Heat flux variations of an infrared tube heater at low and medium firing rates
Bibliographic record
Abstract
Radiant heat flux has been measured on a floor from an infrared tube heater using a calibrated heat flux sensor. Knowledge of heat flux in combination with other comfort parameters, such as ambient air temperature, air velocity, metabolic rate, and draft, will help to determine and establish thermal comfort zones. Iso-flux curves were determined for a region where the heat flux varies from 31.69 BTU/hr.ft2 to 63.40 BTU/hr.ft2 (100 W/m2 to 200 W/m2), with this range being selected as an illustrating example. A medium firing rate in the range of 130,000 BTU/h (38.10 kW) and a low firing rate in the range of 60,000 BTU/h (17.58 kW) were used. Heat flux was measured for heights of the tube-reflector assembly above the floor from 100 in. (2.54 m) to 12 ft (3.66 m). Results show that the average and maximum heat flux measured decreases with the height for both firing rates. The heat flux measured on the floor from a low firing rate heater is less than that from a high firing rate heater. Numerical simulation has also been performed to predict the experimental measurements, and results show good agreement between the two techniques. By increasing the height of infrared tube heaters having low firing rates, the same heat flux range can nearly be achieved as for infrared tube heaters having medium firing rates installed at lower heights. In addition, bounded coverage areas on a floor for the heat flux zones have also been calculated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".