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

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