Numerical Simulation Analysis of Unsteady Temperature in Thermal Insulation Supporting Roadway
Bibliographic record
Abstract
High geothermal hazard is a basic problem that must be solved in deep mining; thereby the research on thermal insulation supporting for high temperature control of deep roadway is increasing. However, the quantitative analysis of its thermal insulation effect is yet to be carried out. By building the physical model and control equations of the thermal insulation supporting roadway and considering heat‐humidity transfer at wall, the temperature field distribution of surrounding rock and airflow is numerically calculated. Based on numerical simulation results, the evolution law of temperature with ventilation time is analyzed at airflow inlet, outlet, and different sections, then the variation law of surface heat transfer coefficient with position and time is obtained. For heat insulation support structure, the results show that it is not obvious to change the distribution law of temperature field, but it is effective to weaken the convection heat transfer between surrounding rock and airflow. In the main airflow area, the rate of heat exchange gradually decreases with the heat exchange becoming more and more sufficient; in boundary layer, the airflow temperature quickly transits from the wall temperature to that of the main airflow area because of intense collisions of airflow masses, so the mechanism of temperature change is different. The surface heat transfer coefficient well reflected the unstable heat‐humidity transfer, especially in the beginning of ventilation or at airflow inlet. Therefore, the heat insulation supporting structure is helpful to the auxiliary cooling of high temperature mine.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".