Hydrothermal performance of turbulent flow in tubes with spherical dimples
Bibliographic record
Abstract
Engineers increasingly utilize dimpled tubes in thermal systems to enhance performance, with spherical dimples demonstrating the most significant impact. Simple and accurate correlations are essential for efficiently assessing the performance of newly designed or improved equipment. Extensive research has been conducted on spherical dimples; however, no study has comprehensively examined the combined effects of geometric parameters such as dimple diameter, dimple pitch, and dimple stars. This study uses numerical simulations to investigate the hydraulic and thermal performance of spherical dimpled tubes with varying geometric parameters, including dimple pitch, diameter, and the stars. New correlations for the Nusselt number ( Nu ), friction factor (fr), and performance evaluation criteria (PEC) are developed as functions of these parameters and the Reynolds number. The dimpled tube is analyzed under a constant heat flux of 10 kW/m 2 . The study finds that increasing dimple pitch decreases Nu , fr, and PEC, with deviations ranging from 3 % to 40.5 %, 18.8 %–109.9 %, and −4.2 % to 12.6 %, respectively. Increasing dimple diameter and the number of stars increases Nu and fr, but the effect on PEC varies. Specifically, as dimple diameter increases, Nu changes by 13.1 %–89.3 %, fr rises by 51.9 %–509 %, and PEC varies between −20.2 % and 3.1 %. When the number of dimple stars increases, Nu changes by 21.3 %–45 %, fr increases from 58.7 % to 277.9 %, and PEC improves by 5.3 %–20 %. The results also show that, based on the dimple parameters and Reynolds number, the PEC number can reach a maximum of 1.4.
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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".