Data-driven Roughness Estimation for Glaze Ice Accretion Simulation
Bibliographic record
Abstract
In-flight ice accretion on aircraft is a major weather-related threat. Industries use both experimental investigations in icing conditions and ice accretion solvers based on computational fluid dynamics (CFD) for aircraft development. An ice accretion solver couples airflow over the geometry, water droplets impingement, and phase change to compute the ice accretion. Such a solver usually relies on a two-equation model: a mass balance and an energy balance. Past studies highlighted the importance of the roughness-sensitive convective heat loss for energy balance. Uncertainties persist in the CFD models given the complexity of the ice accretion phenomenon, which usually mixes solid ice with liquid runback water (glaze ice). A major uncertainty is related to the surface roughness pattern, which is difficult to measure in experiments. The calibration of the roughness pattern for a CFD test case was seldom investigated in literature. Among the available calibration tools, the Bayesian calibration constitutes a powerful data-driven approach suitable for roughness pattern estimation. The objective of the paper is to set up a methodology for the roughness pattern calibration on an airfoil in glaze ice conditions. Specifically, this methodology determines the roughness pattern needed to minimize the root mean square error between the numerical and experimental accretions. First, an ice accretion solver implemented in SU2 CFD generates a roughness-sensitive ice shape database. Second, a Polynomial Chaos Expansion (PCE) metamodel replaces the database. Finally, a Bayesian inversion is performed on the metamodel to determine the roughness pattern producing a realistic ice shape. The fidelity of an ice shape prediction is measured with a root mean square (RMS) error on the iced portion of the airfoil. Such methodology produces promising results, giving an accretion with a RMS error of less than 0.4% of the chord length compared to the experimental accretion thickness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.000 |
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".