Aircraft aerodynamic performance prediction and inverse design based on a gated diffusion model

To address the convergence challenges faced by traditional deep learning models in the inverse design of three-dimensional aircraft under supersonic conditions, a generative gated Denoising Diffusion Probabilistic Model (DDPM) based on a priori prediction model guidance is proposed. This model integrates aerodynamic performance prediction and aircraft shape inverse design through the gating mechanism, aiming to enhance both prediction accuracy and design efficiency while overcoming the training difficulties encountered in handling highly nonlinear problems with traditional models. A Deep Neural Network (DNN) is constructed to serve as the a priori prediction model to obtain preliminary aerodynamic performance predictions and geometric shape estimates. Based on these initial results, the gated DDPM uses the predictive information from the DNN and employs diffusion and reverse diffusion processes to generate design outcomes. In this process, Gaussian noise is gradually added during the diffusion phase, and data distribution is restored in the reverse diffusion phase. This mechanism enhances both convergence and predictive accuracy in complex aerodynamic design tasks. The effectiveness of the gated DDPM is validated using an axisymmetric aircraft dataset. Compared to the prior model, the gated DDPM can control the relative error of most aerodynamic parameters within 0.75% in inverse design tasks, significantly outperforming the DNN model. The results demonstrate that the proposed method can effectively enhance the accuracy of aerodynamic performance prediction and shape inverse design in aircraft design.