考虑性能及成本的固体火箭发动机多学科设计优化

Multidisciplinary Design Optimization for Solid Rocket Motor Considering Performance and Cost

关键词:  多目标优化  多学科设计优化  代理模型  固体火箭发动机  多学科分析

Key words:  Multi-objective optimization  Multidisciplinary design optimization  Surrogate  Solid rocket motor  Multidisciplinary analysis

摘要:

为综合考虑固体火箭发动机燃烧室、药柱、喷管等部件成本与内弹道性能的相互影响，梳理其耦合关系，并建立了以总冲最大、成本最小为优化目标的固体火箭发动机多学科设计优化（MDO）模型。为降低MDO问题的计算成本，提出一种基于Kriging代理模型的多目标自适应优化方法（KRG-MAOM）。优化过程中，分别对目标与约束构建Kriging模型，并采用多目标优化算法求解，在伪Pareto解中综合考虑支配关系与分布特性选取新增样本点，引导优化快速收敛。算例结果表明，KRG-MAOM算法在全局收敛性与优化效率方面具有显著优势。最后，采用KRG-MAOM算法求解该MDO问题，得到可行的Pareto解集方案，与初始方案相比，同性能情况下成本节省约3.36%；同成本情况下性能提升约10.93%，从而验证了MDO模型合理性与KRG-MAOM算法有效性。

Abstract:

A solid rocket motor multidisciplinary design and optimization （MDO） model is constructed by considering coupling relationship between cost of combustor， grain， and nozzle and internal ballistic performance to maximize the total impulse and minimize the cost simultaneously. Additionally， a Kriging-based multi-objective adaptive optimization method （KRG-MAOM） is developed to alleviate the computational cost of solid rocket motor MDO problem. In KRG-MAOM， Kriging surrogates are constructed for both objectives and constraints and combined with a multi-objective optimization algorithm to obtain a pseudo Pareto set. The newly-added sample points are then selected from the pseudo Pareto set considering dominance relationship and distribution characteristics to improve the optimization convergence speed. The results of benchmarks illustrate that KRG-MAOM outperforms the competitive algorithms in terms of global convergence and optimization efficiency. Finally， KRG-MAOM is employed to solve the solid rocket motor MDO problem. The Pareto solutions satisfy all the disciplinary constraints. Compared to the initial solution， the cost can be saved by 3.36% at the same performance； the performance can be improved by 10.93% at the same cost， which demonstrates the reasonability of the constructed MDO model and the practicality of KRG-MAOM.