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In order to obtain the information about flow field, traditionalcomputational fluid dynamics methods need to solve the Navier-Stokes equationson the mesh with boundary conditions, which is a time-consuming task. In thiswork, a data-driven method based on convolutional neural network and multi-headperceptron is used to predict the incompressible laminar steady sparse flowfield around the airfoils. Firstly, we use convolutional neural network toextract the geometry parameters of the airfoil from the input gray scale image.Secondly, the extracted geometric parameters together with Reynolds number,angle of attack and flow field coordinates are used as the input of themulti-layer perceptron and the multi-head perceptron. The proposed multi-headneural network architecture can predict the aerodynamic coefficients of theairfoil in seconds. Furthermore, the experimental results show that for sparseflow field, multi-head perceptron can achieve better prediction results thanmulti-layer perceptron.

Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture