Numerical Simulation of In-Flight Iced Surface Roughness

Ignatowicz, Kevin; Beaugendre, Héloïse; Morency, François

doi:10.1007/978-3-030-64725-4_29-1

Cited by 1 publication

(3 citation statements)

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“…If the energy in the control volume is higher t fusion energy (the solidification energy plus the latent heat of phase change), the liquid. When the total energy of the control volume is between the energy of solidi and the energy of fusion, ice and liquid water coexist, and a glaze ice state is ob The contributions remain almost identical among the ice accretion models, with so ditions such as the radiation or the conductivity through ice [47,97]. The variables i…”

Section: 𝑚 + 𝑚mentioning

confidence: 99%

“…When the total energy of the control volume is between the energy of solidification and the energy of fusion, ice and liquid water coexist, and a glaze ice state is observed. The contributions remain almost identical among the ice accretion models, with some additions such as the radiation or the conductivity through ice [47,97]. The variables in Equations ( 31) and (32) were described by Özgen and Canibek [98].…”

Section: 𝑚 + 𝑚mentioning

confidence: 99%

“…The runback velocity is a function of the skin friction coefficient in the shear-stress driven liquid film models [94,97].…”

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confidence: 99%

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Surface Roughness in RANS Applied to Aircraft Ice Accretion Simulation: A Review

Ignatowicz,

Morency,

Beaugendre

2023

Fluids

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Experimental and numerical fluid dynamics studies highlight a change of flow structure in the presence of surface roughness. The changes involve both wall heat transfer and skin friction, and are mainly restricted to the inner region of the boundary layer. Aircraft in-flight icing is a typical application where rough surfaces play an important role in the airflow structure and the subsequent ice growth. The objective of this work is to investigate how surface roughness is tackled in RANS with wall resolved boundary layers for aeronautics applications, with a focus on ice-induced roughness. The literature review shows that semi-empirical correlations were calibrated on experimental data to model flow changes in the presence of roughness. The correlations for RANS do not explicitly resolve the individual roughness. They principally involve turbulence model modifications to account for changes in the velocity and temperature profiles in the near-wall region. The equivalent sand grain roughness (ESGR) approach emerges as a popular metric to characterize roughness and is employed as a length scale for the RANS model. For in-flight icing, correlations were developed, accounting for both surface geometry and atmospheric conditions. Despite these research efforts, uncertainties are present in some specific conditions, where space and time roughness variations make the simulations difficult to calibrate. Research that addresses this gap could help improve ice accretion predictions.

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Section: 𝑚 + 𝑚mentioning

confidence: 99%