Comparison of Heat Transfer and Friction in Pipes With Various Internal Roughness
Bibliographic record
Abstract
Abstract The heat transfer and friction factor of turbulent pipe flows with different internal roughness are experimentally investigated. Three types of roughness in forms of a mesh, hemispherical elements, and a coil are added to the interior of pipes with an inner diameter of 52.5 mm. The working fluid is air, and the Reynolds numbers vary from 20,000 to 90,000 in increments of 10,000. For investigating the heat transfer, the pipe wall is heated to 375 °C while the inlet air remains at the room temperature. The measurements show that the mesh-type roughness results in a maximum Nusselt number, Nu, increase of approximately 6%, the pipes with hemispherical roughness increased the Nu by a maximum amount of 30%, and the coil increased Nu by up to 60% compared with the smooth pipe. The maximum increase of friction factor is 40% for the pipes with mesh-type roughness, 30% for pipes with hemispherical roughness, and 67% for pipes with coil roughness. The experimental results indicate that adding hemispherical and coil roughness to the internal surface of the pipe can lead to a significant improvement in the rate of heat transfer while adding a mesh-type roughness can have marginal improvements and comes with a large frictional loss penalty. The analysis shows that the highest thermohydraulic performance is achieved using the hemispherical roughness elements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".