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DTSTAMP:20230124T171521Z
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DTSTART;TZID=America/Chicago:20221117T083000
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UID:submissions.supercomputing.org_SC22_sess275_rpost142@linklings.com
SUMMARY:Parallel Computing Accelerates Sequential Deep Networks Model in T
 urbulent Flow Forecasting
DESCRIPTION:Posters, Research Posters\n\nParallel Computing Accelerates Se
 quential Deep Networks Model in Turbulent Flow Forecasting\n\nHassanian, R
 iedel, Helgadottír\n\nThis study aimed to employ deep learning capability 
 and computing scalability to create a model and predict the velocity of th
 e straining turbulence flow. The turbulence flow was generated in a labora
 tory. The turbulence intensity of the flow is controlled via impeller rota
 tion speed. The mean strain rate is made by two circular plates moving tow
 ard each other in the center of the measuring area by an actuator. The dyn
 amics of the particles are measured using high-speed Lagrangian Particle T
 racking at 10,000 frames per second. Measured data from the experiment wer
 e employed to design a gated recurrent unit model. Two powerful parallel c
 omputing machines, JUWELS and DEEP-EST, were employed to implement the mod
 el. The velocity forecasting with a gated recurrent network presents a con
 siderable outcome. The computing machine's scalability using GPUs accelera
 tes this model's computing time significantly, which strengthens the abili
 ty to predict turbulent flow.\n\nRegistration Category: Tech Program Reg P
 ass, Exhibits Reg Pass
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