Computational methods for modeling and design of complex engineering systems under uncertainty

不确定性下复杂工程系统建模与设计的计算方法

• 批准号: RGPIN-2016-06330
• 负责人: Nair, Prasanth
• 金额: $ 2.77万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638804
• 关键词: Computational; methods; modeling; design; complex

Computation-based design is starting to become the norm in many engineering sectors and high-fidelity models are now increasingly used to support decision making in industrial design practice. Despite the significant progress that has been made in this area, a number of challenges remain to be overcome in order to fully realize the potential of computation-based design workflows to accelerate complex systems design. One of the main challenges is computational cost. With the increasing push towards high-fidelity models, evaluation of a wide range of design alternatives/concepts can be computationally infeasible even on massively parallel computers. Another challenge arises from the fact that uncertainty is ubiquitous in the mathematical modeling and characterization of engineering systems. Existing approaches can be computationally prohibitive when dealing with uncertainty, e.g., when engineers wish to predict performance statistics using a high-fidelity model and/or decide on parameter settings that can help mitigate the impact of uncertainty. These two challenges are exacerbated by the fact that computer models of complex real-world engineering systems are often parametrized in terms of a huge number of design variables and uncertain parameters. There is a pressing need for scalable and efficient algorithms to tackle high-dimensional problems in computational modeling and design optimization, especially in the presence of uncertainty.

基于计算的设计开始成为许多工程领域的标准，高保真模型现在越来越多地用于支持工业设计实践中的决策。尽管在这一领域取得了重大进展，但仍有许多挑战有待克服，以充分实现基于计算的设计工作流程的潜力，以加速复杂系统的设计。主要挑战之一是计算成本。随着对高保真模型的不断推动，即使在大规模并行计算机上，对各种设计方案/概念的评估在计算上也是不可行的。另一个挑战来自于这样一个事实，即不确定性在工程系统的数学建模和表征中无处不在。现有的方法在处理不确定性时可能在计算上是禁止的，例如，当工程师希望使用高保真模型预测性能统计数据和/或决定可以帮助减轻不确定性影响的参数设置时。这两个挑战加剧了复杂的现实世界的工程系统的计算机模型，往往是参数化的大量的设计变量和不确定的参数。在计算建模和设计优化中，特别是在存在不确定性的情况下，迫切需要可扩展且有效的算法来解决高维问题。