CAREER: Predictive Analysis of Stability-Critical Structures: an Uncertainty-Informed Path from Measurements to Theory

职业：稳定性关键结构的预测分析：从测量到理论的不确定性路径

• 批准号: 1351742
• 负责人: Mazdak Tootkaboni
• 金额: $ 40万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351742&HistoricalAwards=false
• 关键词: CAREER; Predictive; Analysis; Stability; Critical

The underlying theme of this Faculty Early Career Development (CAREER) Program award is the development of a unified predictive analysis framework that will significantly improves the state of analysis-based design for stability-critical structures. The framework crosses disciplinary boundaries by bringing together data mining, information theory and statistical inference techniques from data sciences and high fidelity stochastic nonlinear solvers that are based on embedded nonlinear predictors into incremental-iterative path following techniques from computational sciences. Thin-wall structural components such as cold-formed steel members and thin wall cylindrical structures are extremely sensitive to material and fabrication imperfections because they fail in buckling mode when subjected to compressive stresses. The buckling/stability failure mode makes it difficult to predict collapse loads. The challenge in devising predictive analysis framework for components is that a slight deviation from perfection dramatically affects their response to loads. The result is a large scatter in results especially when the response up to collapse load is of interest. Realistic input models for observable and unobservable uncertainties that are adaptable to both global and local scales as well as to stochastic and deterministic methods will be a unique feature of the proposed framework. The goal is to develop analytical non-linear computational model that includes all variables, to integrate research in to curriculum and to provide outreach to community college and high school students. The computational model will be validated with experimental data that is already available.The methodologies developed in this project will advance the state of the art in computational modeling of stability-critical structures and will create a leap towards moderating large safety factors involved in designing these structures. A new course in probabilistic methods in structural stability will be developed for the PhD curriculum.

这项教师早期职业发展(Career)计划奖的基本主题是开发一个统一的预测分析框架，该框架将显著改善基于分析的稳定关键结构设计的状况。该框架跨越了学科界限，将数据科学的数据挖掘、信息论和统计推理技术以及高保真随机非线性求解器结合在一起，这些求解器基于嵌入到计算科学的增量迭代路径跟踪技术中的非线性预测器。冷弯薄壁型钢构件和薄壁圆筒结构等薄壁构件在受压应力作用时发生屈曲破坏，对材料和制造缺陷极为敏感。屈曲/稳定破坏模式使倒塌载荷的预测变得困难。为部件设计预测分析框架的挑战在于，与完美的微小偏差会显著影响它们对负载的响应。其结果是结果有很大的分散，特别是当需要考虑崩溃荷载的响应时。可观测和不可观测不确定性的实际输入模型可适应全球和局部尺度以及随机和确定性方法，这将是拟议框架的一个独特特点。其目标是开发包含所有变量的分析性非线性计算模型，将研究整合到课程中，并向社区大学和高中生提供推广。计算模型将用已有的实验数据进行验证。本项目中开发的方法将推动稳定性关键结构的计算建模的最新水平，并将朝着缓和设计这些结构所涉及的大安全系数的方向迈进。将为博士课程开发一门关于结构稳定的概率方法的新课程。