Constructal Peripheral Cooling of a Rectangular Heat-Generating Area
Bibliographic record
Abstract
The present paper determines numerically the optimal geometric parameters for the maximal peripheral cooling of a two-dimensional rectangular solid body with internal heat generation. The objective is to maximize the thermal global conductance (i.e., minimize the hot spot temperature on the solid body) by using the minimal cooling space. The flow is conducted around the heated solid body by a sequence of channels of independent width Di, where 1⩽i⩽4. Each configuration is free to morph itself in two directions: (a) the number of cooling channels, and (b) the aspect ratio of the heated body λ. The numerical results show that a number of cooling channels greater than one (i.e., n>1) is profitable in terms of thermal performance when the heated body resembles a square (i.e., λ∼1). However when λ is free to vary, the thermal performance does not necessarily increase with the number of cooling channels. The paper also discusses the importance to allow each configuration to morph itself in multiple directions by comparing the thermal performance of similar configurations with different number of degrees of freedom. Scale analysis is used to verify the results obtained numerically for all the degrees of freedom considered. The numerical results agree with the scaling trends.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".