Robust Structural Topology Optimization

稳健的结构拓扑优化

• 批准号: 543593-2019
• 负责人: Nair, PrasanthBalagopal
• 金额: $ 4.68万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720744
• 关键词: Robust; Structural; Topology; Optimization

Topology optimization is a powerful tool for goal-oriented generative design of multifunctional engineering structures. In contrast to size and shape optimization approaches that work with a finite-dimensional parametric geometry model, topology optimization algorithms can, in theory, search for the best solution in an infinite-dimensional space of possible topologies. Even though significant advances have been made in the field of topology optimization, most methods currently in use are based on the implicit assumption of determinism, i.e., the loading conditions, boundary conditions and material constitutive properties are assumed to be known precisely. The proposed research program will address the key fundamental, theoretical issues and computational challenges associated with the development of efficient algorithms for robust structural topology optimization. The methodology used in this project will involve efficient numerical schemes for solving the stochastic linear elasticity equations coupled with level-set methods. Advances made in this research program will enable designers to fully exploit the potential of advanced additive manufacturing techniques for a broad range of practical engineering applications where performance robustness is a crucial design requirement.

拓扑优化是面向目标的多功能工程结构创成设计的有力工具。与使用有限维参数几何模型的尺寸和形状优化方法相比，拓扑优化算法理论上可以在可能拓扑的无限维空间中搜索最佳解决方案。尽管在拓扑优化领域已经取得了显著的进步，但目前使用的大多数方法都是基于确定性的隐含假设，即，假定加载条件、边界条件和材料本构性质是精确已知的。拟议的研究计划将解决关键的基础，理论问题和计算的挑战与强大的结构拓扑优化的高效算法的发展。在这个项目中使用的方法将涉及有效的数值方案，解决随机线性弹性方程与水平集方法。这项研究计划取得的进展将使设计人员能够充分利用先进增材制造技术的潜力，用于广泛的实际工程应用，其中性能稳健性是一个至关重要的设计要求。