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The Glenn Icing Computational Environment (GlennICE) is a computational tool designed to calculate ice growth on complex three-dimensional geometries using the input from a user-supplied computational fluid dynamics (CFD) solution for the geometry of interest. The most significant developments in the advancement of GlennICE have been investigating the convergence of the collection efficiency, efficiently finding trajectories, and improving the refinement methodology. Such developments have increased the efficiency of GlennICE for practical engineering application. With the increasing demand for applying GlennICE for more memory-intensive problems, the scalability of GlennICE has yet to be investigated. This paper is aimed at presenting a method to benchmark the scalability of GlennICE utilizing a relevant engineering problem within a parallel environment.

Computational ice shapes were generated on the boundary layer ingesting engine nacelle of the D8 Double Bubble aircraft. The computations were generated using LEWICE3D, a well-known CFD icing post processor. A 50-bin global drop diameter discretization was used to capture the collection efficiency due to the direct impingement of water onto the engine nacelle. These discrete results were superposed in a weighted fashion to generate six drop size distributions that span the Appendix C and O regimes. Due to the presence of upstream geometries, i.e. the fuselage nose, the trajectories of the water drops are highly complex. Since the ice shapes are significantly correlated with the collection efficiency, the upstream fuselage nose has a significant impact on the ice accretion on the engine nacelle. These complex trajectories are caused by the ballistic nature of the particles and are thus exacerbated as particle size increases.