基于多工况关联的无人车辆车身结构轻量化优化设计

Lightweight Optimization Design of Unmanned Vehicle Body Structure Based on Multi-working Conditions Correlation

未来战场的多样化对特种无人车辆的环境适应性提出了更高的要求。为满足特种无人车辆的多工况使用、高机动能力与低成本研制等要求,采用多工况关联设计与轻量化优化的思路对无人车桁架车身结构进行优化设计。考虑到多工况的车身结构设计变量多、设计空间大,面临仿真次数过多的问题,提出基于多工况关联的车身结构轻量化优化方法,利用设计变量区间缩减策略减小设计空间;引入高斯过程代理模型替换仿真分析实现结构设计方案性能的快速评估;结合遗传算法实现方案的优化。实验结果表明,最终优化方案在通过车身刚度强度和模态等多学科性能仿真验证的情况下,质量比初始方案降低14.12%,比只用高斯过程优化设计的方案降低8.87%。

The higher requirements are put forward for the environmental adaptability of special unmanned vehicles in the future battlefield.In order to meet the requirements of multi-working conditions,high maneuverability and low-cost development of special unmanned vehicles,the truss body structure of unmanned vehicles is optimized by the idea of multi-working conditions correlation design and lightweight optimization.However,considering that there are many design variables,large design space and too many simulation times in multi-working conditions,a lightweight optimization method of body structure based on multi-working conditions correlation is proposed;In the proposed method,the strategy of reducing design variables interval is used to reduce the design space.Gaussian process surrogate model is introduced to replace the simulation analysis for realizing the rapid performance evaluation of structural design scheme,and the optimization of the scheme is realized using genetic algorithm.The experimental results show that the mass of the final optimization scheme is 14.12%lower than that of the initial scheme and 8.87%lower than that of the scheme optimized only by Gaussian process under the condition of multi-disciplinary performance simulation verification,such as stiffness,strength and mode.