A Non-Intrusive Polynomial Chaos Method to Efficiently Quantify Uncertainty in an Aircraft T-Tail

Abstract

The problem of developing robust methods for uncertainty quantification (UQ) is of major interest in the engineering and scientific community. To quantify uncertainty, probabilistic models have been developed where traditionally Monte Carlo (MC) methods were used to capture uncertainty bounds. In the engineering context, UQ methods can be practically implemented to limit the amount of prototype redesigns. However MC methods are computationally inefficient due to the large number of samples required to obtain an accurate solution. Polynomial Chaos (PC) methods have recently emerged as an efficient method of probabilistic quantification in lower dimensions compared to MC. This paper will show the ability of a non-intrusive PC method to efficiently quantify uncertainty through first and second order statistics. This approach will lend itself to the treatment of a finite element T-Tail model, using Nastran as a black box around which PC curves can be fit based on its outputs.