  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.
