Investigation of Overlapped Twisted Tapes Inserted in a Double-Pipe Heat Exchanger Using Two-Phase Nanofluid
Bibliographic record
Abstract
nanofluid in a counter flow double-pipe heat exchanger equipped with overlapped twisted tape inserts in both inner and outer tubes. Two models of the same (co-swirling twisted tapes) and opposite (counter-swirling twisted tapes) angular directions for the stationary twisted tapes were considered. The computational fluid dynamic simulations were conducted through varying the design parameters, including the angular direction of twisted tape inserts, nanofluid volume concentration, and Reynolds number. It was found that inserting the overlapped twisted tapes in the heat exchanger significantly increases the thermal performance as well as the friction factor compared with the plain heat exchanger. The results indicate that models of co-swirling twisted tapes and counter-swirling twisted tapes increase the average Nusselt number by almost 35.2-66.2% and 42.1-68.7% over the Reynolds number ranging 250-1000, respectively. To assess the interplay between heat transfer enhancement and pressure loss penalty, the dimensionless number of performance evaluation criterion was calculated for all the captured configurations. Ultimately, the highest value of performance evaluation criterion is equal to 1.40 and 1.26 at inner and outer tubes at the Reynolds number of 1000 and the volume fraction of 3% in the case of counter-swirling twisted tapes model.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".