基于主动学习PC-Kriging模型的复杂结构可靠性分析方法  

Reliability Analysis Method of Complex Structures Based on Active Learning PC-Kriging Model

作　　者：陈吉清[1,2] 张钰奇 兰凤崇[1,2] 周云郊[1,2] 王俊峰 Chen Jiqing;Zhang Yuqi;Lan Fengchong;Zhou Yunjiao;Wang Junfeng(School of Mechanical&Automotive Engineering,South China University of Technology,Guangzhou 510640;Guangdong Province Key Laboratory of Automotive,Guangzhou 510640)

机构地区：[1]华南理工大学机械与汽车工程学院,广州510640 [2]广东省汽车工程重点实验室,广州510640

出　　处：《汽车工程》2025年第2期383-390,共8页Automotive Engineering

基　　金：广东省自然科学基金(2021A15150912);广州市科技计划项目(202007020007)资助。

摘　　要：对于复杂结构可靠性设计中多维设计变量和隐式非线性响应的问题,构造准确的代理模型是一种有效的解决方法。然而,基于预设样本量的试验设计来构建代理模型,可能面临效率低下或准确性不足的挑战。为此,提出一种主动学习PC-Kriging模型的可靠性分析方法,结合多项式混沌展开增强全局近似精度以及Kriging捕捉局部特征的优点,利用主动学习策略,自适应地选择最佳样本点,最大程度减少训练样本量,即减少结构性能分析的计算成本,提高分析效率。进一步构建主动学习PC-Kriging模型驱动的多软件协同设计框架,对前、后处理软件进行二次开发,实现参数化建模、性能分析和后处理的无缝连接,形成一套自动化分析流程。最后,以电池包结构为例进行可靠性分析,验证本文方法的高效性和准确性。Constructing accurate surrogate models is an effective solution to addressing the problem of multi-dimensional design variables and implicit nonlinear responses in the reliability design of complex structures.However,using experiment design based on a predetermined sample size to construct surrogate models may face challenges of inefficiency or insufficient accuracy.Therefore,an active learning PC-Kriging model for reliability analysis is proposed,which combines the advantages of Polynomial Chaos Expansion for enhancing global approxi-mation accuracy and Kriging for capturing local features.The active learning strategy is utilized to adaptively select the optimal sample points to minimize the training sample size,reducing computational cost of structural perfor-mance analysis,and improving analysis efficiency.Further,an active learning PC-Kriging model-driven multi-soft-ware co-design framework is constructed.Secondary development of pre-processing and post-processing software is conducted to enable seamless integration of parametric modeling,performance analysis,and post-processing,form-ing a comprehensive automated analysis workflow.Finally,reliability analysis is performed using a battery pack structure as a case study to verify the efficiency and accuracy of the proposed method.

关 键 词：结构可靠性分析 主动学习 代理模型 PC-Kriging 多软件协同 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U469.72[自动化与计算机技术—控制科学与工程] TB114.3[机械工程—车辆工程]