Evaluating the Effectiveness of the Convolutional LSTM Neural Network for Simulations in Computational Fluid Dynamics

Abstract

Computational Fluid Dynamics (CFD) is an important part of engineering design, with applications in diverse areas. Although its practical application is widespread, the computational cost hinders its utilization. This research evaluates the effectiveness of the Convolutional LSTM (ConvLSTM) neural network for Computational Fluid Dynamics simulations when creating Reduce Order Models (ROM) and simulating turbulent fluid flows interacting with an obstacle. We propose a novel end-to-end Artificial Neural Network (ANN) model architecture based entirely on ConvLSTM that can successfully predict the spatiotemporal evolution of a fluid flow. This data-driven approach achieves similar results to a classical CFD method with direct numerical simulation with a Mean Squared Error of 1.107 Ã 10−05 in a quarter of its execution time. This model could be used to accelerate CFD simulations, leading to a faster engineering development process. By providing rapid preliminary results for prototype testing, engineers can explore more design ideas without waiting days or weeks for simulation results.