Bayesian calibration of glaze ice surface roughness for aircraft iceaccretion simulation
Bibliographic record
Abstract
Abstract: In-flight ice accretion on aircraft surfaces is a major weather-related safety issue. Experimental studies in icing conditions or in cryogenic wind-tunnels are supported by numerical simulations involving computational fluid dynamics (CFD). An ice accretion solver couples airflow over the geometry, water droplets impingement, and phase change to compute the ice accretion shape. An ice accretion solver usually relies on a two-equation model: a mass balance and an energy balance. Past studies have established the importance of convective heat loss for energy balance, which is sensitive to the surface roughness. Uncertainties persist in the CFD models given the relative randomness and complexity of the phenomenon related to ice accretion, which usually mixes solid ice with liquid runback water (glaze ice). A major uncertainty is precisely related to the roughness pattern, which is difficult to establish in experimental setups. The calibration of the roughness pattern for a given test case has been seldom investigated in existing literature. Bayesian calibration constitutes a powerful data-driven approach to establish the roughness pattern from the experimental observation. Nevertheless, such Bayesian approach is rarely employed in the context of aircraft icing despite its wide applicability. Instead, the roughness pattern is usually computed with empirical correlations and the equivalent sand grain approach. Nevertheless, no scientific consensus has been reached on the methodology used to determine the roughness pattern. Moreover, classical approaches may fail to predict the roughness pattern in some icing conditions. The objective of the paper is to establish a methodology for the roughness pattern calibration on an airfoil in glaze ice conditions, including roughness distribution along the surface. More specifically, this methodology will determine the roughness pattern needed to be inputted in the simulation to obtain an accretion that best fits the experimental shape. First, an ice accretion solver implemented in SU2 CFD will be used to generate an ice shape database with several roughness patterns. Second, the database will be replaced by a Polynomial Chaos Expansion (PCE) metamodel. This metamodel estimates a mathematical relation between roughness characteristics and ice shape metrics, such as thickness or accretion limit. Finally, a Bayesian inversion will be performed on the metamodel to determine the calibrated roughness pattern. This calibration is obtained by providing experimental accretion metrics to the Bayesian solver. Such methodology produces promising results, giving a roughness pattern leading to an accretion with less than 5% of discrepancies with the experimental thickness, either locally measured or globally averaged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".