Reliability-Based Design Optimization of Large-Scale Complex Systems

该奖项的研究目标是为复杂工程系统的大规模基于可靠性的设计优化（RBDO）创建新的理论基础和数值算法。（1）一般高维随机系统可靠性分析的扩展多项式维数分解（X-PDD）方法：（2）集成X-PDD和评分函数，并行计算系统可靠性对设计变量的灵敏度;（3）基于可靠性的系统级鲁棒设计优化算法，该算法采用X-PDD，从单次随机模拟中获得概率响应和灵敏度。 系统可靠性分析和设计灵敏度的创新公式将大大加快RBDO过程。 因此，这项研究构成了一个新的和可能的范式转移的进步，解决大规模的，复杂的优化问题的存在的不确定性。 成果包括开发新的理论和算法，生成建模和仿真工具，记录研究成果，工程专业学生教育，以及K-12学生的实践设计经验。如果成功，本研究的结果将适用于广泛的多学科设计优化方法。 潜在的工程应用包括改进耐久性和耐撞性的地面车辆设计，民用和航空航天应用的抗疲劳和耐磨损设计，以及恶劣环境下微电子封装的可靠设计。 除了工程，潜在的应用领域包括能源，金融，管理，调度，运输和物流，其中随机优化起着至关重要的作用。 该奖项所创造的知识转移将通过组织工程设计相关的研讨会，同行评审的期刊出版物，在主要会议和会议上的演讲，软件开发和学生教育来进行。 教育目标包括从代表性不足的少数群体招收研究生，实施软件工具，以升级现有的研究生和本科课程在爱荷华州的大学，并积极参与爱荷华州的项目为初中和高中学生带路计划。

The research objective of this award is to create new theoretical foundations and numerical algorithms for large-scale, reliability-based design optimization (RBDO) of complex engineering systems. The proposed effort will involve: (1) an extended polynomial dimensional decomposition (X-PDD) method for system reliability analysis of a general, high-dimensional, stochastic system; (2) integrated X-PDD and score functions for concurrently calculating sensitivities of system reliability with respect to design variables; and (3) system-level reliability-based and robust design optimization algorithms employing X-PDD for probabilistic response and sensitivities from a single stochastic simulation. The innovative formulation of system reliability analysis and design sensitivities will dramatically accelerate RBDO process. Therefore, the research constitutes a new and possibly paradigm-shifting advance towards solving large-scale, complex optimization problems in the presence of uncertainty. Deliverables include development of new theory and algorithms, generation of modeling and simulation tools, documentation of research results, engineering student education, and hands-on design experience for K-12 students.If successful, the results of this research will be applicable to a broad multidisciplinary design optimization methodology. Potential engineering applications comprise ground vehicle design for improved durability and crashworthiness, fatigue- and fracture-resistant design for civil and aerospace applications, and reliable design of microelectronic packaging under harsh environments. Beyond engineering, potential application areas include energy, finance, management, scheduling, and transportation and logistics, where stochastic optimization plays a vital role. The transfer of knowledge created by this award will take place through organization of engineering design-related symposia, peer-reviewed journal publications, presentations at major conferences and meetings, software development, and student education. Educational goals include graduate student recruitment from an underrepresented minority group, implementation of software tools to upgrade existing graduate and undergraduate courses at The University of Iowa, and active participation in Iowa's Project Lead the Way program for middle- and high-school students.