Data-driven decision analytics framework for complex engineering design applications

适用于复杂工程设计应用的数据驱动决策分析框架

• 批准号: 518139-2017
• 负责人: Nair, PrasanthBalagopal
• 金额: $ 5.72万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696615
• 关键词: Data; driven; decision; analytics; framework

Advances in numerical algorithms and computing architectures made over the last two decades have led to the increasing adoption of computational methods in engineering design practice. This has enabled significant improvements in the performance and quality of complex engineering systems while reducing the time required to bring new products to market. However, in spite of the significant progress that has been made in the field of computation-based engineering design, a number of challenges remain to be overcome in order to develop the next generation of software tools for design of complex engineering systems. The proposed project will address some of the outstanding challenges in the field of data-driven engineering design optimization under uncertainty. The main topics of investigation are: (i) scalable emulator construction algorithms that use Kronecker matrix algebra to accelerate calculations for large-scale high-dimensional datasets, (ii) efficient computational methods for optimum sequential planning of computer experiments and reuse of data for product family design, (iii) scalable compressive sensing algorithms for uncertainty quantification, (ii) emulator-assisted entropy search algorithms for design optimization under uncertainty, and (ii) rapid calculation of decision analytics relevant to engineering design and decision making under uncertainty. Progress in these research areas would enable the application of data-driven methods to complex computational engineering problems that cannot be tackled at present using existing methods. The proposed research program will be carried out in collaboration with Pratt & Whitney Canada who is a global leader in gas turbine engines.

在过去的二十年中，数值算法和计算架构的进步导致了越来越多的工程设计实践中采用的计算方法。这使得复杂工程系统的性能和质量得到显著改善，同时减少了将新产品推向市场所需的时间。然而，尽管在基于计算的工程设计领域已经取得了重大进展，一些挑战仍然有待克服，以开发下一代的软件工具，设计复杂的工程系统。该项目将解决不确定性下数据驱动工程设计优化领域的一些突出挑战。调查的主要题目是：（i）使用Kronecker矩阵代数来加速大规模高维数据集的计算的可扩展仿真器构造算法，（ii）用于计算机实验的最佳顺序规划和产品族设计的数据重用的有效计算方法，（iii）用于不确定性量化的可扩展压缩感测算法，（ii）仿真器辅助的熵搜索算法用于不确定性下的设计优化，以及（ii）与不确定性下的工程设计和决策相关的决策分析的快速计算。这些研究领域的进展将使数据驱动的方法能够应用于目前使用现有方法无法解决的复杂计算工程问题。拟议的研究计划将与普惠加拿大公司合作进行，普惠加拿大公司是全球燃气涡轮机发动机的领导者。