| The consideration of uncertainty enables engineers to make reliable decisions. However, a design under uncertainty is more computationally expensive than a deterministic design. To address the engineering needs for design efficiency, methods are developed to improve the computational efficiency of uncertainty analysis with the consideration of uncertainty in design from three aspects, namely, analysis, modeling, and synthesis. These methods emphasize and address the needs in quality engineering and the design of complex engineering systems.; In uncertainty analysis, the Modified Advanced Mean Value (MAMV) method and its extension are developed for the purpose of evaluations of percentile, reliability, and distribution of system performance with high computational efficiency and robustness. Associated with modeling design feasibility under uncertainty, commonly used feasibility modeling techniques are investigated and constructive guidelines are formulated for engineers to choose appropriate techniques to model the design feasibility robustness. A new method for synthesis under uncertainty, the Sequential Optimization and Reliability Assessment (SORA) method is also developed. Different from the traditional double-loop method, the SORA method adopts a single-loop strategy and separates the reliability assessments from the optimization. The design solution can be identified much more quickly than a traditional double-loop method. To meet the need of multidisciplinary systems design, a multidisciplinary robust design procedure, the system uncertainty analysis (SUA) method, and the concurrent subsystem uncertainty analysis (CSSUA) method are proposed. Compared to the Monte Carlo simulation approach, the proposed techniques significantly reduce the number of design evaluations at the system level.; Through example problems such as the Pratt & Witney engine design and the reliability-based design for vehicle crashworthiness of side impact, it is illustrated that the proposed methods have resulted in significant computational savings. The methods are applicable to the general class of engineering systems, with a great impact on systems that require computationally expensive analyses. |
