  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.
