The influence of local hydrogeological conditions on SCW operations
Bibliographic record
Abstract
Standing column wells (SCW) are efficient ground heat exchangers that can provide significant costs and space savings compared to the conventional closed- and open-loop configurations (Deng O’Neill et al. 2006, Pasquier et al. 2016). These systems operate by pumping and recirculating groundwater in a deep (100-500 m) and uncased vertical borehole. Their performance can also be enhanced during peak demand periods by discharging or “bleeding” part of the pumped water to stimulate advective heat transfer towards the well. This strategy has shown to sustain thermal loads up to 180-240 watts per meter of water column in areas with suitable hydrogeological conditions (Orio 1999, Nguyen et al. 2020, Beaudry et al. 2022). The purpose of this presentation is to demonstrate the strong influence of local hydrogeological conditions on SCWs thermal behavior, and thus the importance of integrating field tests in the design process. To this end, the methodology and results of field investigations conducted on three real SCWs located in different hydrogeological contexts are presented. In all three cases, the field work includes a thermal response test and a pumping test. The results of the first field study suggest that SCWs located in impervious bedrocks rely on purely conductive heat transfer and have their performance enhanced by low borehole thermal resistance. The data obtained at the second site rather shows that in highly permeable aquifers, advection can act as the main heat transfer process, even if bleed is not operated. This phenomenon significantly improves the thermal exchange compared to a purely conductive solution and prevents usual first-order approximations from being used for thermal response test interpretation. At last, results from the third site show that SCWs located in areas with poor hydraulic conductivities can still see significant gains in efficiency by operating only a small bleed of a few liters per minute. References Beaudry, G., Pasquier, P., Marcotte, D., & Zarrella, A. (2022). Flow rate control in standing column wells : A flexible solution for reducing the energy use and peak power demand of the built environment. Applied Energy, 313, 118774. Deng O’Neill, Z., Spitler, J. D., & Rees, S. J. (2006). Performance Analysis of Standing Column Well Ground Heat Exchanger Systems. ASHRAE transactions, 112(2). Nguyen, A., Beaudry, G., & Pasquier, P. (2020). Experimental assessment of a standing column well performance in cold climates. Energy and Buildings, 226, 110391. Orio, C. D. (1999). Geothermal heat pump applications industrial/commercial. Energy Engineering, 96(3), 58‑79. Pasquier, P., Nguyen, A., Eppner, F., Marcotte, D., & Baudron, P. (2016). Standing column wells. In Advances in Ground-Source Heat Pump Systems (p. 269‑294). Elsevier.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".