An intelligent multi-objective robust design optimization framework based on PathLine Transformation and surrogate-assisted sampling for uncertainty-aware airfoil design

This study introduces an intelligent multi-objective robust design optimization (MORDO) framework designed to overcome the computational bottlenecks of uncertainty-aware aerodynamic design. The core innovation lies in the PathLine Transformation (PaLiTra)—a novel adaptive sampling architecture that performs a structural audit of the uncertainty space to maximize information gain. Unlike conventional sampling methods that suffer from data redundancy, PaLiTra adaptively identifies optimal transformation directions to refine surrogate accuracy with minimal high-fidelity evaluations. The framework integrates Kriging-based surrogates with the Expected Hypervolume Improvement (EHVI) criterion to efficiently discover the robust Pareto front, balancing the maximization of mean aerodynamic efficiency ( C l / C d ) with the minimization of performance sensitivity. Validation through the Rosenbrock benchmark and a cantilever beam problem demonstrates that the proposed framework achieves high-fidelity statistical stability ( R 2 ≥ 0.99) at a fraction of the computational cost required by traditional Monte Carlo Simulations (MCS). Application to Unmanned Aerial Vehicle (UAV) airfoil design further confirms that the framework identifies diverse, high-quality robust solutions under operational fluctuations where standard deterministic designs fail. The results establish PaLiTra as a highly efficient and scalable tool for complex engineering problems where exhaustive sampling is computationally prohibitive.