Collaborative Research: Model-Based Multidisciplinary Dynamic Decisions in Design

协作研究：设计中基于模型的多学科动态决策

• 批准号: 1537641
• 负责人: Daniel Apley
• 金额: $ 30万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537641&HistoricalAwards=false
• 关键词: Collaborative; Research; Model; Based; Multidisciplinary

The complexity of engineered systems such as aerospace vehicles, automobiles, and advanced materials systems has reached a tipping point that challenges existing design and analysis methods. Simplified design models, applied in traditional paradigms, are inadequate for capturing complex system behaviors. Moreover, high-fidelity computer simulation models and experimental tests cannot be fully applied in many situations, due to their high costs. As a result, there is a great need for systematically fusing information from multiple sources, including simulation models with multiple levels of fidelity, and for deciding how best to conduct further simulations at each stage of the design process. This research will create a new decision-making framework to address this need and to guide the design of complex engineered systems. The resulting method is expected to increase the value of the design process in industries such as aerospace, automotive, energy, and consumer electronics by reducing the occurrence of undiscovered problems that occur late in a development cycle and decreasing the budget and schedule required for the design process. The project will also provide interdisciplinary research training and education across aerospace, mechanical, and industrial engineering.The intellectual significance of this research is to approach the design of a complex system as an information-seeking and knowledge-generation (learning) process that can be modeled as a stochastic discrete-time dynamical system. Information theory and decision science are integrated to make design decisions that involve not only selection of system attributes, but also choices about subsequent information-seeking actions in a design process. An overarching Bayesian spatial random process modeling framework will be established to fuse heterogeneous information from multifidelity simulations and experiments with uncertainty quantification, where the fidelity of models can be either clearly ranked (hierarchical) or not (nonhierarchical). Approaches based on multidisciplinary statistical sensitivity analysis and multidisciplinary uncertainty analysis will be established for managing the couplings and complexity inherent in fusing information across fidelities and disciplines, while maintaining disciplinary autonomy in distributed analyses. The dynamic decision making framework will provide a uniform accounting for many different types of uncertainties, and decision functions grounded in expected utility theory are formulated to guide subsequent design actions. A further critical contribution of this research is to develop heuristic-based strategies for managing the complexity in solving the daunting optimal decision making problem. The framework will be assessed on a testbed problem involving the design of a distributed electric propulsion aircraft concept.

航天飞行器、汽车和先进材料系统等工程系统的复杂性已经达到了一个临界点，对现有的设计和分析方法提出了挑战。在传统范例中应用的简化设计模型不足以捕捉复杂的系统行为。此外，高保真的计算机仿真模型和实验测试由于成本高，在很多情况下不能完全应用。因此，非常需要系统地融合来自多个来源的信息，包括具有多个保真度级别的仿真模型，并决定如何在设计过程的每个阶段最好地进行进一步的仿真。这项研究将创建一个新的决策框架来解决这一需求，并指导复杂工程系统的设计。由此产生的方法有望通过减少在开发周期后期出现的未被发现的问题，并减少设计过程所需的预算和时间表，从而增加诸如航空航天、汽车、能源和消费电子等行业设计过程的价值。该项目还将提供航空航天、机械和工业工程领域的跨学科研究培训和教育。本研究的智力意义在于将复杂系统的设计视为一个信息寻求和知识生成（学习）过程，该过程可以建模为随机离散时间动力系统。将信息理论与决策科学相结合，进行设计决策，不仅涉及系统属性的选择，还涉及设计过程中后续信息寻求行为的选择。将建立一个总体贝叶斯空间随机过程建模框架，以融合来自多保真度模拟和不确定性量化实验的异构信息，其中模型的保真度可以明确排序（分层）或不排序（非分层）。将建立基于多学科统计敏感性分析和多学科不确定性分析的方法，以管理跨保真度和学科融合信息所固有的耦合和复杂性，同时保持分布式分析中的学科自主性。动态决策框架将为许多不同类型的不确定性提供统一的核算，并制定基于预期效用理论的决策函数，以指导后续的设计行动。这项研究的另一个重要贡献是开发了基于启发式的策略来管理解决令人生畏的最优决策问题的复杂性。该框架将在一个涉及分布式电力推进飞机概念设计的试验台问题上进行评估。