Thermal Performance Analysis of Micro Pin Fin Heat Sinks under Different Flow Conditions
Bibliographic record
Abstract
Due to microscale effects, the segmented microchannels or micro pin fin heat sinks emerged as a high thermal management solution. In this context, the present work analyzes the influence of different heights of square micro pin fins with an aligned array and investigates their influence on pressure drop and heat transfer behavior. The HFE-7100 is used as the working fluid, and the pressure drop and surface temperature behavior are analyzed for different mass fluxes and inlet subcooling. The single-phase flow was analyzed numerically using the computational fluid dynamics (CFD) software ANSYS FLUENT® for comparing the simulation results with the experimental data, showing that the highest micro pin fins configuration provides a more uniform and lowest wall temperature distribution compared to the lowest configuration. There is a good agreement between the experimental results and the numerical analysis, with a mean absolute error of 6% for all the considered parameters. For the two-phase flow condition, experimental tests were performed, and for the highest subcooling, an increase in mass flux causes an enhancement in the heat transfer for low heat flux; by increasing heat flux, there is a gradual predominance of boiling heat transfer over convection as the heat transfer mechanism. The pressure drop drastically increases with the vapor amount flowing into the system, regardless of the pin fin height; the boiling curves for the higher fin height show a much smaller slope and a smaller wall superheat than the fin with the smallest height, and consequently, a high heat transfer performance. A larger region of the heat sink is filled with vapor for lower inlet subcooling temperatures, degrading the heat transfer performance compared to higher inlet subcooling temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".