Experimental investigation of the thermal–hydraulic performance of hook-shaped fins and dimples
Bibliographic record
Abstract
• Novel and cost-effective extended surfaces, GRIPMetal, are investigated. • Experiments were conducted at low Re . • Thermal performance of GRIPMetal peaked at the smallest tip-clearance. • Empirical correlations predicted performance with errors < 4.1 % ( Nu h ) and 10 % ( f h ). • GRIPMetal outperformed existing surface enhancement techniques by 13 %−91 %. In forced convection, arrays of extended surfaces are commonly used to increase heat transfer rates because they increase specific surface area, improve fluid mixing, and generate turbulence which results in higher heat transfer coefficients. The objective of this work is to comprehensively quantify the heat transfer coefficient and pressure drop of arrays of hook-shaped fins and dimples, trademarked as GRIPMetal. Experiments were conducted by pumping water through a rectangular channel to cool heated GRIPMetal surfaces on one side. The channel height was varied to adjust the tip clearance above the arrays. Three sizes of GRIPMetal arrays were tested across a range of Reynolds numbers ( Re ) from 600 to 12,000. The Nusselt number ( Nu ) and friction factor ( f ) were used to evaluate the thermal–hydraulic performance of the arrays and to quantify their effectiveness relative to a flat surface. The overall thermal–hydraulic performance factor ( η o ) was employed, and empirical correlations were developed to describe Nu and f for the arrays. All the GRIPMetal arrays exhibited higher thermal performance than a smooth surface, with Nu ranging from 2.4 to 5.7 times higher, depending on the array type, Re , and channel height. However, the pressure drop was also higher than that of a smooth channel. Despite the pressure drop penalty, the overall thermal-hydraulic performance of the arrays was always higher than that of the smooth surface—the lowest performance factor was 1.4 for the mini arrays. GRIPMetal outperformed other similar heat transfer enhancement techniques explored in the literature, suggesting its potential to serve as a low-cost heat transfer enhancement method.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".