Discuss on approximate optimization strategies using design of computer experiments and metamodels for flight vehicle design

Although the wide use of high fidelity analysis models in modern flight vehicle design is beneficial to improving the design credibility and overall performance of flight vehicle systems, it also causes high computational cost and complexity. In order to alleviate the computational difficulty, approximate optimization strategies using design of computer experiments(DoCE) and metamodels for flight vehicles have become more and more popular, which construct reasonable approximation models to enable efficient convergence to the optimal solution with much less computational burden and shorter design cycles. An extensive literature survey of the state-of-the-art of approximate optimization strategies in the context of flight vehicle design is provided. The definition, solution process, features and key technologies of approximate optimization strategies are presented, and then the development of DoCE, metamodeling, accuracy assessment and metamodel selection, as well as corresponding typical methodologies, are reviewed. Moreover, metamodel management and updating schemes and termination criteria used in both static and adaptive approximate optimization strategies are specifically discussed. The efficiency and convergence behaviors of approximate optimization strategies for solving multidisciplinary design optimization (MDO) problems are analyzed via comparison with decomposition-based strategies. A number of well-known numerical benchmark problems are employed to discuss the characteristics of the aforementioned key technologies. Furthermore, the overall performance and applicability of different approximate optimization strategies are discussed through flight vehicle design applications. Comparative studies demonstrate that approximate optimization strategies show obvious advantages in optimization efficiency, convergence and robustness, which are important for engineering applications. Future research directions of approximate optimization strategies are given.