Bibliographic record
Abstract
Artificial ground freezing is a ground sealing and reinforcement technique regularly used in civil and mining engineering. In order to reliably predict the freezing evolution in the porous medium, this research offers two new numerical models allowing the simulation of the global problem of artificial ground freezing. A first model aims at representing the thermo-hydraulic coupled mechanisms associated with the material freezing while a second model focuses on the estimation of heat transfers between a freeze pipe and the surrounding ground. The thermo-hydraulic model, in addition to being thermodynamically consistent, has been verified both with respect to analytical solutions and large- scale experimental results obtained under conditions of high water flow velocity. The pipe-ground model adopts an innovative approach compared with literature. It allows to determine the boundary conditions of the ground freezing models, not readily available in practice, and to optimize the operating conditions of the system thanks to limited simulation times. By the considered assumptions, their reliability and their practicality, these two models are particularly well adapted to industrial sites like the uranium mine Cigar Lake (Canada) which presents two major constraints: the potential presence of high seepage-flow velocities and the strong ground heterogeneity. In these contexts, applications of the two models, jointly used or not, are presented with respect to simple cases and to the industrial case of Cigar Lake. They can be employed to predict the freezing evolution in the ground considering the thermo-hydraulic interactions, to optimize the freezing system, or to evaluate the impact of specific geological, hydrogeological and operating conditions on the freezing progress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".