Collaborative Research: Efficient Probabilistic Approach Using Order Reduction and Hybrid Models -- A New Paradigm for Structural Dynamic Analysis

协作研究：使用降阶和混合模型的高效概率方法——结构动态分析的新范式

• 批准号: 0926803
• 负责人: Yu Ding
• 金额: $ 25万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0926803&HistoricalAwards=false
• 关键词: Collaborative; Research; Efficient; Probabilistic; Approach

The objective of this collaborative research between University of Connecticut and Texas A&M University is to develop a new probabilistic framework of dynamic analysis for handling large-scale structures under uncertainties. In particular, the research team aims at tackling a number of research challenges such as high computational cost and difficulty in characterizing model uncertainties, often encountered in this type of analysis. The approach in this research is built upon a hybrid modeling and analysis strategy, using both the computations and measurements of structural modal responses. It features the combination of structural dynamic model order-reduction, Bayesian response prediction (based on a set of viable candidate models), and recursive model updating and verification at multiple stages. To enable the probabilistic analysis of high dimensional systems, a highly efficient adaptive Markov Chain Monte Carlo solution procedure will be developed and embedded into the analysis framework. This framework allows us to evaluate simultaneously the structural uncertainties and their impact to vibration responses.If successful, the outcome of this research will lead to the concurrent advancements in both probabilistic structural analysis and Monte Carlo-based solution techniques. The information of structural uncertainties and their impact to vibration responses can be used to guide the robust design of structural products, and also be used to develop thresholds for vibration response-based online structural monitoring. The collaborative and interdisciplinary nature of the research will provide unique learning opportunities for students involved at all levels. The plan for dissemination will make results widely available, contributing to training analysts and engineers by presenting at professional conferences, publishing in refereed journals, and teaching at university-level courses.

康涅狄格大学和得克萨斯州A＆M大学之间的这项合作研究的目的是开发一种动态分析的新概率框架，用于处理不确定性下的大规模结构。 特别是，研究小组旨在应对许多研究挑战，例如高计算成本和表征模型不确定性的难度，这在这种类型的分析中经常遇到。 这项研究中的方法是基于混合建模和分析策略的，同时使用结构模态响应的计算和测量。 它具有结构性动态模型订购，贝叶斯响应预测（基于一组可行的候选模型）以及在多个阶段更新和验证的结合。 为了实现对高维系统的概率分析，将开发出高效的自适应马尔可夫链蒙特卡洛解决方案程序，并将其嵌入分析框架中。 该框架使我们能够同时评估结构不确定性及其对振动反应的影响。如果成功，这项研究的结果将导致概率结构分析和基于蒙特卡洛的解决方案技术的同时进步。 结构不确定性的信息及其对振动响应的影响可用于指导结构产品的强大设计，还用于开发阈值，以用于基于振动响应的在线结构监测。 研究的协作和跨学科性质将为各个级别的学生提供独特的学习机会。 传播计划将使结果广泛可用，从而通过在专业会议上展示，在裁判期刊上出版以及在大学级课程的教学来为培训分析师和工程师提供贡献。