Numerical and experimental study of transient conjugate heat transfer in helical closed‐loop geothermal heat exchangers for application of thermal energy storage in backfilled mine stopes
Bibliographic record
Abstract
The geothermal potential available from deep underground mines has yet to be utilized. However, stope-coupled heat exchangers (SCHE) are aiming to take advantage of the unused low-grade geothermal energy. Backfilled stopes provide a unique opportunity to install nonlinear heat exchangers, as the geometry is not limited to the shape of a borehole. Helical pipes deliver superior fluid mixing and heat exchange compared to straight pipes, due to the effect of the secondary flow within the helical pipe. The helical closed-loop geothermal heat exchanger enables the backfilled stopes of the mine to be repurposed as thermal energy storage units. This article delves into the experimental results from a unique state-of-the-art laboratory scale helical closed-loop heat exchanger with varying thermophysical parameters. Additionally, a novel conjugate numerical model is developed and its results are validated against the base case of the experimental studies. Additionally, the numerical model is validated in a spatial-temporal sense with thermocouple data from the experimental rig. The numerical model is also applied to a helical SCHE situated within a backfilled stope for the first time. The results of the numerical model suggest that the pumping rate through the SCHE has a significant effect on the heat exchange rate and the overall energy transfer between the SCHE and the backfill. Additionally, the temperature contours from the numerical model suggest that a decreased pitch/helical diameter will increase the storage capacity of the helical SCHE. Overall, an average of 2.5 MW can be stored over the first 4 days of geothermal charging with the investigated full-scale SCHE, boasting a pseudo-steady-state storage rate of 1.7 MW.
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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 | 0.001 | 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".