Evaluation of Coupled Thermal and Hydraulic Relationships Used in Simulation of Thermally-Induced Water Flow in Unsaturated Soils
Bibliographic record
Abstract
This paper focuses on the role of coupling between the thermal and hydraulic properties of soils on simulations of the distribution in temperature and degree of saturation surrounding a geothermal heat exchanger in an unsaturated soil deposit. This information is relevant to the simulation of geothermal heat storage systems in unsaturated soil layers. The simulations involved heat transfer coupled with water flow in both liquid and vapor forms, and were performed considering the properties of sand, silt, and clay. A water table was fixed at a depth of 20 m below the extent of the heat exchanger, which means that the different soils considered have different initial hydraulic conditions along the length of the heat exchanger. After heat injection for 90 days at the same heat injection rate, the ground temperatures varied significantly with the soil type, with the clay showing the greatest changes in temperature despite having a lower thermal conductivity than the sand. The clay layer also experienced the greatest changes in degree of saturation and had the highest heat transfer due to latent heat transfer, likely because the initial degree of saturation was higher in this soil. The results indicate that a larger change in degree of saturation may occur in soils with a higher initial hydraulic conductivity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".