Experimental assessment of inter-well reinjection in standing column wells by analysis of transfer functions obtained from non-stationary deconvolution
Bibliographic record
Abstract
Standing column wells are semi-open-loop ground heat exchangers that can achieve highly efficient thermal exchange rates through the strategic control of the pumping and bleed flow rates. The management of groundwater discharges associated with bleed use remains a challenge. A solution is inter-well reinjection , which proposes to imbalance the return flow rates between the standing column wells. This approach has been shown to be more efficient than fully balanced recirculation, although a direct comparison with a conventional bleed operation has not yet been conducted. To provide a robust evaluation of inter-well reinjection performance, a 35-day-long experiment is conducted on five standing column wells connected to a real building. The experimental transfer functions representing the operating modes tested (full recirculation, bleed, and inter-well reinjection) are evaluated using a non-stationary deconvolution algorithm and their adequacy with the conceptual site model is verified by comparison with numerical transfer functions obtained in a Monte-Carlo experiment. The results indicate that inter-well reinjection leads to a 10% higher thermal efficiency in the scenarios tested compared to full recirculation, albeit with a slightly reduced performance compared to typical bleed use. This confirms the potential of inter-well reinjection for boosting the efficiency of thermal exchange in SCWs while facilitating groundwater management and avoiding the installation of costly injection facilities. The methodology used to evaluate the experimental transfer functions is also found to be robust, as it allowed the reproduction of the measured temperatures with a root mean square error of 0.04 ° C . Lastly, comparison of the experimental transfer functions with the Monte-Carlo experiment suggests that the accuracy of the conceptual model could be improved.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".