Traditional fine models are often inefficient in optimizing the dynamics of complex structural systems such as large liquid rocket engines. To enhance the design efficiency, this paper proposes a fast optimization method based on finite element modeling, central Latin hypercube experimental design with minimax criteria, hierarchical Kriging surrogate model, and combined multi-island genetic algorithm + nonlinear sequential quadratic programming approach. First, a low-frequency dynamic fine finite element model of the entire engine structure was established, and the sampling points were obtained by considering the experimental design and a hierarchical Kriging model that describes the relationship between the design variables of the engine structure parameters and the modal frequency characteristics. Next, the height of the frame, size of the gimbal mount, and stiffness and installation angle of the servo mechanism were considered as design variables. To satisfy the design requirements in terms of the first modal frequency of the engine structure and realize the optimal design of the modal characteristics, the global optimization of the high-precision Kriging model was performed in the design space by using the hybrid optimization algorithm. Finally, the optimization results were substituted into the finite element model to perform the modal analysis, and modal tests were conducted to verify the effectiveness of the optimization method in realizing large-scale structural system optimization. The results indicated that the first modal frequency of the engine structure was increased from 7.53 Hz to 11.50 Hz and the optimization results could satisfy the requirements for the dynamics design. The proposed optimization design method can considerably improve the optimization efficiency by ensuring high accuracy of the results. The presented modeling strategies and optimization concepts can be applied to several engineering applications and provide novel solutions for the dynamic optimization problems of large and complex structures.

传统的精细模型通常在优化复杂结构系统（例如大型液体火箭发动机）的动力学方面效率低下。为了提高设计效率，本文提出了一种基于有限元建模，基于minimax准则的中央拉丁超立方体实验设计，分层Kriging替代模型以及组合的多岛遗传算法+非线性顺序二次规划方法的快速优化方法。首先，建立整个发动机结构的低频动态精细有限元模型，并通过考虑实验设计和描述发动机结构参数的设计变量与发动机设计参数之间关系的分层克里格模型获得采样点。模态频率特性。接下来，框架的高度 万向支架的尺寸，伺服机构的刚度和安装角度均视为设计变量。为了满足发动机结构的第一模态频率的设计要求并实现模态特性的优化设计，使用混合优化算法在设计空间中对高精度Kriging模型进行了全局优化。最后，将优化结果代入有限元模型中进行模态分析，并进行模态试验，验证了该优化方法在实现大型结构系统优化中的有效性。结果表明，发动机结构的第一模态频率从7.53 Hz增加到11。50 Hz，优化结果可以满足动力学设计要求。通过确保结果的高精度，所提出的优化设计方法可以大大提高优化效率。提出的建模策略和优化概念可以应用于多种工程应用，并为大型和复杂结构的动态优化问题提供新颖的解决方案。