FVM Network to Reduce Computational Cost of CFD Simulation

Despite the rapid growth of CPU performance, the computational cost to simulate the chemically reacting flow is still infeasible in many cases. There are few studies to accelerate the CFD simulation by using neural network models. However, they noted that it is still difficult to predict multi-step CFD time series data. The finite volume method (FVM) which is the basic principle of most CFD codes seems not to be sufficiently considered in the previous network models. In this study, a FVM network (FVMN) which simulate the principles of FVM by the tier-input and derivative-output system was proposed. The performance of this baseline model was evaluated using unsteady reacting flow datasets. It was confirmed that the maximum relative error of the FVMN (0.04%) was much smaller than the general model (1.12%) in the training dataset. This difference in error size was more prominent in the prediction datasets. In addition, it was observed that the calculation speed was about 10 times faster in FVMN than CFD solver even under the same CPU condition. Although the relative error with the ground truth data was significantly reduced in the proposed model, the linearly increasing gradient error is a remaining issue in longer transient calculations. Therefore, we additionally suggested Machine learning aided CFD framework which can substantially accelerate the CFD simulation through alternating computations.