Experimental measurement of thermal-hydraulic characteristics at low Reynolds number in wavy fin heat exchangers
Bibliographic record
Abstract
Experiments were conducted to predict the thermal hydraulic characteristics for flow in wavy fin heat exchangers. Low Reynolds number (0.1 < Re < 100) and larger Prandtl number (318 < Pr < 573) were considered using high viscous oil. Experimental results were obtained for fifteen different fin specimens having various geometric parameters. The corrugation ratio ranged from 0.1 to 0.18, the aspect ratio ranged from 0.07 to 0.67, and spacing ratio ranged from 0.4 to 1.23. -- Experimental results of heat transfer and fluid friction data for all the wavy fins exposed the presence of two flow regimes. These were: 1. the low Reynolds number regime where the flow behaviour is the same as that in rectangular ducts, 2. the laminar boundary layer regime where vortices induced in the wall waviness valley region with the increase of Reynolds number provided a higher heat transfer performance and pressure drop penalty. This could be due to the complex flow pattern in the wavy fin channels. Models for each regime were developed from fundamental solutions of fluid dynamics and heat transfer. Experimental results were compared with both models which suggested a new model. -- Finally, an asymptotic model was developed by combining both analytical models for predicting the Fanning friction factor, f, and Colburn factor, j, which covered a wide range of Reynolds number. Most of the experimental data sets agreed with this model to within ± 25 %.
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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 | 0.001 | 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) | 0.001 | 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".