Computational fluid dynamic modelling of the Frood-Stobie ice stope thermal storage for mine ventilation heating
Bibliographic record
Abstract
Deep mines are subject to increased heat loads from the ventilation air, which undergoes auto-compression and increases approximately 1°C per 100 m. Also, mines located in sub-arctic climates require the mine ventilation air to be heated in winter to ensure that icing does not occur within the ventilation shaft. Ice stopes, a system by which ice is created underground by spraying warm return service water onto the cold incoming air in winter, can be utilised for both heating and cooling. The ice storage can be maintained till cooling is required in summer, at which point the ice is melted and the resulting chilled water is similarly sprayed onto the oncoming ventilation air to cool it down, in a bulk air cooler. Computations fluid dynamics simulation, using ANSYS Fluent, was conducted to allow more control on the system and optimise the ice creation within the stope. Simulation results showed higher snow yields and heat transfer efficiencies in colder temperatures with simulations conducted for -5 to -30°C. The maximum air temperature which could be achieved at the stope air outlet, while still resulting in the water particles being fully frozen, was approximately -2.2°C. A linear correlation could be derived between the optimal water flow rate required (for maximum heating and ice fraction of one) and the inlet air temperature, allowing some control on the system’s performance. Future work will concentrate on establishing the best water spray parameters to melt the ice within the stope and produce chilled water to be used in the bulk air cooler.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".