Uncertainty Quantification for Airfoil Icing using Polynomial Chaos Expansions

TL;DR

This paper demonstrates that Polynomial Chaos Expansions can efficiently quantify uncertainties in airfoil icing effects on aerodynamics, offering a faster alternative to traditional Monte Carlo simulations.

Contribution

It introduces the application of Polynomial Chaos Expansions to airfoil icing uncertainty quantification, showing significant efficiency improvements over Monte Carlo methods.

Findings

PCE provides accurate uncertainty estimates consistent with Monte Carlo results.

PCE significantly reduces computational time for airfoil icing simulations.

The method is effective across various icing scenarios.

Abstract

The formation and accretion of ice on the leading edge of a wing can be detrimental to airplane performance. Complicating this reality is the fact that even a small amount of uncertainty in the shape of the accreted ice may result in a large amount of uncertainty in aerodynamic performance metrics (e.g., stall angle of attack). The main focus of this work concerns using the techniques of Polynomial Chaos Expansions (PCE) to quantify icing uncertainty much more quickly than traditional methods (e.g., Monte Carlo). First, we present a brief survey of the literature concerning the physics of wing icing, with the intention of giving a certain amount of intuition for the physical process. Next, we give a brief overview of the background theory of PCE. Finally, we compare the results of Monte Carlo simulations to PCE-based uncertainty quantification for several different airfoil icing scenarios. The results are in good agreement and confirm that PCE methods are much more efficient for the canonical airfoil icing uncertainty quantification problem than Monte Carlo methods.