Investigation on Drag Reduction Through Dimple Machining
Bibliographic record
Abstract
High friction between a fluid and a pipe wall results in increased pumping requirements. This friction contributes to lower production rates and reduced system capacity. Thermal heating, fluid blending, and drag reducing agents (DRA) are commonly used methods for decreasing pressure drop in pipelines. Surface patterns inscribed onto internal pipe walls have also been shown to reduce fluid friction. In this paper, the effects of different surface patterns on the shear between a fluid and a wall are studied. Surfaces with different dimple patterns are investigated. Micro-dimpled patterns on the surface are created using an inclined, flat end micro-milling tool. The surfaces with different dimpled patterns are characterized and tested through morphological, contact angle, and viscosity measurement studies. The effects of the surface patterns are also studied through simulation. A Power Law relationship and apparent fluid viscosity is determined for the low Reynolds numbers investigated. The deepest dimpled surfaces investigated (0.2 mm dimple depth) result in a drag reduction of approximately 20% for silicone oil. Further research and application of the results to transmission pipeline systems are discussed.
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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 | 0.000 |
| 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) | 0.001 | 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".