Stable–unstable flow of geothermal fluids in fractured rock
Bibliographic record
Abstract
Abstract Density‐driven geothermal flow in 3‐D fractured rock is investigated and compared with density‐driven haline flow. For typical matrix and fracture hydraulic conductivities, haline flow tends to be unstable (convecting) while geothermal flow is stable (non‐convecting). Thermal diffusivity is generally three orders of magnitude larger than haline diffusivity and, as a result, large heat conduction diminishes growth of geothermal instabilities while low mass diffusion enables formation of unstable haline ‘fingering’ within fractures. A series of thermal flow simulations is presented to identify stable and unstable conditions for a wide range of hydraulic conductivities for matrix and fractures. The classic Rayleigh stability criterion can be applied to classify these simulations when fracture aperture is very small. However, the Rayleigh criterion is not applicable when the porous matrix hydraulic conductivity is very small, because stabilizing fracture–matrix heat conduction is independent of matrix hydraulic conductivity. In that case, the numerically estimated critical fracture conductivity is nine orders of magnitude larger than the theoretically calculated critical fracture conductivity based on Rayleigh theory. The numerical stability analysis presented here may be used as a guideline to predict if a geothermal system in 3‐D fractured rock is stable or unstable.
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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) | 0.000 | 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 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".