A DESIGN SPACE EXPLORATION METHODOLOGY TO SUPPORT DECISIONS UNDER EVOLVING UNCERTAINTY IN REQUIREMENTS AND ITS APPLICATION TO ADVANCED VEHICLES
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- NGN 3000
SUMMARY Recent technological developments have resulted in the emergence of new advanced vehicles such as suborbital vehicles and personal air vehicles. These innovative vehicles have in turn opened up new markets that are characterized by a large and complex solution space that requires designers to account for multiple objectives in early design phases. The complexity of these new vehicles also gives rise to a large combinatorial space of possible configurations for which no baseline has been established. A successful market penetration requires designers to define an optimized baseline and to identify both the main design drivers and potential technology gaps. Another major challenge is the presence of evolving uncertainty in requirements due to the lack of experience and established regulations. Hence, flexible decision-making techniques are needed to alleviate risks inherent to the launch of new programs and support informed go/no-go decisions. This research aims at supporting the development of emerging markets by establishing a methodology that enables a broad design space exploration at a conceptual level, and guides the selection of solutions against unclear objectives and under evolving uncertainty in requirements. In particular, this research uses the development of profitable, safe, and robust suborbital programs as a proof-of-concept to demonstrate the capabilities of the proposed methodology. A review of current design approaches identified a lack of efficient design space exploration techniques. Current methods are indeed only capable of either comparing, at a high-level, numerous architectures or of optimizing a handful of alternatives with respect to more detailed parameters. In addition, there is a lack of available methodology to model and propagate evolving uncertainty in requirements. To bridge these gaps, a four-step methodology is developed based on the generic top-down design decision support process. First, the decision criteria are established. In particular, the design objectives are clearly identified and the design constraints are determined and modeled with time-dependent membership functions. Second, a new variable-oriented morphological analysis is developed to generate xxx all feasible concepts so that they can be systematically further optimized and compared. Third, a modeling and simulation environment is developed, which is capable of rapidly evaluating the performance, life-cycle costs, and safety of all types of suborbital vehicles at a conceptual design level. Finally, a new evolutionary multi-architecture algorithm based on architecture fitness is implemented that drives multi-objective optimization algorithms to simultaneously compare and optimize all configurations. To support decisions under evolving uncertainty, requirements are modeled with time-dependent membership functions and are propagated using fuzzy set theory. The new modeling and simulation environment was developed and implemented in the context of suborbital vehicle design. By leveraging cycle-based approaches and surrogate modeling techniques, the performance of all chemical rocket engines can be evaluated with an accuracy of 3%, while dividing the execution time by a factor of 105 compared to current physics-based models. This environment is also the first of its sort capable of estimating the life-cycle costs of hybrid rocket engines. The application of the proposed methodology also provides decision makers with key insights into the suborbital market. In particular, it demonstrates that a wisely developed commercial suborbital program might be profitable. The methodology also quantifies the trade-offs between affordable winged air launched vehicles powered by solid engines and safe slender vehicles powered by hybrid engines. When compared with existing approaches, the proposed methodology allows decision makers to find solutions 40% more performant for the same execution time or 40 times faster for the same accuracy. By quantifying the trade-offs between risk and expected performance, this methodology also helps designers make challenging go/no-go decisions and provides them with the best program start date. In particular, it provides a robust solution that increases the probability of success by 10% compared to those generated by traditional approaches. Finally, this methodology is a precious tool for designers who wish to quantify and rapidly assess the impacts of potential future regulations on the selection of a design concept and the profitability of the corresponding program.
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