Rapid Analysis of Cylindrical Bypass Flow Field Based on Deep Learning Model

Abstract

Deep learning models provide a novel research perspective for hydraulic machinery and fluid dynamics mechanism research. Traditional computational fluid dynamics requires a lot of computational resource and calculation time, while deep learning models can effectively solve this problem. In this paper, a deep learning model is proposed for the rapid flow field analysis of a two-dimensional cylindrical bypass flow, and the errors of the prediction results are analyzed, so as to verify the feasibility of deep learning for accelerating the numerical simulation process. On this basis, the influence of different network structures on the prediction performance of the deep learning model is explored, and the optimal structural parameters of the neural network are found, indicating that it will achieve real-time prediction of the flow field performance, and save considerable computational resource and calculation time. The research in this paper is of great significance for the application about the rapid analysis of hydraulic machinery fluid dynamics based on deep learning models.