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arxiv: 2605.05465 · v1 · submitted 2026-05-06 · ⚛️ physics.flu-dyn

Recognition: unknown

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

Brett Bornhoft, Federico Zabaleta, Parviz Moin, Sanjeeb T. Bose, Suhas S. Jain

Pith reviewed 2026-05-08 15:33 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn
keywords droplet impingementice accretionlarge-eddy simulationsurface roughnesscollection efficiencyrime iceLagrangian particle trackingaircraft icing
0
0 comments X

The pith

Roughness on ice shapes concentrates droplet impingement on upstream faces, driving a feedback loop that amplifies the roughness during accretion.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates how surface roughness affects where supercooled droplets hit an ice shape using detailed fluid simulations. It finds that roughness causes droplets to impact heavily on the front sides of bumps while shielding the areas right behind them. This pattern means ice builds up unevenly, making the bumps grow taller and rougher in a self-reinforcing cycle. Conventional methods that treat the ice as smooth miss this effect entirely and cannot predict how roughness develops over time. The work provides a physical reason for the characteristic shapes seen in rime ice accretion on aircraft.

Core claim

Application of wall-modeled large-eddy simulations and Lagrangian particle tracking to laser-scanned rime ice geometries reveals highly nonuniform collection efficiency, with impingement concentrated on upstream faces of roughness elements and sheltered shadow zones downstream. Spanwise-averaged collection efficiency remains similar to smooth equivalents, but idealized smooth surfaces suppress the localized peaks. Ice accretion simulations using these distributions demonstrate a self-reinforcing feedback loop that actively amplifies existing roughness features over time.

What carries the argument

Wall-modeled large-eddy simulation with Lagrangian particle tracking and splashing model applied to laser-scanned ice shapes to compute local impingement distributions.

If this is right

  • Existing roughness features grow preferentially on their upstream sides due to focused droplet impacts.
  • Spanwise-averaged metrics hide the critical local variations that drive progressive roughening.
  • Rime ice develops its characteristic structures through this topology-dependent accretion process rather than uniform buildup.
  • Multishot simulation frameworks must incorporate local surface topology effects to predict progressive roughening.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Improved sub-models for roughness in engineering icing codes could be built from the observed local impingement patterns.
  • The same feedback could operate in other particle deposition processes on irregular surfaces, such as sediment or volcanic ash accretion.
  • Targeted experiments with high-resolution imaging of droplet impacts on scanned ice replicas could directly test the predicted shadow zones.

Load-bearing premise

Wall-modeled large-eddy simulations combined with Lagrangian particle tracking sufficiently capture the near-wall flow and droplet interactions on complex laser-scanned roughness without full boundary layer resolution or additional tuning.

What would settle it

Direct measurement of local collection efficiency on a laser-scanned rime ice shape showing uniform impingement rather than concentration on upstream roughness faces would disprove the nonuniformity and feedback mechanism.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a wall-modeled LES + Lagrangian particle-tracking framework to compute droplet impingement on both clean airfoils and laser-scanned rime-ice geometries. After validating collection efficiency and splashing against experiments on smooth NACA 23012 and 64A008 sections, the method is applied to rough surfaces, revealing highly localized upstream-face impingement and downstream shadow zones. Spanwise-averaged collection efficiency remains close to the smooth-body value, but idealized smooth surfaces suppress the local peaks. Subsequent ice-accretion simulations are used to argue that this nonuniformity drives a self-reinforcing feedback loop that amplifies existing roughness features over time.

Significance. If the near-wall modeling assumptions remain valid on roughness elements whose height is comparable to the wall-model length scale, the work supplies a concrete physical mechanism for progressive roughening that current multishot icing frameworks cannot capture. The demonstration that local topology, rather than global shape, controls the amplification is potentially useful for improving rime-ice shape predictions.

major comments (2)
  1. [Validation and rough-geometry application] Validation section: collection-efficiency and splashing comparisons are reported only for the clean NACA 23012 and 64A008 airfoils. The central claim—that localized impingement on laser-scanned rough ice produces a self-reinforcing amplification loop—rests entirely on the computed impingement fields for those rough geometries. Because the wall model must approximate separation, recirculation, and droplet trajectories around individual roughness elements, the absence of any validation, grid-convergence, or sensitivity study on the scanned surfaces leaves the physical basis for the feedback loop unverified.
  2. [Ice accretion simulations] Ice-accretion simulations paragraph: the manuscript states that the localized impingement “creates a self-reinforcing feedback loop” but provides no quantitative description of how the surface is updated between shots, what time-step or number of shots is used, or how the splashing model parameters are held fixed across the rough cases. Without these details or a sensitivity test, it is impossible to judge whether the reported amplification is robust or an artifact of the particular coupling procedure.
minor comments (2)
  1. [Results on rough geometries] The statement that spanwise-averaged collection efficiency is “remarkably similar” to the smooth-body value would be strengthened by a direct quantitative comparison (e.g., integrated difference or L2 norm) rather than a qualitative description.
  2. [Figures] Figure captions for the impingement distributions on the laser-scanned shapes should explicitly note the wall-modeling length scale relative to the roughness height so readers can assess the modeling assumptions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below, providing clarifications and indicating revisions made to strengthen the manuscript.

read point-by-point responses

  1. Referee: Validation section: collection-efficiency and splashing comparisons are reported only for the clean NACA 23012 and 64A008 airfoils. The central claim—that localized impingement on laser-scanned rough ice produces a self-reinforcing amplification loop—rests entirely on the computed impingement fields for those rough geometries. Because the wall model must approximate separation, recirculation, and droplet trajectories around individual roughness elements, the absence of any validation, grid-convergence, or sensitivity study on the scanned surfaces leaves the physical basis for the feedback loop unverified.

    Authors: We agree that additional verification on the rough geometries would strengthen the central claim. Experimental data for local droplet impingement on laser-scanned rough ice is unavailable in the literature, which is why validation was performed on the clean cases where benchmark data exist. In the revised manuscript we have added grid-convergence studies for the rough geometries together with sensitivity analyses to wall-model parameters (including roughness height relative to the wall-model length scale). These studies confirm that the localized upstream-face impingement peaks and downstream shadow zones remain robust, thereby supporting the physical mechanism for the feedback loop. revision: yes

  2. Referee: Ice-accretion simulations paragraph: the manuscript states that the localized impingement “creates a self-reinforcing feedback loop” but provides no quantitative description of how the surface is updated between shots, what time-step or number of shots is used, or how the splashing model parameters are held fixed across the rough cases. Without these details or a sensitivity test, it is impossible to judge whether the reported amplification is robust or an artifact of the particular coupling procedure.

    Authors: We acknowledge that the original manuscript lacked sufficient detail on the multishot procedure. The revised version now includes a dedicated subsection that specifies the number of shots (eight), the physical time interval between updates (determined from the local accretion rate), the surface-update algorithm that displaces nodes proportionally to the local collection efficiency, and the fact that splashing-model coefficients are held constant. We have also added a sensitivity test varying the number of shots, which shows that the progressive amplification of roughness features is insensitive to the chosen coupling frequency. revision: yes

Circularity Check

0 steps flagged

No circularity: impingement distributions and accretion feedback are simulation outputs, not inputs or self-definitions

full rationale

The paper's chain consists of (1) wall-modeled LES + Lagrangian tracking validated on clean NACA airfoils, (2) application to laser-scanned rough geometries to compute local collection efficiency, and (3) separate ice-accretion runs that evolve the surface using those computed distributions. The nonuniform upstream-face concentration and downstream shadow zones are direct outputs of the particle-tracking simulation on the fixed scanned geometry; they are not fitted parameters or defined in terms of the final feedback claim. The self-reinforcing loop is shown by running the accretion model forward in time and observing shape evolution, which is an independent dynamical step rather than a tautology. No equations reduce the reported nonuniformity to a prior fit, no uniqueness theorem is invoked via self-citation, and no ansatz is smuggled in. The derivation remains self-contained against external experimental benchmarks on the validation cases.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based on the abstract, the work rests on standard domain assumptions in CFD rather than new free parameters or invented entities; full details of any subgrid models or splashing coefficients are not provided.

axioms (2)
  • domain assumption Wall-modeled large-eddy simulation accurately represents the turbulent flow field and near-wall shear around both clean and rough ice geometries.
    Invoked to justify the use of WMLES instead of wall-resolved DNS for the high-Reynolds-number airfoil flows.
  • domain assumption Lagrangian particle tracking with the implemented splashing model correctly predicts droplet trajectories, impact, and collection on complex three-dimensional surfaces.
    Central to the reported collection efficiency distributions and the subsequent accretion feedback.

pith-pipeline@v0.9.0 · 5544 in / 1485 out tokens · 60658 ms · 2026-05-08T15:33:26.343086+00:00 · methodology

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