Collaborative Research: Model-Based Multidisciplinary Dynamic Decisions in Design

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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.