Uncertainty assessment of the hydraulics properties surrounding a standing column well with a thermal response test
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".