Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows

TL;DR

This paper introduces a finite volume method-based neural network architecture with a physics-informed loss function to accelerate unsteady CFD simulations, achieving higher accuracy and 10x speedup in reactive and non-reactive flows.

Contribution

A novel FVM-inspired neural network architecture with physics-informed loss that improves prediction accuracy and computational speed for unsteady CFD simulations.

Findings

FVM-based network outperforms previous MLP models in accuracy.

Physics-informed loss reduces non-physical overfitting.

Network achieves about 10 times faster computation than traditional CFD.

Abstract

Despite rapid improvements in the performance of central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cases. The application of the convolutional neural networks (CNNs) specialized in image processing in flow field prediction has been studied, but the need to develop a neural netweork design fitted for CFD is recently emerged. In this study, a neural network model introducing the finite volume method (FVM) with a unique network architecture and physics-informed loss function was developed to accelerate CFD simulations. The developed network model, considering the nature of the CFD flow field where the identical governing equations are applied to all grids, can predict the future fields with only two previous fields unlike the CNNs requiring many field images (>10,000). The performance of this baseline model was evaluated using CFD time series data from non-reacting flow and reacting flow simulation; counterflow and hydrogen flame with 20 detailed chemistries. Consequently, we demonstrated that (1) the FVM-based network architecture provided improved accuracy of multistep time series prediction compared to the previous MLP model (2) the physic-informed loss function prevented non-physical overfitting problem and ultimately reduced the error in time series prediction (3) observing the calculated residuals in an unsupervised manner could indirectly estimate the network accuracy. Additionally, under the reacting flow dataset, the computational speed of this network model was measured to be about 10 times faster than that of the CFD solver.