MLP-residual surrogate model for aerodynamic prediction in projectile external flows

Accurate and rapid aerodynamic prediction is essential for projectile trajectory simulation and digital twin driven exterior ballistic systems, where conventional CFD methods are computationally expensive and difficult to deploy in real-time applications. To address this challenge, this paper proposes a primary-residual dual-stage surrogate modeling method based on a multilayer perceptron (MLP). A primary prediction model for aerodynamic parameters is constructed using CFD simulation data, where Mach number and angle of attack are used as input variables to establish the global nonlinear aerodynamic mapping. By introducing a residual learning mechanism, a compensation model is established to address fitting deviations in highly nonlinear regions, thereby correcting the nonlinear biases of the primary model predictions. The residual network further captures localized nonlinear discrepancies not fully resolved by the primary model, significantly improving prediction accuracy in high-Mach-number and large-angle-of-attack regions. Comparative validation against polynomial fitting, RBF, and single-stage MLP models shows that the proposed method achieves the best overall performance. For example, the RMSE is reduced from 2.91 to 0.87 for lift prediction and from 0.634 to 0.086 for pitching moment prediction. Overall, the proposed model reduces the average RMSE by approximately 50% across all aerodynamic parameters, demonstrating stronger robustness, improved generalization capability, and practical engineering applicability for digital twin based projectile exterior ballistic prediction.