Parallel Computing Accelerates Sequential Deep Networks Model in Turbulent Flow Forecasting
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DescriptionThis study aimed to employ deep learning capability and computing scalability to create a model and predict the velocity of the straining turbulence flow. The turbulence flow was generated in a laboratory. The turbulence intensity of the flow is controlled via impeller rotation speed. The mean strain rate is made by two circular plates moving toward each other in the center of the measuring area by an actuator. The dynamics of the particles are measured using high-speed Lagrangian Particle Tracking at 10,000 frames per second. Measured data from the experiment were employed to design a gated recurrent unit model. Two powerful parallel computing machines, JUWELS and DEEP-EST, were employed to implement the model. The velocity forecasting with a gated recurrent network presents a considerable outcome. The computing machine's scalability using GPUs accelerates this model's computing time significantly, which strengthens the ability to predict turbulent flow.
