Turbulent flow and heat transfer enhancement for straight circular and twisted oval tubes with different dimple shapes
Bibliographic record
Abstract
Dimples on a tube surface can cause flow separation and generate secondary flows over the upper half of the dimples. The secondary flows disrupt the velocity and thermal boundary layers, which can enhance heat transfer rates. This work makes several contributions: it numerically investigates the effects of twisting and dimpling a straight smooth circular tube (SSCT) on hydraulic and thermal performance, examines the influence of different dimple geometries on SDCTs and TDOTs, and compares in-line and helical dimple arrangements in SDCTs regarding their thermo-hydraulic characteristics. Therefore, this study aims to examine and compare the hydraulic and thermal performance of SDCTs and TDOTs with three dimple shapes: spherical, elliptical, and teardrop. For SDCTs, spherical and elliptical dimples are implemented in both helical and in-line configurations, while for TDOTs, spherical, elliptical, and teardrop-shaped dimples are employed in an in-line configuration. The study considers Reynolds numbers (Re) from 5,000 to 25,000 at a fixed Prandtl number of 1.7, using the performance evaluation factor (PEF) to compare the thermo-hydraulic performance of various cases with that of an SSCT. According to the simulations, the SDCT with spherical dimples in a helical pattern enhances the convective heat transfer coefficient by as much as 2.1 at Re = 5,000 compared to the SSCT, while the corresponding friction factor ratio increases by only 7.85 at Re = 25,000. In contrast, the SDCT with helically arranged elliptical dimples attains the highest PEF value of 1.15 at Re = 5,000.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".