Cylindrical models of heat flow and thermo-elastic stresses in underground tunnels
Bibliographic record
Abstract
Abstract Purpose The trapped geothermal heat in the infinite rock mass through which mine tunnels are excavated is a great threat to the safety of personnel and mine operating equipment in deep underground hot mines. In order to lessen the temperature inside the tunnel a considerable amount of energy is being spent by the way of using ventilation and cooling systems to dissipate the heat. However, operational costs of the system rise quite considerably, especially as the mines get deeper. Shotcrete is used both as a structural lining and as an effective insulation to reduce the heat load on the ventilation and cooling system within such tunnels. The paper aims to discuss these issues. Design/methodology/approach In order to analyse this problem of heat flow and thermal stresses and their time dependent pattern, several cylindrical models, in both analytical and numerical forms, are discussed and compared in this paper. Findings This study shows the validation of ABAQUS® software to predict the time dependent temperature and the thermal stresses in mine tunnels through the comparisons with the available analytical models. Further, thermal insulation effects of shotcrete are also evaluated with these theoretical models and it is found that all the models gave results in close agreements with one another. Originality/value Therefore, this study provides the theoretical proof for advantages in applying shotcrete as the thermal insulation layer in underground mines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".