Investigation of airflow through porous zones: Integrating computational fluid dynamics modeling into mine ventilation network simulation
Bibliographic record
Abstract
Using mine ventilation network (MVN) software to model airflow through porous zones in underground mining operations is arduous due to its complex and dynamic nature. However, accurately measuring airflow is essential to control underground mine air quantity and quality. This study integrated the computationally expensive conjugate porous media model into versatile MVN software to efficiently analyze airflow through the porous zone. In MVN software, a novel friction factor coefficient was compiled into the broken rock-filled drawpoint model, which was later verified against the three-dimensional (3D) computational fluid dynamics model. Several simulations were conducted to ensure model reliability by varying the porosity and broken rock diameter of the porous zone. Results demonstrate that the novel friction factor coefficient can accurately predict airflow through porous media using MVN software and reduce computation time by > 99% compared to the 3D solver. In addition, sensitivity analyses were conducted to assess the effects of various factors on the system. This method enables mine ventilation engineers to effectively plan the rapidly changing underground MVN.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".