Collaborative Research: Model-Based Multidisciplinary Dynamic Decisions in Design

航空航天器、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。 在传统的范例中应用的简化的设计模型不足以捕获复杂的系统行为。此外，高保真计算机模拟模型和实验测试由于成本高，在许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水平的仿真模型，并决定如何最好地在设计过程的每个阶段进行进一步的仿真。这项研究将创建一个新的决策框架，以满足这一需求，并指导复杂工程系统的设计。由此产生的方法，预计将增加在航空航天，汽车，能源和消费电子产品等行业的设计过程中的价值，减少未发现的问题，发生在开发周期的后期，并减少所需的预算和时间表的设计过程。 该项目还将提供航空航天、机械和工业工程等跨学科的研究培训和教育。该研究的智力意义在于将复杂系统的设计作为一个信息搜索和知识生成（学习）过程，可以建模为随机离散时间动态系统。信息论和决策科学相结合，使设计决策，不仅涉及系统属性的选择，但也选择在设计过程中的后续信息寻求行动。 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名（分层）或不（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，以管理跨学科和学科融合信息所固有的耦合性和复杂性，同时保持分布式分析中的学科自主性。动态决策框架将提供一个统一的会计为许多不同类型的不确定性，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基于经验的策略，以管理解决令人生畏的最佳决策问题的复杂性。该框架将在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。

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.