Hydro-thermal coupled behaviors in free zones and matrix of porous media: insights from a micro-continuum transport approach
Bibliographic record
Abstract
The intricate coupling of seepage and thermal behaviors in fractured rock masses is highly significant because it affects geological stability and geothermal extraction, and is vital for predicting subsurface fluid and heat flow for sustainable management of underground resources. In this study, a two-phase Darcy–Brinkman–Stokes method is extended to describe hydro-thermal behaviors of fractured soils and rocks. Such an extension makes up for the limitations of previous studies on seepage in rock and soil medium. A novel solver, hybridPorousInterHTFoam, is then developed to simulate such hydro-thermal behaviors in the fractured rock mass. The applicability of the coupled numerical model and corresponding solver is validated by simulating a well-designed experiment. By conducting various numerical simulations, the influence of important factors such as seepage velocity, aperture, conduits shape, and fracture roughness on heat transfer efficiency within fractured rock is investigated. The findings emphasize the substantial control exerted by fluid velocity and fracture aperture on heat transfer within rock masses while demonstrating that fracture roughness has no significant effect on heat transfer with seepage. Additionally, the shape, number, and distribution of conduits contribute to the heat transfer process with seepage in three-dimensional rock masses.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".