Title: Uncertainty Quantification across Design Space using Spatially Accurate Polynomial Chaos
Abstract: In the last decade, the demand for stochastic engineering results has increased substantially. Concurrently, improvements in computing hardware, access to large computing clusters, and new efficient statistical methods have made uncertainty quantification studies feasible for complex, computationally expensive simulations such as computational fluid dynamics or finite element methods. In current practice, uncertainty may be quantified at particular locations within a flight envelope or design space, but an ongoing challenge remains to interpolate or extrapolate this information to predict the uncertainty at untested or unsimulated locations. Gaussian processes offer one solution for the prediction of uncertainty at unsimulated locations; however the underlying assumption that quantities of interest are represented by multi-variate Gaussian distributions is overly restrictive and often unrealistic for typical engineering problems. The goal of this paper is to introduce a new statistical method entitled Spatially Accurate Polynomial Chaos which can interpolate or extrapolate both aleatory and epistemic uncertainty throughout a flight envelope or design space. As a demonstration of the method, results are shown for simulations of the NASA Common Research Model.
Publication Year: 2020
Publication Date: 2020-01-05
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 2
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