A Flow Field Super-resolution Strategy for Direct Numerical Simulation Based on Physics-informed Convolutional Neural Networks

Abstract

In the computational fluid dynamics method, the discretization of the solution domain has an important impact on the calculation results. The higher resolution grid improves the solution accuracy and is accompanied by a significant increase in the calculation time. How to improve efficiency under the premise of ensuring accuracy is of great significance in engineering. To this end, we propose a super-resolution strategy for direct numerical simulation (DNS): take the numerical simulation results at low-resolution grid as the initial solution, construct a model for super-resolution utilizing the convolutional neural networks, and embed the flow governing equations in the model to modify the initial solution. The proposed method is verified in the engineering case of pipeline transportation of non-Newtonian fluids. The results show that this strategy can improve the solution accuracy and shorten the simulation time. The deviation between the high-resolution results reconstructed by the model and the high-resolution flow field simulated by DNS is 63.18% lower than that of the low-resolution one simulated by DNS, and the calculation time is saved by 84.65%.