基于代理模型的高效全局气动优化设计方法研究进展

Aerodynamic shape optimization based on high-fidelity computational fluid dynamics plays an increasingly important role in improving aerodynamic and overall performance of an aircraft. Surrogate-Based Optimization (SBO), a genetic efficient global optimization, has become a hot topic in this area. During the past two decades, a great progress has been made. Various advanced new surrogate modelling techniques have been proposed, and optimization theory and algorithm are constantly improved. In this article, recent progress of efficient global aerodynamic shape optimization using SBO is reviewed. First, the state of the art of optimizations with variable-fidelity surrogate models, gradient-enhanced models, and a parallel optimization method based on none-bio-inspired evolutionary mechanism are reviewed. Second, in terms of frontier issues, recent progress of multi-objective design optimization, hybrid inverse/optimization design method, robust design optimization, as well as multidisciplinary design optimization are discussed. Literature review shows that SBO has significant superiority in efficiency, robustness, and global search. In addition, it enables efficient aerodynamic shape optimizations with number of design variables up to 100, showing huge potential in engineering applications. Finally, some key issues and challenges relevant to the theory, method, and applications of SBO are presented, and future research directions are suggested.