Numerical and experimental simulation of spontaneous combustion of coal
Bibliographic record
Abstract
In this paper, our main aims are to make up for the defects of large-scale coal spontaneous heating tests on trial methods.An unsteady-state numerical model is developed to simulate self-heating in a largescale coal spontaneous heating reactor with experimental conditions.The self-heating process of coal is reproduced successfully, and the main characteristics of nonlinear heating and windward movement of the fire source are obtained during the whole process.By comparison, the simulated and experimental results of the Tianchi coal samples show a good agreement with the temperature rise features and fire source positions.This confirms the validity of the model.Based on the model, a sensitivity analysis is constructed to investigate the influence of the external environment on coal spontaneous heating.The main contents include (1) heat dissipation surroundings; (2) air quantity; (3) air direction.The simulated conclusions contribute to optimizing the experimental parameters and obtaining the shortest spontaneous combustion period.In addition, the model is found to be of great significance for predicting the fire source temperature and position in practical coal mine conditions.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".