The Use of a Convection Boundary Condition in the Simulation of a Heat Tracer Experiment Conducted in Bedrock
Bibliographic record
Abstract
Heat transfer experiments conducted in the subsurface are usually interpreted using either analytical or numerical models, which incorporate first-type boundary conditions (specified temperature) to introduce the heat into the solution domain. An alternative approach is to use a third-type boundary condition, often refereed to as a convection bc in the heat transfer literature, which includes a heat transfer coefficient to accommodate the exchange of heat between fluid flowing outside the domain to that inside the domain under potential. To explore the impact of this boundary condition, a semi-analytical model was developed for a linear flow system in a discrete rock fracture with advective heat transfer in the fracture and conductive heat transfer in the matrix. To illustrate the influence of the heat transfer coefficient, the model is applied to the results of a heat tracer experiment conducted in a discrete fracture connecting two boreholes in a crystalline rock, with warm fluid injection in one borehole and passive temperature measurement in the other. The experimental results were also simulated using a similar model having a first-type condition at the injection borehole for comparison. The simulations show that the heat transfer coefficient has a significant influence on the shape of the breakthrough curve and allows for an excellent match with the field data, whereas the model with the first-type condition cannot obtain a match of similar quality.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".