Uncertainty assessment of the hydraulics properties surrounding a standing column well with a thermal response test
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Bibliographic record
Abstract
The standing column well (SCW) is known for being a highly efficient ground heat exchanger as it relies on both conduction and advection heat transfer processes. Therefore, the interpretation of a thermal response test (TRT) is strongly influenced both by the hydraulic and thermal properties surrounding the SCW. In this study, it is shown that a TRT can allow identifying the thermal and hydraulic properties around a SCW. The analysis is conducted in a Bayesian framework allowing an accurate and robust identification of the hydraulic properties and their uncertainties. A closed-form expression of the likelihood is used to consider the autocorrelation of the residuals between observed and simulated temperatures. A coupled numerical model is used to generate a training database for an artificial neural network. Then, the latter serves as an emulator of the SCW's short-term g-function given various input parameters. A case study is presented based on a 100-hour TRT performed on a SCW built at a demonstration site located in the city of Mirabel, Canada. For the specific site studied, hydraulic properties were identified with an uncertainty of less than 30 % at a two-sigma level. Such important results lead to more appropriate and efficient design of SCWs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it