Managing Uncertainty in Bilevel Robust Design Optimization

这笔拨款为开发协作优化框架提供资金，用于复杂工程系统（如汽车、飞机或消费品）的稳健设计。协同优化框架将考虑和管理由用于设计这些系统的计算机模拟工具生成的性能预测中的不确定性。每个计算机模拟模型都是对现实的抽象，其性能预测具有一定的不确定性。在基于仿真的设计过程中必须考虑到这种不确定性。在采用分解技术促进分布式计算的双层优化算法中，将开发一种估计系统性能不确定性的隐式方法。该方法将考虑与设计输入相关的不确定性以及来自每种模拟工具的性能预测的不确定性。收敛性的数学证明将被开发来验证在本研究中开发的双层优化算法。该框架将在提供并行计算和并发设计的分布式计算环境中实现。行业合作伙伴将使用一套基准测试问题测试框架并测量计算改进。预计本研究中开发的协作优化框架的使用将在降低成本和风险的情况下缩短产品开发时间。协同优化框架将为并行计算环境下复杂工程系统的并行设计提供便利。并行计算的好处可以缩短产品开发时间。在这种并行设计环境中管理不确定性和风险的能力将确保最终系统的健壮性能。非确定性协同优化框架的发展将证明，在并行计算环境下，设计人员可以有效地管理与基于仿真的新产品设计相关的不确定性和风险。

This grant provides funding for the development of a collaborative optimization framework for the robust design of complex engineering systems such as automobiles, aircraft or consumer products. The collaborative optimization framework will account for and manage the uncertainties in the performance predictions generated by the computer simulation tools used for the design of these systems. Each computer simulation model is an abstraction of reality and has some uncertainty associated with its performance predictions. This uncertainty must be accounted for in the simulation based design process. An implicit method for estimating system performance uncertainties within a bilevel optimization algorithm that employs decomposition techniques to facilitate distributed computation will be developed. The methodology will account for both the uncertainty associated with design inputs and the uncertainty of performance predictions from each of the simulation tools. A mathematical proof of convergence will be developed to validate the bilevel optimization algorithm being developed in this investigation. The framework will be implemented in a distributed computing environment providing for parallel computation and concurrent design. Industry partners will test the framework and measure the computational improvements using a suite of benchmark test problems.It is anticipated that the use of the collaborative optimization framework developed in this research will lead to reduced product development times at reduced cost and risk. The collaborative optimization framework will facilitate the concurrent design of complex engineering systems in a parallel-computing environment. The benefits of parallel computation lead to reductions in product development times. The ability to manage uncertainty and risk in this parallel design environment will ensure robust performance of the resulting system. The development of the non-deterministic collaborative optimization framework will demonstrate that designers can effectively manage both the uncertainty and risk associated with the simulation based design of new products in a parallel computing environment.