A Distributed Framework for Probabilistic Analysis
McCormick, David Jeremy
Olds, John R.
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Probabilistic multidisciplinary design optimization promises to incorporate critical design uncertainty in order to create optimal products with a high probability of meeting design constraints under a wide variety of circumstances. Several methods of accelerated probability analysis are available to designers. What is not available is a formal method for tying contributing analysis-level probability analysis into an integrated design framework capable of optimization. This would allow probability methods to be tailored to the characteristics of a particular contributing analysis as well as potentially reduce the dimensionality of the problems considered. This research presents such a method, and then tests it on a conceptual launch vehicle design problem. This probabilistic optimization problem consisted of 84 noise variables and four design variables. This problem setup consistently found system optimums in 6-8 hrs. It utilized several probability approximation methods run in an iterative manner to generate probabilistic vehicle sizing information. Once the probabilistic optimum was identified and confirmed using this process, a system-level Monte Carlo random simulation of the vehicle design was conducted around the optimum point to confirm the accuracy of the distributed approximation method. Because this simulation was prohibitively expensive, it was only conducted at the single optimum point. Following this accuracy confirmation, a comparison to a deterministic optimization of the same problem illustrated the difference between probabilistic and deterministic optimums.