Recent developments of the multi-physics solver champs-ice
Bibliographic record
Abstract
This paper presents recent improvements in CHAMPS (Chapel Multi-Physics Software) developed at Polytechnique Montréal with emphasis on the aero-icing capabilities.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 and fluid-structure interactions through an Unstructured Finite-Volume Unsteady Reynolds-Averaged Navier-Stokes (URANS) realm.It is programmed using the open-source Chapel language, which facilitates parallel computing on laptops, desktops and High-Performance Computing (HPC) platforms.The basic approach to model ice shapes involves several modules with a segregated strategy: aerodynamic, droplet, thermodynamic and geometric.Four newly implemented state-of-the-art features expand the aero-icing suite, CHAMPS-ICE.A first feature is the implementation of a transitional turbulence model to enhance the physical representation of the boundary layer state near the airfoil (1).Furthermore, a local ice roughness model is added (2) to properly quantify the position of the laminar-turbulent transition which influences the surface convective heat transfer.As for the droplet module, it considers the first and second-order effects of Supercooled Large Droplets (SLD) such as droplet splashing and deformation (3).This extension brings the collection efficiency amplitude and impingement limits closer to experimental data.Another development addresses the stochasticity of the ice accretion process using an advancing front technique (4).Those newly implemented features are discussed and validated on 2D and 3D rime and glaze ice cases taken from the American Institute of Aeronautics and Astronautics (AIAA) Ice Prediction Workshops and the European Ice Genesis project.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".