Improved robustness formulations and a simulation-based robust concept exploration method
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The goal when applying robust engineering design methods is to improve a system's quality by reducing its sensitivity to uncertainty that has influence on the performance of the product. In the Robust Concept Exploration Method (RCEM) this approach is facilitated with additionally giving the designer the possibility to search for a compromise between the desired performance and a satisfying robustness. The current version of the RCEM, however, has some limitations that render it inapplicable for nonlinear design problems. These limitations, which are demonstrated in this thesis, are mainly connected to the application of global response surfaces and the Taylor series for variance estimations. In order to analyze the limitation of the robustness estimation, several alternative methods are developed, assessed and introduced to a modified RCEM. The developed Multiple Point Method is based on the Sensitivity Index (SI) and improves the variance estimation in RCEM significantly, especially for nonlinear problems. This approach is applicable to design problems, for which the performance functions are known explicitly. For problems that require simulations for the performance estimation, the simulation-based RCEM is developed by introducing the Probabilistic Collocation Method (PCM) to robust concept exploration. The PCM is a surrogate model approach, which generates local response models around the points of interests with a minimum number of simulation runs. Those models are utilized in the modified-RCEM for the uncertainty analysis of the system's performance. The proposed methods are tested with two examples each. The modified RCEM is validated with an artificial design problem as well as the design of a robust pressure vessel. The simulation-based RCEM is validated using the same artificial design problem and the design of a robust multifunctional Linear Cellular Alloy (LCA) heat exchanger for lightweight applications such as mobile computing. The structure of the theoretical and empirical validation of the methods follows the validation square.
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