Pressure Drop and Heat Transfer Characteristics in a Microchannel with Pin-Fins
Bibliographic record
Abstract
Pin-fin heat sinks are now considered one of the possible solutions for the thermal management of small-scale devices requiring high heat dissipation rates. Pin-fins with fixed diameters, different heights and spacing were numerically investigated in the current study for a range of Re=200-1000. The micro-channel cross-section with pins at the bottom surface measures 55 mm in length and has a cross-sectional area of 1 mm x 1 mm. The fin height ranges from 0.2 to 0.8 mm and the distance between pin-fins ranges from 3-6 mm. The fins had a circular cross section 0.25 mm in diameter. The Box-Behknen method was used to determine the number of numerical runs based on the parametric range of pin height and spacing and the Re number. Input data and corresponding outputs were presented using the Genetic Aggregation Response Surface Methodology. An optimum pin height and spacing in terms of heat transfer rates was obtained. It has been observed that at the optimum design, considering the highest Performance Evaluation Criteria (PEC) value the microchannel with pin-fins, can provide an enhancement of 364% in heat transfer rates compared to the microchannel without pins, while the corresponding increase in pressure drop reaches up to 162%. Correlations are proposed for heat transfer and pressure drop calculations able to predict the numerical results mostly within 10%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".