Download or read book Uncertainty Budgeting Methods for Conceptual Aircraft Design written by Max Maria Jacques Opgenoord and published by . This book was released on 2016 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantification and management of uncertainty are critical in the design of engineering systems, especially in the early stages of conceptual design. This thesis presents an approach to defining budgets on the acceptable levels of uncertainty in design quantities of interest, such as the allowable risk in not meeting a critical design constraint and the allowable deviation in a system performance metric. A sensitivity-based method analyzes the effects of design decisions on satisfying those budgets, and a multiobjective optimization formulation permits the designer to explore the tradespace of uncertainty reduction activities while also accounting for a cost budget. For models that are computationally costly to evaluate, a surrogate modeling approach based on high dimensional model representation achieves efficient computation of the sensitivities. Example problems in aircraft conceptual design illustrate the approach. The first example investigates the influence of uncertainty in the propulsion technology on the overall aircraft design, whereas the second problem looks at the influence of six different uncertain design parameters from three different disciplines within the aircraft design. Secondly, the distributional sensitivity analysis (DSA) method is extended for better computational efficiency and wider applicability. Instead of assuming that all uncertainty in an input parameter can be reduced, DSA apportions output uncertainty as a function of the uncertainty reduction of a particular input parameter. This leads to more information on influences of uncertainty reduction, and to a more informative ranking of input parameters. In this thesis the ANOVA-HDMR framework is used for DSA to increase computational efficiency. Additionally, this approach allows for using DSA for more general distributions.