Forced convection boiling heat transfer inside helically-coiled heat exchanger
Bibliographic record
Abstract
Abstract For decades, forced convection boiling heat transfer has been considered as one of the most efficient type in the heat transfer mechanism. It has been widely utilized in heat transfer equipment of various industries. Meanwhile, helically-coiled heat exchanger has been commonly utilized in numerous industrial applications. Thus, there is an interest to utilize forced convection boiling heat transfer in helically-coiled heat exchanger. Boiling phenomenon inside helically-coiled tube is more complex as compared to the straight tube due to the secondary flow induced by centrifugal force. This study investigates flow boiling heat transfer performance of water-vapor inside helically-coiled tube by using computational fluid dynamic approach. A Eulerian-Eulerian two-fluid model is used to capture interphase exchange forces and heat transfer between liquid and vapor phase. Wall boiling model is adopted to take into account the boiling condition in the vicinity of the wall. The developed model is then validated against the previously published experimental data. Good agreement for the outlet vapor quality and pressure drop between numerical study and experimental measured value is achieved. The result reveals that the boiling starts at the inner wall of the tube (φ=450°) due to the presence of secondary flow induced by coil curvature. The relationship between HTC and vapor quality along the helically-coiled tube are discussed and evaluated. This study serves as a guideline for future study on forced convection boiling heat transfer in the helically-coiled tube.
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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 | 0.000 |
| 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) | 0.001 | 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".