Collaborative Research: Optimal Design of Flaw-tolerant Structures and Material Microarchitectures via Stochastic Topology Optimization

合作研究：通过随机拓扑优化进行容错结构和材料微体系结构的优化设计

• 批准号: 1401575
• 负责人: Mazdak Tootkaboni
• 金额: $ 18.79万
• 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: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1401575&HistoricalAwards=false
• 关键词: Collaborative; Research; Optimal; Design; Flaw

Topology optimization is a computational process for designing structures and materials that meet some optimality criteria, e.g. minimum weight or maximum stiffness. The technique has gained significant traction among design engineers and has been shown to identify designs where efficient use of materials resulted in unprecedented performance. The objective of this research is to develop a new topology optimization framework capable of designing structural topologies that are robust in the presence of geometric uncertainties. Accounting for dimensional uncertainties in the optimization process is of paramount importance in many engineering applications, for example in processes involving extreme miniaturization of the material architecture, where the physical realization of the optimal design is generally subjected to significant tolerances. The application of this research to state-of-the-art commercially available manufacturing technologies, including additive manufacturing, will greatly impact the development of architected cellular materials and promote technology transfer. Results of this research will be incorporated into graduate courses within the Engineering and Applied Science PhD program at the University of Massachusetts, Dartmouth. This project will provide training for graduate and undergraduate students at UMass Dartmouth (where many students are first generation college students), the University of California, Irvine and Johns Hopkins University.This research will result in a new design optimization framework capable of designing structural topologies that are robust in the presence of geometric uncertainties. A number of strategies will be explored based on novel integration of stochastic analysis and uncertainty representation and propagation methods with efficient topology optimization techniques and inverse homogenization-based material design frameworks. Stochastic topology optimization frameworks for both continuum and discrete structures will be developed where flaw-tolerance is achieved through careful incorporation of nodal and boundary uncertainties. Novel methodologies for the characterization and representation of geometric uncertainties are planned, including an experimental effort aimed at carefully measuring typical geometric flaws in architected cellular materials fabricated with state-of-the-art additive manufacturing techniques, and quantifying their impact on the statistical variations of the macroscopic mechanical properties. This experimental investigation will feed into the development of a topology optimization framework centered on flaw-tolerance.

拓扑优化是设计符合某些最佳标准的结构和材料的计算过程，例如最小重量或最大刚度。该技术在设计工程师中获得了巨大的吸引力，并已被证明可以确定对材料有效使用导致前所未有的性能的设计。这项研究的目的是开发一个新的拓扑优化框架，该框架能够设计在存在几何不确定性的情况下可靠的结构拓扑结构。在优化过程中考虑尺寸的不确定性在许多工程应用中至关重要，例如，在涉及物质体系结构极端微型化的过程中，最佳设计的物理实现通常会具有明显的公差。这项研究应用于包括增材制造在内的商业可用制造技术，将极大地影响建筑材料的开发并促进技术转移。这项研究的结果将纳入马萨诸塞大学达特茅斯大学工程和应用科学博士学位课程的研究生课程中。该项目将为UMass Dartmouth（许多学生是第一代大学生），加利福尼亚大学，欧文大学和约翰·霍普金斯大学的研究生和本科生提供培训。这项研究将导致一个新的设计优化框架，能够设计出在几何不阶段的情况下设计出强大的结构性拓扑结构。将基于随机分析和不确定性表示和传播方法的新型整合以及有效的拓扑优化技术和基于反均质化的材料设计框架来探索许多策略。将开发用于连续体和离散结构的随机拓扑优化框架，通过仔细掺入节点和边界不确定性来实现易于障碍的情况。计划了用于表征和表示几何不确定性的新方法，包括旨在仔细测量用最先进的增材制造技术制造的构建的蜂窝材料中典型的几何缺陷，并量化其对宏观机械特性统计变化的影响。这项实验研究将介绍出以缺陷为中心的拓扑优化框架的发展。