PREDICTION OF AIRFLOW AND TEMPERATURE FIELD IN AN ICE RINK WITH RADIANT HEAT SOURCES
Bibliographic record
Abstract
Three dimensional mixed convection in an ice rink heated by eight radiant heaters has been simulated numerically using a k-ε Standard model associated to a wall function correction. This large building is modelled by considering or not the presence of radiant heaters and also by considering or not radiation between the internal surfaces. This gives a total of four scenarios. The flow was first considered isothermal and calculation have been carried out with several different grids (from 7×105 to 2.05×106 nodes) allowing especially to fix a uniform step in the length-wise direction. Results indicate the usefulness of the CFD technique as a powerful tool which provides a detailed description of the air flow, the temperature field and the turbulent quantities. The most important results of this work are: -When isothermal case is considered, velocities are localised in the zone of stands, which represent the tenth of the total volume. -The agreement between calculated values and some measured air temperatures is good. The results indicate that in a significant part of the ice rink the air is essentially stagnant and significant air velocities can only be found in the spectator zone and in the top region parallel to the ceiling. Also turbulent kinetic is localised in the same regions. This justifies the use of less complicated turbulent models, by different authors, in this kind of application. - The use of radiant heating results in an important increasing of the temperature field within the ice rink. Compared to the case without heaters, temperature at some heights over the ice increases more than 5°C.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".