Simulation Uncertainty in Multidisciplinary Design

这项拨款为复杂工程系统（如汽车、飞机或消费品）设计的基于决策的设计框架的开发提供资金。这些工程系统的设计涉及广泛使用计算机仿真设计工具。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。本研究将探讨这些不确定性如何通过由许多学科特定的模拟工具组成的多学科系统传播。这项研究的目标是能够量化这些传播的不确定性，以便在基于决策的设计框架中使用。本研究将评估用于估计复杂工程系统中最坏情况传播不确定性的实验数据分析技术，并研究在系统性能的响应面近似上执行蒙特卡罗模拟的效用，以量化传播不确定性的分布。在量化了复杂系统中传播的不确定性之后，本研究的第二阶段将致力于基于决策的设计框架的实现。一种基于信任域管理的增强拉格朗日方法的优化算法将用于并行子空间设计/优化，以驱动基于决策的设计框架。这种优化将基于系统性能的人工神经网络响应面逼近。优化器将使产品在随机性能约束下的总生命周期成本的净收入的预期效用最大化。预计本研究结果将在降低成本和风险的情况下提高产品性能，缩短产品开发时间。基于决策的设计框架的开发将证明设计师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 decision based design framework for the design of complex engineering systems such as automobiles, aircraft or consumer products. The design of these engineering systems involves extensive use of computer simulation design tools. Each computer simulation model is an abstraction of reality and has some uncertainty associated with its performance predictions. This research will investigate how these uncertainties propagate through a multidisciplinary system comprised of many discipline-specific simulation tools. A goal of this research is to be able to quantify these propagated uncertainties for use in a decision-based design framework. This investigation will evaluate techniques available from experimental data analysis for estimating the worst case propagated uncertainty in complex engineering systems and investigate the utility of performing Monte Carlo simulations on response surface approximations of system performance for quantifying the distributions of the propagated uncertainties. Having quantified propagated uncertainties in complex systems, the second phase of this research effort will be devoted to the implementation of a decision-based design framework. An optimization algorithm based on a trust region managed augmented Lagrangian approach for concurrent subspace design/optimization will be used to drive the decision-based design framework. This optimization will be based on artificial neural network response surface approximations of the system performance. The optimizer will maximize the expected utility of the net revenue for the total life cycle cost of the product subject to stochastic performance constraints. It is anticipated that the results of this research will lead to improved product performances as well as reduced product development times at reduced cost and risk. The development of a decision-based design framework will demonstrate that designers can effectively manage both the uncertainty and risk associated with the simulation based design of new products.