On the Thermal Conductivity Calculation From Pore-Scale Simulations of Porous Materials
Bibliographic record
Abstract
Abstract Heat transfer in porous materials is of great importance for various natural, biological, and industrial processes. For the large difference between the microscopic and macroscopic dimensions, the volume averaging method (VAM) has been developed to obtain apparent thermal conductivity at the macroscopic level for the microscopic temperature and flow distributions, which can be calculated from the pore-scale simulations. In this article, we perform analysis on the influence of different representative element volume (REV) options on the validity of the thermal equilibrium assumption and the VAM calculated thermal conductivity coefficients. Numerical results from a demonstration simulation are also presented to verify and illustrate the theoretical analysis. Our results and discussion reveal a strong dependence of the thermal equilibrium condition and the calculated conductivity values on REV selection, while this should not be the case since the artificial REV selection should not affect the physical features of a system. This work raises long-time over-looked concerns and calls for caution in future relevant studies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".