<p>Computational fluid dynamics (CFD) simulation is usually a computationally expensive, memory demanding, and time-consuming iterative process. These drawbacks limit the use of CFD, especially when either spatiotemporal scales or geometry complexity increases. This paper presents the preliminary results from the assessment of approximation models for predicting non-uniform steady turbulent flows in a 3D domain, utilizing deep learning (DL) algorithms. In particular, three different algorithms of artificial neural network (ANN), recurrent neural networks (RNN) long-term short-term memory network (LSTM) with/out autoencoder were utilized. The desired features were obtained from a validated CFD model to link multi-variable input spaces (e.g. input speed and direction, geometry configuration) with multi-variable output space (e.g. velocity magnitude, pressure gradient). It resulted in obtaining an efficient and accurate approximation of the entire velocity field for given input flow field characteristics. The results presented the performance of the utilized models. This integrated approach can provide immediate feedback for real-time design iterations for the entire computational domain at the early stages of design. Hence, designers and engineers can easily generate immense amounts of design alternatives without facing the time-consuming task of evaluation and selection.</p><p>Keywords: Deep Learning, Artificial Neural Networks, Recurrent Neural Networks, LSTM, Computational Fluid Dynamics, Turbulent Flow</p>

Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks

Paper Proceedings: https://asmedigitalcollection.asme.org/POWER/POWER2021/volume/85109

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