Numerical Evaluation of Heat Transfer and Entropy Generation of Helical Tubes with Various Cross-sections under Constant Heat Flux Condition
Bibliographic record
Abstract
The presence of curvature-induced secondary flow in helical pipe which create complex transport phenomena and higher transfer rate has attracted significant attention from both academic and industry. Flow behavior and transport processes in helical tube have been intensively investigated. Nevertheless, most studies were focused on the performance based on first law of thermodynamics with limited studies concerning the performance based on second law of thermodynamics. The objective of this study is to investigate the heat transfer performance of helical tube according to both first and second law. The heat transfer rate and entropy generation of helical tubes with various cross-sections, i.e. circular, ellipse and square, subjected to constant wall heat flux conditions are numerically evaluated by utilizing computational fluid dynamics (CFD) approach. Their performances are compared to those of straight tube with identical cross-section. The results indicate that helical tube provides higher heat transfer at the cost of higher pressure. Moreover, it was found that entropy generation in helical tubes is considerably lower as compared to that in straight tube. Among the studied cross-sections, square has the highest heat transfer albeit having the highest pressure drop and entropy generation for both straight and helical tubes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".