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一个统一的预测分析框架，该框架将大大改善基于稳定性结构的基于分析的设计。该框架通过将数据科学的数据挖掘，信息理论和统计推理技术汇总到来自数据科学的高忠诚定位非线性求解器中，从而跨越了纪律界限，这些技术是基于嵌入式非线性预测变量的高忠诚性非线性求解器，将其从计算科学的技术中添加到增量级别的路径中。薄壁的结构成分，例如冷形成钢构件和薄壁圆柱结构对材料和制造缺陷非常敏感，因为它们在受到压缩应力时会在屈曲模式下失败。屈曲/稳定性故障模式使得难以预测塌陷载荷。为组件设计预测分析框架的挑战在于，与完美的略有偏差极大地影响了它们对负载的反应。结果是结果的散射很大，尤其是当对崩溃负荷的响应引起人们的关注时。现实的输入模型，用于适应全局和本地尺度以及随机和确定性方法的可观察和不可观察的不确定性，将是所提出的框架的独特功能。目的是开发包括所有变量的分析非线性计算模型，将研究集成到课程中，并向社区大学和高中生提供宣传。计算模型将通过已经可用的实验数据进行验证。该项目中开发的方法将推进稳定性 - 关键性结构的计算建模中的最新技术，并将迈向调节设计这些结构所涉及的大型安全因素。将为博士课程开发结构稳定性概率方法的新课程。