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.

该教师早期职业发展（职业）计划奖的基本主题是开发统一的预测分析框架，该框架将显着改善稳定性关键结构的基于分析的设计状态。该框架跨越了学科界限，将数据科学中的数据挖掘、信息论和统计推断技术以及基于嵌入式非线性预测器的高保真随机非线性求解器结合到计算科学中的增量迭代路径跟踪技术中。冷弯型钢构件和薄壁圆柱形结构等薄壁结构部件对材料和制造缺陷极其敏感，因为它们在受到压应力时会以屈曲模式失效。屈曲/稳定性失效模式使得预测塌陷载荷变得困难。设计组件预测分析框架的挑战在于，与完美的轻微偏差会极大地影响它们对负载的响应。结果是结果出现很大的分散，特别是当对塌陷载荷的响应感兴趣时。适用于全球和局部尺度以及随机和确定性方法的可观察和不可观察不确定性的现实输入模型将是所提出框架的独特特征。目标是开发包含所有变量的分析非线性计算模型，将研究整合到课程中，并为社区大学和高中生提供服务。该计算模型将使用现有的实验数据进行验证。该项目开发的方法将推进稳定性关键结构的计算建模的最新技术，并将在调节这些结构设计中涉及的大安全因素方面实现飞跃。将为博士课程开发一门结构稳定性概率方法的新课程。