Heat Transfer Coefficient Distribution of W and Broken W Turbulators at High Reynolds Numbers
Bibliographic record
Abstract
Abstract An experimental and numerical study of the convective heat transfer enhancement provided by two rib families (W and Broken W) is presented, covering Reynolds numbers (Re) between 300,000 and 900,000 in a straight channel with a rectangular cross section (AR = 1.29). These high Reynolds numbers were selected for the current study since most data in the available literature typically pertain to investigations at lower Reynolds numbers. The objective of this study is to assess the local heat transfer coefficient (HTC) enhancement (compared with a smooth channel) and the overall thermal performance, taking into account the effect of increased roughness on the friction factor, of a group of W-shaped turbulators over a wide range of Reynolds numbers. Furthermore, the effects of increasing the rib spacing on the thermal performance of the Broken W configuration are presented and discussed. The numerical results are compared against heat transfer measurements obtained using the transient liquid crystal (TLC) method. The research shows that for the Broken W turbulators, increasing the Reynolds number is associated with an overall decrease of the thermal performance while the thermal performance of the W configuration is relatively insensitive to Reynolds number. Nevertheless, the Broken W configuration delivers higher thermal performance and heat transfer compared with the W configuration for the range of Re investigated. The Broken W configuration with a pitch spacing of 10 times the rib height was shown to provide the optimal thermal performance in the configurations investigated here.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".