Thermal conductivity of granular porous media: A pore scale modeling approach
Bibliographic record
Abstract
Pore scale modeling method has been widely used in the petrophysical studies to estimate macroscopic properties (e.g. porosity, permeability, and electrical resistivity) of porous media with respect to their micro structures. Although there is a sumptuous literature about the application of the method to study flow in porous media, there are fewer studies regarding its application to thermal conduction characterization, and the estimation of effective thermal conductivity, which is a salient parameter in many engineering surveys (e.g. geothermal resources and heavy oil recovery). By considering thermal contact resistance, we demonstrate the robustness of the method for predicting the effective thermal conductivity. According to our results obtained from Utah oil sand samples simulations, the simulation of thermal contact resistance is pivotal to grant reliable estimates of effective thermal conductivity. Our estimated effective thermal conductivities exhibit a better compatibility with the experimental data in companion with some famous experimental and analytical equations for the calculation of the effective thermal conductivity. In addition, we reconstruct a porous medium for an Alberta oil sand sample. By increasing roughness, we observe the effect of thermal contact resistance in the decrease of the effective thermal conductivity. However, the roughness effect becomes more noticeable when there is a higher thermal conductivity of solid to fluid ratio. Moreover, by considering the thermal resistance in porous media with different grains sizes, we find that the effective thermal conductivity augments with increased grain size. Our observation is in a reasonable accordance with experimental results. This demonstrates the usefulness of our modeling approach for further computational studies of heat transfer in porous media.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".