Abstract: An aeroheating agent model based on the radial basis function neural network is proposed to rapidly acquire heat flux on the surface of hypersonic vehicles and shorten the aeroheating design cycle. A regularization radial basis function neural network is firstly constructed on each grid node of the solid surface, followed by the acquisition of the connection weights of different nerve cells through simultaneous training of all neural networks based on the data of the train set. Finally, the heat flux results of different positions on the surface of vehicles are predicted synergistically by the neural networks on grid nodes. The simulation results of the elliptical blunt vehicle designed by the Mars Science Laboratory of NASA indicate that the agent model can be employed to rapidly predict heat flux of hypersonic vehicles with good generalization capability. In addition, the results of heat flux on the stagnation point and the windward wall prove that the heat flux deviations between the agent model and numerical simulation are smaller than 10%.

Key words: neural networks, radial basis function, hypersonic, aeroheating, agent models