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Sensitivity of ice accretion and aerodynamic performance degradation to critical physical and modeling parameters affecting airfoil icing

Understanding ice accretion and aerodynamic performance degradation is essential in any aircraft certification program to ensure safe flight in icing conditions. In addition to well-known meteorological icing parameters and flight conditions, several physical and modeling parameters are known to pla...

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Bibliographic Details
Published in:Aerospace science and technology 2020-03, Vol.98, p.105659, Article 105659
Main Authors: Prince Raj, L., Yee, K., Myong, R.S.
Format: Article
Language:English
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Summary:Understanding ice accretion and aerodynamic performance degradation is essential in any aircraft certification program to ensure safe flight in icing conditions. In addition to well-known meteorological icing parameters and flight conditions, several physical and modeling parameters are known to play a critical role in the simulation of ice accretion on aircraft surfaces. In this study, the sensitivity of eight ice shape attributes and ice mass to five critical physical and modeling parameters was investigated using a high-fidelity computational method. In the sensitivity analysis, the Sobol sequence sampling method, the radial basis function, and Sobol's method were used to generate the sampling points in the given design space, to construct the metamodel, and evaluate the sensitivity indices, respectively. Based on the sensitivity indices, the number of shots turned out to be the largest contributor in the sum of both the first-order and total effects. Surface roughness was also shown to be the dominant parameter affecting the ice horn height and ice horn position because of the strong connection between roughness and heat flux. In general, it was shown that to varying degrees each parameter has a direct effect on ice accretion attributes and aerodynamic performance degradation. Further, it was noted that the parameters' interactions have a significant effect on the ice accretion attributes.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2019.105659