面向弹道性能提升需求的高性能机动飞行器多学科优化设计

Multi-disciplines Optimization Design of High-performance Aircraft for the Trajectory Performance Enhancement

摘要

针对高性能机动飞行器在气动、弹道、制导与控制等多学科间耦合紧密、迭代频繁、流程复杂的问题,提出了一种基于高精度代理模型与改进智能算法的系统级多学科协同优化设计方法。首先,利用正交试验设计与CFD数值模拟获取高保真样本数据,构建了基于Kriging方法的气动拟合系数代理模型,实现了由几何外形参数到气动特性的高精度快速映射,有效解决了多学科迭代中气动解算耗时长的难题。在此基础上,统筹考虑制导精度及姿态稳定性需求,建立了涵盖气动外形、制导律参数及控制增益的系统级多目标优化模型。针对传统优化算法易陷入局部最优的问题,引入浓度调节机制、平衡算子与遗传进化算子改进了粒子群算法。全系统仿真结果表明,该方法有效解决了学科间的强耦合难题,优化后的飞行器平均气动阻力系数降低约8%,静稳定裕度提升至5.41%,控制系统相位裕度达65.23°,对高超声速机动目标的终端制导精度由1.40m收敛至0.49m,显著提升了飞行器的弹道综合性能。

Abstract

To address the design challenges of high-performance maneuvering vehicles, characterized by tight coupling, frequent iterations, and complex processes across disciplines such as aerodynamics, ballistics, guidance and control, a system-level multidisciplinary design optimization method based on high-precision surrogate models and an improved intelligent algorithm was proposed. Firstly, by utilizing orthogonal experimental design and computational fluid dynamics simulation to acquire high-fidelity sample data, a Kriging-based surrogate model for aerodynamic fitting coefficients was constructed. Rapid and high-precision mapping from geometric parameters to aerodynamic characteristics can be achieved. The issue of excessive computational cost in aerodynamic analysis during the iterative process was effectively resolved. On this basis, a system-level multi-objective optimization model encompassing aerodynamic shape, guidance law parameters, and control gains was established by comprehensively considering the requirements for guidance accuracy and attitude stability. To overcome the tendency of traditional algorithms to fall into local optima, an improved particle swarm optimization algorithm, which incorporates a concentration regulation mechanism, an equilibrium operator, and genetic evolution operators, was employed to perform global optimization. Full-system simulations demonstrate that the proposed method effectively addresses strong interdisciplinary coupling challenges. The optimized vehicle achieves an approximate 8% reduction in the average aerodynamic drag coefficient, an increase in the static stability margin to 5.41%, and a control system phase margin of 65.23°. Furthermore, the terminal guidance accuracy against hypersonic maneuvering targets improves from 1.40m to 0.49m, significantly enhancing the comprehensive ballistic performance of the vehicle.

关键词

多学科优化 / 改进粒子群算法 / 气动外形优化 / 制导控制一体化

Key words

multidisciplinary design optimization / improved particle swarm optimization / aerodynamic shape optimization / integrated guidance and control

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基金资助

JCKY202204B001