An experimental and analytical study of the effect of cold compression on the thermophysical properties of a granular medium
Bibliographic record
Abstract
Based on the previous literature, very few models have described the thermal behavior of granular media or powders as a function of the mechanical stresses to which they are subjected. In recent years, many researchers have been interested in establishing laws that can express the relationship between the apparent thermal conductivity and the mechanical behavior of granular media. The present paper seeks to present a simple model that describes the variation of the apparent thermal conductivity of a granular medium as a function of the mechanical stress. One of the main objectives of this paper is to produce a tool for calculating the thermal conductivity of heterogeneous media, especially that of granular media. For the resolution of the problem, it was decided to use an experimental method recently developed in the laboratory and which is due to be the basis of our calculations. This method is called the hot rod method. It was initially developed to evaluate the damage to a soil subject to superficial heat shock (fires, burns). The results show that for short times and a distance between two measurement points large enough, the 2D transfer can be reduced to a 1D transfer which for long times is hybridized to a hot wire transfer. The modeling of thermal transfers within the environment makes it possible to know the temperature field of soil under the effect of a thermal accident.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".