Heat Transfer Improvement Using Three Types Novel Turbulators Inserts with Two Pitch Ratios in Double Pipe Heat Exchanger
Bibliographic record
Abstract
The present work experimentally investigates the effect of novel turbulators on improving heat exchanger (HE) performance.Tests are conducted by insertion of three types of turbulators, 3PS, 4PS, and 5PS, including two pitch ratio PR (5-3.76).The results showed that the enhancement in heat transfer (HT) when using 5PS was more than the remaining two types.The greatest worth was at the lowest pitch ratio.The outcomes of experiments showed that the enhancement in the Nusselt number for each type of 5PS, 4PS and 3PS for small pitch ratio was 194%, 177% and 164% more than that of the plain tube, correspondingly.Also, the thermal performance factor, friction factor, and Nusselt number are all increased as the pitch ratio decreases.As a result, the small pitch ratio delivers a more considerable rate of HT and lower loss in friction.In addition, the results showed that the factor of thermal performance was more significant compared to unity for each type studied, and the maximum value of thermal performance factor acquired at η=1.54 is accomplished for the 5PS turbulators.The Correlations between the friction factor and Nusselt number were evolved for the range of Reynolds number (Re) of 12385 to 24766.The most relevant results of the evaluated study were presented to aid researchers in understanding the advances in HT enhancement in double pipe HE using novel turbulators inserts.
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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 | 0.001 |
| 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".