Dependence of the Heat Transfer Coefficient at Quenching on Diameter of Cylindrical Workpieces
Bibliographic record
Abstract
For computer simulation of a quenching process, the fundamental prerequisite is to have the relevant heat transfer coefficient (HTC) calculated as function of workpiece's surface temperature and time respectively. In order to calculate the HTC, experimental measurement of the temperature-time history (cooling curve) near the workpiece surface is necessary. In this investigation, cylindrical probes of 20, 50, and 80 mm diameter are used. The cooling curve was measured always at 1 mm below the surface of the probe. Special care has been taken to keep all other factors (design of the probes, temperature measurement, quenching conditions, and calculation procedure), which can influence on the calculated HTC, constant to assure that the only variable is the diameter of the probe. Supposing a radially symmetrical heat flow at half length of the probe, the HTC was calculated using one-dimensional (1-D) inverse heat conduction method. The unexpected striking result of this investigation is the fact that for biggest probe diameter (80 mm), the calculated HTC as function of surface temperature does not show the film boiling phase. A plausible explanation of this effect is given based on the critical heat flux density. The possibility to establish a simple fixed relation (a correction factor) between the HTC and the diameter of cylinders is discussed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".