Abstract: Advances in the automatic post-processing analysis of aircraft airfoil CFD simulation results can effectively improve the efficiency of product design. Therefore, an intelligent partitioning method of airfoil surface flow field data is proposed, which can effectively obtain the airfoil surface flow field partitioning results. Firstly, the airfoil dataset is obtained by modifying the aerodynamic shapes in batch mode with parameterization, and numerical simulation is conducted to generate flow field calculation results. Secondly, the conformal ge-ometry method is adopted to reduce the dimension of the surface flow field data, and perform the resampling and matrixing process, so that the data can be used as the standard input of the prediction model. Thirdly, a convolutional neural network model is built up to train and predict the flow field data. Finally, the parti-tioning results are resampled to the airfoil surface by inverse mapping. Experiments show that the proposed intelligent partitioning method can efficiently partition the flow field data on the airfoil surface for different physical quantities, with an accuracy of more than 92% on the test data set.

Key words: flow field post-processing, conformal parameterization, conformal transformation, convolu-tional neural network, flow field partitioning