权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Data driven computational frameworks for robust design optimization of complex engineering systems

数据驱动的计算框架，用于复杂工程系统的稳健设计优化

基本信息

批准号：
453359-2013
负责人：
Nair, PrasanthBalagopal
金额：
$ 4.16万
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：
2015
资助国家：
加拿大
起止时间：
2015-01-01 至 2016-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=575626
关键词：
Data driven computational frameworks robust

项目摘要

Simulation-based predictive tools have revolutionized engineering design practice, allowing designers to improve system performance and safety before physical prototypes are built and tested. However, a number of computational challenges remain to be addressed in order to apply high-fidelity simulation tools to design complex engineering systems. The proposed research program will deliver efficient computational methods and a software framework that will enable engineers to optimize the performance and robustness of complex engineering systems on a limited computational budget. The focus of this work will be on the development of novel greedy function approximation strategies for constructing computationally efficient surrogate models of high-fidelity simulation models. This work will also include the formulation of linear and nonlinear dimensionality reduction strategies that will enable the application of greedy surrogate modeling algorithms to high-dimensional, large-scale simulation databases typically encountered in engineering practice. Data-driven optimization frameworks for robust design will be developed leveraging the algorithms developed for surrogate modeling. The computational methods developed during this research project will be tested and validated on aeroengine design and aerodynamic shape optimization problems. The key deliverables from this research project will be novel greedy algorithms for constructing surrogate models, surrogate-assisted optimization strategies for robust design, and a general-purpose software toolkit that will enable significant reductions in the design cost for complex real-world engineering systems.

基于仿真的预测工具彻底改变了工程设计实践，使设计人员能够在构建和测试物理原型之前提高系统性能和安全性。然而，为了应用高保真仿真工具来设计复杂的工程系统，一些计算挑战仍有待解决。拟议的研究计划将提供有效的计算方法和软件框架，使工程师能够在有限的计算预算下优化复杂工程系统的性能和鲁棒性。这项工作的重点将是开发新的贪婪函数逼近策略，用于构建计算效率高的高保真仿真模型的代理模型。这项工作还将包括线性和非线性降维策略的制定，这将使贪婪代理建模算法的应用程序，高维，大规模的仿真数据库通常会遇到在工程实践中。将利用为替代建模开发的算法开发用于稳健设计的数据驱动优化框架。在本研究项目中开发的计算方法将在航空发动机设计和气动外形优化问题上进行测试和验证。该研究项目的关键成果将是用于构建代理模型的新型贪婪算法，用于鲁棒设计的代理辅助优化策略，以及一个通用软件工具包，该工具包将显着降低复杂现实世界工程系统的设计成本。