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IGLOO3D Computations of the Ice Accretion on Swept-Wings of the SUNSET2 Database
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 10, 2019 by SAE International in United States
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In the framework of the SUNSET2 program, ice accretion numerical tools were assessed against experimental data. The database of ice shapes was generated on CRM-like swept-wings in NASA’s IRT wind tunnel. This paper discusses calculations of ice accretion on some cases of this database with the ONERA’s 3D ice accretion suite, called IGLOO3D. This tool is described in the first part of the paper. It is designed to couple three codes which solve the air flow (Navier-Stokes solver), the trajectories of water droplets (Eulerian or Lagrangian solver) and the ice accretion (Messinger approach), respectively. The coupling is made in a one-way manner by exchanging CGNS files. IGLOO3D is modular and makes it possible to couple any code as long as it reads and produces CGNS files.
The ice thickness predicted by IGLOO3D is compared against the experimental ice shapes and the LEWICE3D results. Several issues are discussed, especially regarding the bulk ice density and the effect of the convective transfer in the energy balance. The convective transfer is strongly influenced by roughness. Several approaches are shown in the present paper regarding the modelling of the effects of roughness. First, the baseline approach consists of employing the Makkonen integral model with a uniform equivalent sand-grain roughness height. Second, the heat transfer coefficient is inferred from Navier-Stokes computations with a model for roughness effects. The distribution of roughness properties is inferred from a dedicated ice shape of the SUNSET2 database. This approach is quite new for 3D ice accretion computations, which raises some new issues discussed in the present paper.
CitationRadenac, E., Gaible, H., Bezard, H., and Reulet, P., "IGLOO3D Computations of the Ice Accretion on Swept-Wings of the SUNSET2 Database," SAE Technical Paper 2019-01-1935, 2019, https://doi.org/10.4271/2019-01-1935.
Data Sets - Support Documents
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