A novel method to improve the efficiency of a cooking device via thermal insulation
Bibliographic record
Abstract
Abstract We propose a method of finding the transient temperature variation in an insulated cooking device. We also report a means of optimising the thickness of insulation. The cooking device is a double walled cylindrical vessel with spacing of 5–20 mm between the vertical walls (width) and spacing of 560 or 870 mm between top and bottom surfaces (height). The height to width ratio (H/L) is between 28 and 174 and Rayleigh number (Ra) is between 907 and 2.61 × 105. First, an energy balance for the cooking device is established. A correlation is developed to predict the heat transfer coefficient (HTC) as a function of Ra and H/L. The method developed for finding the transient variation in temperature has been tested on two cooking device volumes: 120 and 700 lit. Using the optimised parameters, a reduction in heat loss of 22% and 30%, respectively, is observed. © 2011 Canadian Society for Chemical Engineering
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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 | 0.000 |
| 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".