2022 · 14 citations · 0 references
ComputingComputational EngineeringComputational Fluid DynamicsFluid DynamicsMachine LearningDeep LearningTime Series ForecastingTime Series ModelingTime Series PredictionTurbulenceTurbulent FlowData-driven Turbulence ModelingDeep Learning For Time Series ForecastingDeep Learning-based ForecastingGated Recurrent Unit NetworksLagrangian Particle TrackingLong Short-term Memory NetworksRecurrent Neural Network ForecastingRecurrent Neural Networks
This study presents a deep learning (DL) neural network hybrid data-driven method that is able to predict turbulence flow velocity field. Recently many studies have reported the application of recurrent neural network (RNN) methods, particularly the Long short-term memory (LSTM) for sequential data. The airflow around the objects and wind speed are the most presented with different hybrid architecture. In some studies, the investigated data set in fluid dynamics were generated via known equations, and they have no random and chaotic behavior. Data series extracted from Computational Fluid Dynamics (CFD) have been used in many cases. This work aimed to determine a method with raw data that could be measured with devices in the airflow, wind tunnel, water flow in the river, wind speed and industry application to process in the DL model and predict the next time steps. This method suggests spatial-temporal data in time series, which matches the Lagrangian framework in fluid dynamics. Gated Recurrent Unit (GRU), the next generation of LSTM, has been employed to create a DL model and forecasting. Time series data source is from turbulence flow has been generated in a laboratory and extracted via 2D Lagrangian Particle Tracking (LPT). This data has been used for the training model and to validate the prediction in the suggested approach. The achievement via this method dictates a significant result and could be developed.