Data-Driven Low-Dimensional Modeling and Uncertainty Quantification for Airfoil Icing

TL;DR

This paper develops a data-driven framework combining Proper Orthogonal Decomposition and Polynomial Chaos Expansions to model and quantify uncertainty in airfoil ice shape effects on aerodynamic performance.

Contribution

It introduces a novel low-dimensional parameterization of ice shapes and an efficient surrogate modeling approach for uncertainty quantification in aerodynamics.

Findings

Identified key parameters governing ice shape variability.

Built an efficient surrogate model for aerodynamic performance prediction.

Quantified the impact of ice shape uncertainty on aerodynamics.

Abstract

The formation and accretion of ice on the leading edge of an airfoil can be detrimental to aerodynamic performance. Furthermore, the geometric shape of leading edge ice profiles can vary significantly depending on a wide range of physical parameters, which can translate into a wide variability in aerodynamic performance. The purpose of this work is to explore the variability in airfoil aerodynamic performance that results from variability in leading edge ice shape profile. First, we demonstrate how to identify a low-dimensional set of parameters that governs ice shape from a database of ice shapes using Proper Orthogonal Decomposition (POD). Then, we investigate the effects of uncertainty in the POD coefficients. This is done by building a global response surface surrogate using Polynomial Chaos Expansions (PCE). To construct this surrogate efficiently, we use adaptive sparse grid sampling of the POD parameter space. We then analyze the data from a statistical standpoint.