Interpretation of a thermal response test in a Bayesian framework to infer the hydraulic properties surrounding a standing column well
Bibliographic record
Abstract
The main objective of a thermal response test is to identify the thermal properties near ground heat exchangers. In standing column wells, the groundwater acts as the heat carrier fluid, rending hydraulic and thermal responses strongly related. Consequently, the identification of hydraulic properties through thermal response test is possible. In this study, the Bayesian framework is employed to infer joint and marginal distributions of thermal and hydraulic properties based on the experimental data gather during a 160-hour thermal response test. This test was conducted on a standing column well constructed in a layered aquifer having locally high groundwater velocities. To simulate the thermal response test, a finite element numerical model is first used to generate a training dataset. Then, a neural network is trained to emulate the ground heat exchanger response in fractions of seconds. This strategy accelerates the computation of the Markov chain Monte Carlo slice sampling algorithm. A closed-form expression of the likelihood that takes into account the temporal correlation of the data is also used to obtain additional speedup. Main results indicate an accurate estimation of the hydraulic properties in comparison to results found by complementary experimental pumping tests completed at the same site. In accordance with the high velocities observed while drilling at this particular site, high hydraulic conductivity is obtained at the bottom of the borehole. It was possible to draw robust statistical distributions of hydraulic properties with only a thermal response test, while also assessing the thermal properties and their uncertainties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".