Lagrangian Particle Tracking for Ice Accretion Applications
Bibliographic record
Abstract
This paper presents the implementation of a Lagrangian particle tracking solver used in ice accretion applications. The integration was executed within CHAMPS (CHApel Multi-Physics Software), an in-house multi-physics software under development at Polytechnique Montréal. This software, written in the Chapel language, is designed to simulate two-dimensional and three-dimensional multi-physics phenomena involving aerodynamics such as ice accretion interactions through an Unstructured Finite-Volume Reynolds-Averaged Navier-Stokes (RANS) simulations. The primary objective of the implementation is to enhance the robustness and accuracy of the droplet solver embedded in the ice accretion framework. This is achieved through the incorporation of a hybrid Eulerian-Lagrangian adaptive seeding technique, and taking into account the effects of SuperCooled Large Droplets (SLD). The results obtained demonstrate a favorable agreement in comparison to the Eulerian solver and experimental data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".