CFD Friction Factors Verification in an Underground Mine
Bibliographic record
Abstract
Friction factor determination is necessary to model the ventilation system in an underground mine. These parameters were obtained by means of in situ measurements in two potash mines between 2013-2015 [1-2], obtaining an adjusted model to the reality in terms of airflow distribution. In this study, the friction factors were verified by means of a CFD software. The simulation of the air behaviour inside the mine drifts was done using a multiphysical numerical model of computational fluid dynamics (CFD). The well-known FLOW-3D software was applied to numerically solve Navier-Stokes equations for solution domains, introducing deviations and mean values of airflow and cross-sections. A standard k-Ypsilon model was used to estimate turbulence flow. These models are based on the fluid volume method and are capable of simulating ventilation flow conditions. Results obtained by this method displayed similar friction factors to the values obtained from in situ measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".