Thermal radiation and conduction properties of materials ranging from sand to rock-fill
Bibliographic record
Abstract
This paper presents an experimental study on thermal radiation and the thermal conductivity of rock-fill materials using a 1 m × 1 m × 1 m heat transfer cell. Testing temperatures are applied by temperature-controlled fluid circulation at the top and bottom of the sample. Heat flux and temperature profiles are measured to establish the effective thermal conductivity λe, which includes contributions from both conduction and radiation heat transfer mechanisms. The materials studied had an equivalent particle size (d10) ranging from 90 to 100 mm and porosity (n) ranging from 0.37 to 0.41. The experimental results showed that thermal radiation greatly affects the effective thermal conductivity of materials with λe values ranging from 0.71 to 1.02 W·m−1·K−1, compared with a typical value of 0.36 W·m−1·K−1 for conduction alone. As expected, the effective thermal conductivity increased with particle size. An effective thermal conductivity model has been proposed, and predictions have been successfully compared with the experimental results. Radiation heat transfer becomes significant for d10 higher than 10 mm and predominant at values higher than 90 mm. The results of the study also suggest that the cooling potential of convection embankments used to preserve permafrost conditions may not be as efficient as expected because of ignored radiation effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".