The objective of this collaborative research project is to assess multidisciplinary optimization (MDO) methods as coordinating frameworks for the systems engineering process during the detailed design of large-scale complex systems. Systems engineering is the process that coordinates the integration of many design teams within a large project. The research addresses simultaneous optimization of the system design and the systems engineering process itself. The emphasis of the work is on the detailed design phase, in which an organization is divided along component lines rather than by engineering discipline, and the design of thousands of components occur in parallel. The research will first investigate payoff measures of the system in a relevant context and formulate these measures into optimization objectives and constraints. Next, several MDO methods will be examined for their potential to facilitate systems engineering process coordination, and formal metrics will be established to measure effectiveness of the methods. These methods will be assessed using a simulation of the design of a gas turbine engine as a representative example of a large-scale complex system. The simulation will include both physics-based analyses and behavioral models that describe the action of component design teams in response to different process coordination approaches.

The engineering effort devoted to detailed design in systems such as aircraft significantly exceeds the effort expended on the earlier phases of conceptual and preliminary design. If successful, this research can potentially introduce improved ways of organizing the detailed design of large-scale complex systems to reduce cost overruns and schedule delays. The research program may also reinvigorate the study of MDO in a new and meaningful way. To encourage continued research in this area, a graduate seminar course on the applicability of MDO to systems engineering management will be created, employing the simulation as a pedagogic aid.

Project Report

The objective of this research was to formulate a computational approach to simulate the behavior of design engineers engaged in the development of complex engineered systems such as aircraft, spacecraft, and gas turbine engines. A particular focus was to simulate the decision-making behavior of individual engineers situated in a design and manufacturing firm. This simulation was enabled by developing a systematic approach for computationally modeling the preferences, knowledge, and risk aversion tendencies of design engineers based on the incentives implied by their role in the design firm. Multiple individual designer models were then integrated into a computational framework to allow for the simulation of an engineering team, with each designer focused on a different part or different task related to the development of the overall complex system. To facilitate refinement of the models, several scenarios were simulated, including a part geometry design problem and the design of components of a gas turbine engine. Whereas most prior work in the literature has abstracted design activities as information processing tasks or simple work models, this research grant resulted in new approaches that capture the influence of specific physical characteristics of a complex system in defining the difficulty of the design problem. The resulting models are appropriate as a basis for future studies to compare the influence of designer preferences and risk aversion tendencies on the quality, performance, cost, and value of complex engineered systems. The methods developed in this grant could be leveraged to investigate the root causes of cost and schedule overruns and performance shortfalls in the development of complex engineered systems and to develop insights to reduce these shortcomings in future projects. The techniques could also assist in defining organizational and incentive structures that promote good project performance in the development of commercial or defense systems.

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Georgia Tech Research Corporation
United States
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