Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach

Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned
along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook
vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive
iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice
shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has
recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A
method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a
self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012
airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at
0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without
ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early
stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits
of the method are investigated using the non-iced NACA 0012 airfoil measurements.