Aerodynamic Optimization of Box-Wing Planform Through Machine Learning Integration

This study discusses a machine learning-driven methodology for optimizing the aerodynamic performance of both conventional, like common research model（CRM）, and non-conventional, like Bionica box-wing, aircraft configurations. The approach leverages advanced parameterization techniques, such as class and shape transformation（CST） and Bezier curves, to reduce design complexity while preserving flexibility. Computational fluid dynamics（CFD） simulations are performed to generate a comprehensive dataset, which is used to train an extreme gradient boosting（XGBoost） model for predicting aerodynamic performance. The optimization process, using the non-dominated sorting genetic algorithm（NSGA-Ⅱ）, results in a 12.3% reduction in drag for the CRM wing and an 18% improvement in the lift-to-drag ratio for the Bionica box-wing. These findings validate the efficacy of machine learning based method in aerodynamic optimization, demonstrating significant efficiency gains across both configurations.