Effective Thermal Conductivity of Tight Porous Media
Bibliographic record
Abstract
Abstract Fluid and heat flow in complex porous media is widely used in various sciences such as medicine, environmental engineering, geoscience, and petroleum engineering. Understanding flow and heat transfer is may be difficult unless the pore geometry is well understood. The focus of this study is the determination of effective thermal dispersivity by both conduction and forced convection. For this purpose, experimental measurements and simulation results of heat and momentum transfer are presented. Experiments are conducted in a sand pack with various surrounding temperatures and injection rates. A 3-D heat transfer model was developed with and without fluid flow with three components. First component is mobile or stagnant fluid in the pore space, second component is the sandstone continuous matrix, and the third component is another solid that has a separate thermal conductivity and will mimic the constant temperature boundary. The transfer of the heat through the solid and fluid and also from the solid to the fluid is related to the composition and connectivity of the solid in the geometry. However, when there is forced convection, the key factor is the Peclet number. The velocity of the fluid can change the effective thermal conductivity up to four orders of magnitude. For the experiments, a sand pack 48cm long was used at temperatures of 40 - 60 °C and water injection rates of 1 - 100 cc/min. The model is augmented by numerical calculations of heat transfer parameters such as effective thermal conductivity and effective thermal advection by monitoring the Peclet number of the process. The variability of thermal dispersion of tight systems under specific composition and pore topology was presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".