Design of an RBF Surrogate Model for Low Reynolds Number Airfoil Based on Transfer Learning

Computational fluid dynamics (CFD) simulations can be used to generate accurate airfoil surrogate models, while the XFOIL program is very rapid. Combining these advantages, a radial basis function (RBF) surrogate model based on transfer learning is proposed in this paper. This model can effectively replace flow field calculations and improves the model generation speed. The model was validated in this study using iSIGTH optimization software. This software provides fast modeling and optimized aerodynamic performance of a low Reynolds number airfoil surrogate model under multi-design point conditions.