Sensitivity of the convective heat transfer coefficient to the uncertain surface roughness characteristics
Bibliographic record
Abstract
Numerically predicting the heat transfer on a rough \nsurface is a challenge. Modelling a rough surface without \nadding a thermal correction model tends to over predict \nthe heat transfer coefficient in a RANS simulation. \nThermal correction models must be added to compute the \neffect of roughness elements, described by their height, \nwhich allows computing the equivalent sand grain \nroughness, on the thermal boundary layer. The \nuncertainties on the heat transfer can be critical in inflight \nicing studies, where the early stages of icing \nincrease the surface roughness. The objective of the \npaper is to study the sensitivity of the heat transfer on a \nrough flat plate to the roughness characteristics. The \nanalysis, using the generation of metamodels and the \ncalculation of sensitivity indexes, will allow determining \nwhich characteristics influence the most the heat transfer \ncoefficient variability. After detailing the metamodeling \nmethodology, metamodels predictions obtained and \nsensitivity indexes calculated will be exposed.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".