Steady‐state heat flux measurements in radiative and mixed radiative–convective environments
Bibliographic record
Abstract
Abstract The steady‐state responses of four heat flux gauges (Schmidt–Boelter, Gardon, directional flame thermometer (DFT) and hemispherical heat flux gauge (HFG)) were examined under various radiative and convective heating conditions. In radiative environments, Gardon measurements were up to 8% higher than Schmidt–Boelter measurements, but in mixed radiative–convective environments, Gardon measurements were 8–18% below those of the Schmidt–Boelter gauge. This difference increased as the convective portion of the total heat transfer increased, due to discrepancies between the radiation‐based calibration environment and the application environment. The DFT data in radiative environments were comparable with the Schmidt–Boelter and Gardon values (within 12%), with the difference largely attributed to natural convection losses from the DFT. In mixed environments, the DFT values were significantly lower than those of the Schmidt–Boelter gauge due to differences in the surface temperatures of the gauges, resulting in the convective flow cooling, rather than heating, the DFT. The HFG heat flux estimates were 35–48% lower than the Schmidt–Boelter measurements under radiative conditions, influenced by large conduction losses from the sensor plate to the gauge housing. Lateral conduction due to a mismatch between the experimental convective flow outlet diameter and the gauge width also affected results from the DFT and HFG. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".