Influence of flow attack angle on the heat transfer and pressure drop of hook-shaped fins and dimples
Bibliographic record
Abstract
The thermal – hydraulic performance of pin fin arrays can be adjusted to suit specific applications by altering the flow attack angle to reduce pumping power or improve heat transfer. This study investigates the effect of attack angle on hook-shaped pins and dimples. Numerical simulations were carried out to quantify the effect of attack angles ( α ) ranging from 0° to 90° in increments of 22.5° Water served as the working fluid, and the analysis spanned Reynolds numbers ( Re ) from 600 to 5000. The thermal performance of the array was evaluated using the average Nusselt number ( Nu avg ), hydraulic performance was assessed via the average friction coefficient ( f avg ), and the combined thermal-hydraulic performance relative to a bare surface was quantified by the overall thermal performance ( η o ). Results demonstrate that introducing an attack angle significantly enhances the thermal performance of the array. Specifically, an α of 22.5° led to a 44 % improvement in Nu avg at Re = 5000. Furthermore, certain configurations, such as α = 45°, 67.5°, and 90°, simultaneously improved heat transfer and reduced pressure drop. The findings demonstrate the potential of optimizing the array’s orientation for specific applications to enhance performance without increasing pumping power and, in some cases, to even reduce it.
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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 | 0.000 |
| 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) | 0.000 | 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".