Investigate effect of the pitch and helical fins height on the hydrothermal performance of the double‐pipe heat exchanger
Bibliographic record
Abstract
Abstract This study explores the impact of pitch and helical fin height on the hydrothermal performance of a two‐tube heat exchanger. Both numerical and experimental analyses were employed to investigate how different fin geometries influence pressure drop, heat transfer efficiency, and overall system performance. Six new models, denoted as Models B to G, featuring various helical fin configurations with distinct heights and angles using smooth copper plate fins, were developed alongside the traditional model (Model A), while maintaining the tube specifications of Model A. The findings demonstrate that helical fin angle and height play a significant role in shaping the thermal and hydrodynamic characteristics of the heat exchanger. Optimal fin configurations were identified to enhance heat transfer effectiveness while minimizing pressure losses, offering valuable insights for the design of efficient two‐tube helical fin heat exchangers and underscoring the importance of geometric parameters in enhancing hydrothermal performance. The evaluation of the seven models under consistent conditions revealed notable enhancements. For instance, Model D exhibited a Nusselt number 56.5% higher than the conventional model, while Model F displayed a 51.84% increase. Models D and F achieved variation coefficients in the overall performance factor (OPF) of 1.63 and 1.12, respectively, surpassing the traditional model. Additionally, two equations derived from multiple regression analysis were formulated to establish relationships between the Nusselt number and friction factor with parameters such as the Reynolds number, Prandtl number, and fin pitch/height ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".