Experimental characterisation of heat flux during industrial quenchingprocesses for accurate estimation of the heat transfert coefficient
Bibliographic record
Abstract
High strength steels used in landing gears in aircraft often go through a quench and temper (Q&T) heat treatment cycle that results in the required mechanical properties. Accurate estimation of the heat transfer during the cooling stage (i.e. quench) is of critical importance for reliable prediction of the distortion that could occur after quenching. While standard laboratory test methods are useful to compare the cooling performance of different quenchants; however, such tests hardly represent the actual conditions experienced in industry and often introduce large uncertainties in the predicted severity of the distortion. Specifically, the complex geometry of the component, the positioning of the heating furnace with respect to the quench bath, etc. are illustrative examples of the sources of differences in the heat extraction dynamic on real-life components from laboratory results since small probe diameters tend to produce film boiling caused by a higher heat flux density, which may not be the case for industrialized components. Furthermore, widely accepted quenching intensity factors based on standard testing (e.g., Grossman number) are unable to represent the physical phenomena of the quenching process.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".