A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods

In this work, we propose a deep-learning-based mapping model for simulating and predicting the flow field of Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) of propellers. The model employs image processing and computer vision methods to process the two-dimensional propeller RANS and LES simulation data. First, images are obtained by simulating the flow fields with the location data used to acquire a set of features specific to the corresponding positions. Second, the regression models for the flow fields and the mapping between the two different flow fields are established to predict the LES flow field at that position. Specifically, we utilize a deep convolutional neural network (CNN) for feature extraction from the flow field, which is then integrated with a nonlinear module for the purposes of regression and mapping. The effectiveness and accuracy of the proposed model in flow field prediction are demonstrated by its application to propeller RANS and LES simulations. It is shown that the overall error rate between the LES flow field predictions generated using this method and actual flow field data is 7.92%. Additionally, we also evaluate the model's generalization ability, stability, and robustness by testing it on the data of propeller flow fields at different Reynolds numbers. The results verify the applicability of the proposed model in various problems of flow field simulation and prediction.

Automated summary

In this work, a deep-learning-based mapping model is proposed for simulating and predicting the flow field of propellers using Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES). The model leverages image processing and computer vision techniques to process the two-dimensional propeller RANS and LES simulation data. By using a deep convolutional neural network (CNN) for feature extraction and a nonlinear module for regression and mapping, the proposed model demonstrates high accuracy and effectiveness in flow field prediction. The model's generalization ability, stability, and robustness are also evaluated and verified.