WAVERIDER FAIRING OPTIMIZATION INCORPORATING MULTI-SOURCE MODEL TIME COST

To address the common challenges of high computational resource consumption and uncontrolled convergence time in optimization problems, this paper proposes a multi-fidelity surrogate model optimization method that considers the time cost of computational models. By incorporating the time cost into the optimization sampling process and dynamically adjusting model fidelity, the method ensures convergence to a better solution within a predetermined time frame. This approach meets the practical engineering requirement of obtaining an optimal design within limited time constraints. The effectiveness of the proposed method is validated through various numerical test cases. Taking the enhancement of the wide-speed-range flight performance of a waverider as a practical example, this study applies the proposed optimization approach to the wide-speed-range optimization of the waverider aft-body fairing through parameterization. The results indicate an improvement in the aerodynamic performance of the waverider at off-design conditions. Analysis of the lift and drag variations of the waverider aft-body reveals that the optimized fairing generates both lift enhancement and drag reduction effects at small angles of attack. As the angle of attack increases, the drag reduction effect decreases, while the lift enhancement remains relatively stable, demonstrating good compatibility with the waverider’s inherent lift characteristics. From an overall aerodynamic performance perspective, both the original and optimized fairings improve the waverider’s aerodynamic performance at supersonic (Ma = 2, 4) and hypersonic (Ma = 6, 8) speeds, with the optimized fairing exhibiting more significant improvements. Within the small angle-of-attack range, the overall lift-to-drag ratio increases by 5% ~ 25%. These findings confirm the effectiveness of aft-body fairing optimization in enhancing the wide-speed-range aerodynamic performance of the waverider and demonstrate the engineering applicability of the proposed optimization method.