM2 GPU 1 Intel (R) Stratix 10 FPGA 32GB DDR4- Processors: 32 x Intel (R) Xeon (R) Platinum 8260M Scalable Processor @ 2.4GHz (768cores total)The scalability of these two parallel machines for training models is reported in theresult section.5 RESULTSJUWELS booster module assessed with 1 to 4 GPUs with one node. The results inFigure 5 show GPUs made effective speed up for GRU model training in order to with 2GPUs, 3 GPUs, and 4 GPUs the computing time reduces 50%, 100%, and 150% ratherthan one node with only 1 GPU.Figure 6 illustrates the computing scale for described GRU model in this study on 1node with 1 and 2 GPUs of the DEEP-EST DAM module machine. As it is expected theGPUs significantly increased the speed of computing by more than 50Table 1 reports the computing time for the applied parallel computing machine. Also,R. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. BouhlaliFigure 5: Computing scalability, JUWELS BOOSTER MODULE, 1 node assessed with 1 to 4 GPUsfor GRU training model.Figure 6: Computing scalability, DEEP-EST DAM MODULE, 1 node assessed with 1 to 2 GPUs forGRU training model.Table 1: Parallel computing machine scalability to create the GRU model with GPUsParallel Machine/Module Nodes GPUs Computing time [s] Speed-upJUWELS/ Booster Module 1 1 940.643 1.0001 2 629.094 1.4951 3 466.445 2.0161 4 379.135 2.481DEEP-EST/ DAM Module 1 1 881.813 1.0001 2 548.754 1.606table 2 shows the computing time for SVM and regression, which determines these twomethods do not require parallel computing in contrast to the GRU model and can beR. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. BouhlaliTable 2: Computing time to create the ML models; support vector polynomial regression and linearregressionMachine Support vector poly. Regression model Linear regression modelJUWELS/ Booster Module(1 node without GPUs) 17.165 0.016parallel computing [s]implemented more straightforwardly and very fast. Still, they could not provide reliableand useful predictions with this data. GRU is significantly distinct and accurate.6 CONCLUSIONSThis work used time-series data, including 2D velocity field, position, and time of astraining turbulence flow generated in the laboratory. Position coordinate during the timewas concerned as the main feature to predict the velocity field. This statement determinesthe spatial and temporal properties of the fluid dynamics based on the Lagrangian frame-work. The results show the GRU model has a significant prediction for this data. Forthis approach, parallel computing scalability was investigated on two powerful machines.The GPUs can effectively speed up the GRU model, a subset of recurrent neural net-works (RNN), with faster performance than LSTM and simpler architecture. The resultsmention this model could be suggested to employ recorded data for velocity fields suchas wind speed, downstream wake profile for objects in the airflow, water flow in the pipe,and airflow in the channel and generate forecasting for the next step time.FundingThis work was performed in the Center of Excellence (CoE) Research on AI and Simu-lation Based Engineering at Exascale (RAISE) and the EuroCC projects receiving fundingfrom EU’s Horizon 2020 Research and Innovation Framework Programme under the grantagreement no.951733 and no. 951740 respectively.REFERENCES[1] S. B. Pope, Turbulent Flows. Cambridge University Press, 2000.[2] J. L. L. Henk Tennekes, A First Course in Turbulence. MIT Press, 1972.[3] P. A. Davidson, Turbulence: An Introduction for Scientists and Engineers. OxfordUniversity Press, 2004.[4] D. Barbosa de Alencar, C. De Mattos Affonso, R. C. Limão de Oliveira, J. L.Moya Rodŕıguez, J. C. Leite, and J. C. Reston Filho, “Different models for fore-casting wind power generation: Case study,” Energies, vol. 10, no. 12, 2017.R. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlali[5] R. Jursa and K. Rohrig, “Short-term wind power forecasting using evolutionary algo-rithms for the automated specification of artificial intelligence models,” InternationalJournal of Forecasting, vol. 24, no. 4, pp. 694–709, 2008. Energy Forecasting.[6] J. P. S. Catalao, H. M. I. Pousinho, and V. M. F. Mendes, “An artificial neuralnetwork approach for short-term wind power forecasting in portugal,” in 2009 15thInternational Conference on Intelligent System Applications to Power Systems, pp. 1–5, 2009.[7] M. Carolin Mabel and E. Fernandez, “Analysis of wind power generation and pre-diction using ann: A case study,” Renewable Energy, vol. 33, no. 5, pp. 986–992,2008.[8] A. Couto, P. Costa, and T. Simões, “Identification of extreme wind events using aweather type classification,” Energies, vol. 14, no. 13, 2021.[9] M. A. J. C.A.Garcia, P.G.Lind, “On the existence and characterization of extremeevents in wind data,” in Proceedings of the 7. European Conf. on Renewable EnergySystems, pp. 320–327, 2019.[10] C.-M. Lee, Gylfason, P. Perlekar, and F. Toschi, “Inertial particle acceleration instrained turbulence,” Journal of Fluid Mechanics, vol. 785, p. 31–53, 2015.[11] P. A. Srinivasan, L. Guastoni, H. Azizpour, P. Schlatter, and R. Vinuesa, “Predic-tions of turbulent shear flows using deep neural networks,” Phys. Rev. Fluids, vol. 4,p. 054603, May 2019.[12] H. Eivazi, H. Veisi, M. H. Naderi, and V. Esfahanian, “Deep neural networks fornonlinear model order reduction of unsteady flows,” Physics of Fluids, vol. 32, no. 10,p. 105104, 2020.[13] S. L. Brunton, B. R. Noack, and P. Koumoutsakos, “Machine learning for fluidmechanics,” Annual Review of Fluid Mechanics, vol. 52, no. 1, pp. 477–508, 2020.[14] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computa-tion, vol. 9, pp. 1735–1780, 11 1997.[15] S. Hassanianmoaref, “An experimental study of inertial particles in deforming tur-bulence yedreza: in context to loitering of blades in wind turbines,” m. eng. thesis,Reykjavik University, 2020.[16] B. Lahcen, “On the effects of buoyancy on passive particle motions in the convectiveboundary layer from the lagrangian viewpoint,” m. eng. thesis, Reykjavik University,2012.R. Hassanian, M. Riedel, A. Helgadottirs, P. S. Costa and L. Bouhlali[17] M. Riedel, R. Sedona, C. Barakat, P. Einarsson, R. Hassanian, G. Cavallaro, M. Book,H. Neukirchen, and A. Lintermann, “Practice and experience in using parallel andscalable machine learning with heterogenous modular supercomputing architectures,”in 2021 IEEE International Parallel and Distributed Processing Symposium Work-shops (IPDPSW), pp. 76–85, 2021.[18] J. S. Centre, “Juwels user documentation.” https://apps.fz-juelich.de/jsc/hps/juwels/index.html, May 2022.[19] J. S. Centre, “Deep-est - dynamical exascale entry platform - prototype sys-tem.” https://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/DEEP-EST/_node.html;jsessionid=1B6BD3B371D4C56131EB4BAF5C82FD96, May2022.

Lagrangian Particle Tracking Data of a Straining Turbulent Flow Assessed Using Machine Learning and Parallel Computing

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Lagrangian Particle Tracking Data of a Straining Turbulent Flow Assessed Using Machine Learning and Parallel Computing

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