Reliability-Based Design Optimization of Large-Scale Complex Systems

大型复杂系统基于可靠性的设计优化

• 批准号: 0969044
• 负责人: Sharif Rahman
• 金额: $ 27.23万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0969044&HistoricalAwards=false
• 关键词: Reliability; Based; Design; Optimization; Large

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.

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）创造新的理论基础和数值算法。所建议的工作将涉及：(1)用于一般高维随机系统的系统可靠性分析的扩展多项式维数分解（X-PDD）方法；(2)结合X-PDD和score函数，并行计算系统可靠性对设计变量的敏感性；(3)基于系统级可靠性的稳健设计优化算法，该算法采用X-PDD对单个随机模拟的概率响应和灵敏度进行优化。系统可靠性分析和设计灵敏度的创新表述将极大地加快RBDO进程。因此，该研究为解决存在不确定性的大规模复杂优化问题提供了一个新的、可能是范式转移的进展。交付成果包括新理论和算法的开发，建模和仿真工具的生成，研究成果的文档，工程学生教育以及K-12学生的动手设计经验。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用于提高耐久性和耐撞性的地面车辆设计，用于民用和航空航天应用的抗疲劳和抗断裂设计，以及恶劣环境下微电子封装的可靠设计。除了工程领域，潜在的应用领域还包括能源、金融、管理、调度、运输和物流，其中随机优化发挥着至关重要的作用。该奖项创造的知识转移将通过组织工程设计相关的专题讨论会、同行评审的期刊出版物、在主要会议上的演讲、软件开发和学生教育来实现。教育目标包括从未被充分代表的少数群体中招收研究生，实施软件工具来升级爱荷华大学现有的研究生和本科课程，并积极参与爱荷华州面向初高中学生的“项目引领计划”。