Hydrogeothermal characterization and modelling of a standing column well experimental installation
Bibliographic record
Abstract
Standing column wells (SCW) are efficient ground heat exchangers that offer promising potential for integration in dense urban areas.Recent years have witnessed a growing interest in SCWs, resulting in the development of various simulation models incorporating heat transfer, groundwater flow and geochemical reactions within the well and the surrounding ground.However, these models commonly use a configuration that involves pumping at the bottom of the well and reinjection from its top, which can lead to installation and maintenance difficulties in deep wells.Furthermore, very few SCW models have been validated against reliable field data.This paper presents an original finite element model coupling advection-diffusion of heat and groundwater flow within a top pumped SCW and its surrounding ground as well as the experimental setup used for its validation.Within the scope of this study, experimental data obtained after an extensive field characterization campaign and a thermal response test performed with a large-scale geothermal laboratory were used directly as inputs in the numerical model.Experimental validation shows that without any calibration procedure, the model reproduces the experimental inlet and outlet groundwater temperatures with a mean absolute error of 0.14 °C.It is also shown that the placement of the pump at the top of the well offers a more practical design that has minor impact on the thermal performance of the system.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".