Recent Advances in Uncertainty Quantification Methods for Engineering Problems

TL;DR

This paper reviews recent advances in uncertainty quantification methods for engineering, focusing on Polynomial Chaos and Gaussian Process techniques, demonstrated through a supersonic nozzle CFD case study.

Contribution

It details the latest developments in UQ methods like Polynomial Chaos and Gaussian Process, applied to complex engineering problems with multiple uncertainties.

Findings

UQ methods effectively estimate mean and standard deviation of outputs.

Application to a supersonic nozzle demonstrates practical utility.

Integration of UQ with CFD enhances robustness analysis.

Abstract

In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the robustness of engineering designs. This chapter aims to detail recent advances in popular uncertainty quantification methods used in engineering applications. This chapter describes the two most popular meta-modeling methods for uncertainty quantification suitable for engineering applications (Polynomial Chaos Method and Gaussian Process). Further, the UQ methods are applied to an engineering test problem under multiple uncertainties. The test problem considered here is a supersonic nozzle under operational uncertainties. For the deterministic solution, an open-source computational fluid dynamics (CFD) solver SU2 is used. The UQ methods are developed in Matlab and are further combined with SU2 for the uncertainty and sensitivity estimates. The results are presented in terms of the mean and standard deviation of the output quantities.