On the Reduction of Wall Temperature Non-Uniformity of a Three-Dimensional Experimental Apparatus
Bibliographic record
Abstract
This paper presents a detailed analysis that was performed for the design of a “uniform” temperature boundary condition imposed on a boundary of a three-dimensional cubical experimental apparatus for benchmark natural convection heat transfer study. The three-dimensional experimental apparatus was constructed with plates which were assembled to act as boundary conditions to the enclosure walls. Test measurements revealed that temperature non-uniformity along one of the plates (boundary) was significant enough that the benchmark study could not be carried out to the desired accuracy of about 1% error. A subsequent detailed mathematical analysis revealed that the temperature non-uniformity on the plate was a result of the effect of thermal spreading/constriction resistance. Modifications to the original design of the apparatus were made to reduce the temperature non-uniformity on the plate by adding a heat source around the plate where the uniform temperature setting was desired. Before the addition of this heat source, a careful mathematical analysis shows a significant reduction in temperature non-uniformity from about 4% (based on the initial design) to less than 1% (for the modified design). By examining the temperature difference between two locations on the plate, the predicted temperature difference obtained through mathematical analyses show excellent agreement with the measured temperature difference.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".