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A common probabilistic approach to uncertainty allocation is to assign acceptable variability in the sources of uncertainty, such that pre-specified probabilities of meeting performance constraints are satisfied. However, the computational cost of obtaining the associated trade-offs increases significantly when more sources of uncertainty and more outputs are considered. Consequently, visualizing and exploring the trade-off space becomes increasingly difficult, which, in turn, makes the decision-making process cumbersome for practicing designers. To tackle this problem, proposed is a parameterization of the input probability distribution functions, to account for several statistical moments. This, combined with efficient uncertainty propagation and inverse computation techniques, results in a computational system which performs order(s) of magnitude faster, compared with a combination of Monte Carlo Simulation and optimization techniques. Also, to aid decisionmaking regarding the potential combinations of uncertainty allocation, enablers for visualizing the trade space are proposed. The combined approach is demonstrated by means of a representative aircraft thermal system integration example.

Interactive Uncertainty Allocation and Trade-off at Early-stage Aircraft Computational Design