Thermal Enhancement and Hydraulic Characteristics of Helically Coiled Tubes with Spherical Dimples
Bibliographic record
Abstract
This study investigates using helically coiled tubes and dimples as two separate heat transfer enhancement approaches. Accordingly, numerical simulations are used to assess the thermal and hydraulic performance of a helically coiled tube with spherical dimples. Several key geometric variables are examined, including the coil diameter (Dc), dimple pitch (Pd), dimple diameter (dd), and dimple star. These parameters are non-dimensionalized relative to the tube diameter (d), with the non-dimensional coil diameter (Dc/d) ranging from 12.5 to 22.5, the dimple pitch (Pd/d) from 1 to 2, the dimple diameter (dd/d) from 0.1 to 0.3, and the number of dimple stars from 4 to 8, within the Reynolds number (Re) range of 10,000 to 25,000. The numerical simulations predict that the presence of dimples significantly enhances the dimpled helically coiled tube (DHCT)’s hydraulic and thermal performance. Simulations of various DHCT configurations indicate that the Nusselt number (Nu) and friction factor (fr) can reach values as high as 393.7 and 0.363, respectively, under certain conditions. Additionally, compared to a smooth helically coiled tube, Nu and fr can increase by up to 1.91 and 9.95 times, respectively. Moreover, the performance evaluation criterion (PEC) can attain a maximum value of 1.22. From an engineering standpoint, straightforward and dependable correlations are crucial for quickly assessing the performance of newly designed or enhanced equipment. This study develops innovative and accurate correlations using a comprehensive dataset of 324 points, covering a wide range of dimensionless geometric parameters and Re. These correlations help estimate pressure drop, heat transfer improvement, and overall thermal-hydraulic performance in DHCT. Due to their wide applicability and practical significance, these correlations, especially the one developed for the PEC, are expected to make a substantial contribution to existing research and serve as valuable tools for engineering design and analysis.
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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 | 0.001 |
| 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".