LES of Droplet Impingement: Application to Clean and Laser-Scanned Ice Shapes

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Zabaleta, Federico, Bornhoft, Brett, Jain, Suhas S., Bose, Sanjeeb T., Moin, Parviz
Format: Preprint
Veröffentlicht: 2026
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866913097123364864
author Zabaleta, Federico
Bornhoft, Brett
Jain, Suhas S.
Bose, Sanjeeb T.
Moin, Parviz
author_facet Zabaleta, Federico
Bornhoft, Brett
Jain, Suhas S.
Bose, Sanjeeb T.
Moin, Parviz
contents The prediction of aircraft icing is conventionally performed using multishot simulation frameworks that fail to predict the progressive roughening of the ice surface. To understand roughness formation, we investigate droplet impingement on clean and laser-scanned rough ice shapes using a high-fidelity computational framework based on wall-modeled large-eddy simulations and Lagrangian particle tracking. This methodology is validated against experimental data for a NACA 23012 airfoil and a NACA 64A008 swept tail, accurately predicting collection efficiency and supercooled large droplet splashing. The framework is subsequently applied to laser-scanned rime ice geometries to quantify the impact of surface roughness on local impingement distributions. The results reveal that physical roughness induces a highly nonuniform collection efficiency, with droplet impingement intensely concentrated on upstream-faces of roughness elements, creating sheltered shadow zones immediately downstream. While the spanwise-averaged collection efficiency remains remarkably similar to that of an equivalent smooth body, idealized smooth surfaces completely suppress these localized impingement peaks. Ice accretion simulations demonstrate that this localized impingement creates a self-reinforcing feedback loop, actively amplifying existing roughness features over time. These findings provide a direct physical explanation for the formation of characteristic rime ice structures and highlight the critical role of local surface topology in the accretion process.
format Preprint
id arxiv_https___arxiv_org_abs_2605_05465
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle LES of Droplet Impingement: Application to Clean and Laser-Scanned Ice Shapes
Zabaleta, Federico
Bornhoft, Brett
Jain, Suhas S.
Bose, Sanjeeb T.
Moin, Parviz
Fluid Dynamics
The prediction of aircraft icing is conventionally performed using multishot simulation frameworks that fail to predict the progressive roughening of the ice surface. To understand roughness formation, we investigate droplet impingement on clean and laser-scanned rough ice shapes using a high-fidelity computational framework based on wall-modeled large-eddy simulations and Lagrangian particle tracking. This methodology is validated against experimental data for a NACA 23012 airfoil and a NACA 64A008 swept tail, accurately predicting collection efficiency and supercooled large droplet splashing. The framework is subsequently applied to laser-scanned rime ice geometries to quantify the impact of surface roughness on local impingement distributions. The results reveal that physical roughness induces a highly nonuniform collection efficiency, with droplet impingement intensely concentrated on upstream-faces of roughness elements, creating sheltered shadow zones immediately downstream. While the spanwise-averaged collection efficiency remains remarkably similar to that of an equivalent smooth body, idealized smooth surfaces completely suppress these localized impingement peaks. Ice accretion simulations demonstrate that this localized impingement creates a self-reinforcing feedback loop, actively amplifying existing roughness features over time. These findings provide a direct physical explanation for the formation of characteristic rime ice structures and highlight the critical role of local surface topology in the accretion process.
title LES of Droplet Impingement: Application to Clean and Laser-Scanned Ice Shapes
topic Fluid Dynamics
url https://arxiv.org/abs/2605.05465