Performances of helical baffle heat exchangers with different baffle assembly configurations
Bibliographic record
Abstract
The flow and heat transfer characteristics of helical baffle heat exchangers with diverse inclined angles and baffles, but similar baffle pitch and tube layout, were numerically simulated, three using non‐continuous trisection baffles, two using non‐continuous quadrant baffles, and one using a continuous helical baffle. The results show that, under the same operating conditions, the 20°TCO (trisection circumferential overlap baffles with 20° inclined angle) structure can significantly enhance shell side heat transfer with strong Dean vortex “secondary flow“ and restrained V‐notch leakage, because the shapes of trisection baffles are very suitable to equilateral triangle tube layout and there is a row of tubes to dampen the leakage flow in each circumferential overlapped area of adjacent baffles. The shell side Nusselt Number Nu o and comprehensive index ( Nu o / Eu z,o 1/3 ) of 20°TCO structure are 18.31 %, 25.82 %, 5.93 %, 6.36 %, and 15.04 %, and 15.43 %, 18.47 %, 5.30 %, 3.91 %, and 11.10 % higher than those of the 20°TEE (trisection end‐to‐end baffles with 20° inclined angle), 36.2°TMO (trisection middle overlap baffles with 36.2° inclined angle), 18°QCO (quadrant circumferential overlap baffles with 18° inclined angle), 18°QEE (quadrant end‐to‐end baffles with 18° inclined angle), and 18.4°CH (continuous helical baffle with 18.4° helical angle) structures, respectively.
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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 | 0.000 |
| 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".