Effect of Elastic Deformation and Rough Grain Surface on Heat Conduction in Partially Saturated Granular Porous Media
Bibliographic record
Abstract
Abstract Accurate prediction of the effective thermal properties of partially saturated rock, soil, and other types of porous media is essential to a wide variety of phenomena and processes in engineering and natural sciences. The effective thermal conductivity of porous media depends strongly on the morphology of the media and the fluid distribution in the pore space, as well as on geomechanical processes such as dilation and microcracking. A precursor to such processes is the deformation of the grains' surface due to the overburden pressure that is dependent on pore‐scale features, such as the surface texture of the grains. We report on the results of the extensive two‐dimensional computer simulations of heat conduction of granular porous media in which the grains have rough, self‐affine fractal surface, and water partially saturates the pore space, and the media are subjected to an external compressive pressure. The Young's modulus of the grains and the fractal dimension of their surface profile dictate the contact area for the deformation. The spatial distribution of the fluids and the resulting saturation is generated by a lattice Boltzmann method, while the conduction process is simulated by a thermal lattice Boltzmann. Two essentially linear dependence of the effective thermal conductivity on the water saturation emerge, separated at a critical water saturation S c that signals the formation of percolation clusters of water‐filled pores. One interval, below S c , is dominated by heat conduction through the solid phase, assisted by conducting water globules that bridge the noncontacting grains. The second interval emerges above S c in which the water‐filled pores create a percolating conductive cluster that dominates heat conduction through the medium. The effect of various factors on the conduction process is studied in detail.
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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 | 0.001 | 0.000 |
| 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".