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）创建新的理论基础和数值算法。提出的工作将涉及：（1）用于对一般，高维，随机系统的系统可靠性分析的扩展多项式分解（X-PDD）方法； （2）集成X-PDD和得分功能，用于同时计算系统可靠性相对于设计变量的敏感性； （3）基于系统级可靠性和鲁棒设计优化算法，采用X-PDD来实现概率响应和敏感性，来自单个随机模拟。 系统可靠性分析和设计敏感性的创新表述将极大地加速RBDO过程。 因此，这项研究构成了在不确定性存在下解决大规模，复杂的优化问题的新范式转移的发展。 可交付成果包括开发新理论和算法，生成建模和仿真工具，研究结果文档，学生教育的文档以及针对K-12学生的动手设计经验。如果成功，这项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地面车辆设计，可提高耐用性和撞车性，用于民用和航空航天应用的疲劳和抗裂缝设计，以及在恶劣环境下的微电子包装的可靠设计。 除工程外，潜在的应用领域还包括能源，金融，管理，调度以及运输和物流，随机优化起着至关重要的作用。 该奖项创造的知识转移将通过组织与工程设计相关的研讨会组织，与同行评审的期刊出版物，大会和会议的演讲，软件开发和学生教育进行。 教育目标包括来自代表性不足的少数群体的研究生招聘，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，以及积极参与爱荷华州的项目领导项目领导中学和高中生的计划。