基于代理模型的汽车结构安全多目标优化的研究报告

Multi-Objective Optimization Method Based on Metamodel for Vehicle Structural Safety

提出基于自适应径向基函数的多目标优化方法。该方法通过遗传拉丁超立方实验设计、径向基函数和隔代映射遗传算法等技术,系统地评价代理模型。采用改进的贪婪算法挑选最后迭代步中的测试点到最终样本空间,获得整个设计域上的自适应径向基函数模型。该方法被应用于车身薄壁构件耐撞性多目标优化设计中,快速地找到了多组设计方案,较好地平衡了薄壁构碰撞过程中的吸能量和碰撞力。提出基于智能布点技术的微型多目标遗传算法。该算法采用加强径向基函数构建全局代理模型,再运用高效的微型多目标遗传算法进行近似优化。并根据优化结果信息进行智能布点,反馈到设计空间进而不断地更新代理模型,使实验设计过程和近似优化过程形成闭环的过程,提高了优化效率。该方法被应用于某重型商用车驾驶室动态特性优化中,获得大量支配优化前的设计方案使驾驶室动态特性更好并且质量更轻。提出基于信赖域模型管理的优化方法。该方法将在整个设计空间上的复杂优化问题,转化为一系列信赖域上的近似多目标优化问题。通过每个信赖域上的优化结果,确定信赖度和下代域的中心、半径。进而不断地缩放、平移信赖域,来保证获得与真实模型一致的非支配解。该方法被应用于某车门结构优化实际中,通过匹配关键部件的厚度,很好地平衡了车门的各项动静态特性指标。结合信赖域和智能布点技术,用来处理信赖域模型管理需要多次重采样导致效率低下的问题。通过样本遗传策略,遗传落在下代信赖域空间上的样本,减少实验设计样本个数从而提高效率。通过遗传智能布点策略,根据距离比较原则从非支配解外部解集中挑选部分到信赖域空间,提高关键区域代理模型的精度从而加快收敛。该方法被成功应用于基于耐撞性和模态特性的轿车车身结构轻量化设计中,解决了汽车结构安全中的多目标优化问题。

Most vehicle structural safety optimization problems involve multiple objectives, which cannot be expressed explicitly but acquired by complex computational model, and thus it increases the difficulty of solving multi- objective optimization problems. Intelligent optimization method is able to search for multiple optimal solutions in one single simulation run, but the low efficiency limits its application to complex vehicle structural crash problems. Common multi-objective optimization methods based on metamodel can well deal with the low efficiency and become a research focus, but the solution accuracy is usually low. Therefore, this project studies the multi- objective optimization methods based on metamodel, aims to improve the efficiency and accuracy in the design of vehicle crash safety. A new multi-objective optimization algorithm is proposed based on adaptive radial basis function. This method effectively assesses metamodel by using inherit Latin hypercube design, radial basis function and intergeneration projection genetic algorithm. The proposed method is applied to the thin-walled sections for structural crashworthiness, which is beneficial to quickly find multi-group design schemes and can well balance energy absorption and collision force. A micro multi-objective genetic algorithm based on intelligent sampling technology is put forward. The algorithm adopts the extented radial basis function to build a global metamodel, and then employs the efficient micro multi-objective genetic algorithm for approximate optimization. The method has been used in the dynamic characteristic optimization of a heavy commercial vehicle cab and obtains many optimal design schemes. Optimization algorithm based on trust region model management is proposed to solve the multi- objective optimization problem in complex engineering. The method transforms the complex optimization problems in the entire design space into a series of approximation problems in trust region. The method has been applied in a door structure optimization, and well balances the static and dynamic performance by matching the thickness of key components. Based on trust region and intelligent sampling technology, an efficient multi-objective method is developed. The method has been successfully used in the lightweight design of car body based on crashworthiness and modal characteristics, and demonstrates its ability to solve multi-objective optimization problems in vehicle structural safety.