Novel thermosyphon design for underground artificial ground freezing: CFDanalysis
Bibliographic record
Abstract
The Cigar Lake Mine (Saskatchewan, Canada) contains the richest high-grade uranium deposits in the world. Geotechnical concerns and underground water seepage during compels the need for artificial ground freezing (AGF) at a depth of 400-460 meters below the ground surface, where the ore is located and thus called the active zone. The AGF systems are driven by energy intensive refrigeration plants at 300 million dollars annually, which is expected to double in an expansion plan. In this project, we propose an innovative artificial ground freezing technology that runs on sustainable energy resources. Our work improves traditional thermosyphons to meet the needs of underground AGF systems by incorporating air insulation layer in the top passive zone where ground freezing is not needed (which extends for 400 meters in the Cigar Lake Mine) to magnify energy extraction in the active zone. The superior performance of this novel design has been demonstrated mathematically using well-validated lab-scale models based on the conservation principles of mass, momentum, and energy. The results show that the novel design extracts twice the amount of thermal energy as compared to that of traditional thermosyphons. Overall, the proposed thermosyphon design shows great potential as an economic and sustainable alternative for present underground AGF systems.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".