Uncertainty analysis of operational conditions in selective artificial ground freezing applications
Bibliographic record
Abstract
Artificial ground freezing (AGF) systems are susceptible to uncertain parameters highly affecting their performance. Particularly, selective artificial ground freezing (S-AGF) systems involve several uncertain operational conditions. In this study, uncertainty analysis is conducted to investigate four operational parameters: 1) coolant inlet temperature, 2) coolant flow rate, 3) pipes emissivity, and 4) pipes eccentricity. A reduced-order model developed and validated in our previous work for field-scale applications is exploited to simulate a total of 5,000 cases. The uncertain operational parameters are set according to Monte Carlo analysis based on field observations of a field-scale freeze-pipe in the mining industry extending to 460 m below the ground surface. The results indicate that the freezing time can range between 270 and 350 days with an average of 310 days, whereas the cooling load per one freeze-pipe ranges from 90 to 160 MWh, with an average of 129 MWh. Furthermore, it is observed that the freezing time and energy consumed are mostly dominated by the coolant inlet temperature, while energy dissipated in the passive zone (where ground freezing is not needed) is mostly affected by pipes emissivity. Overall, the conclusions of this study provide useful estimations for engineers and practitioners in the AGF industry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".