Investigation on drag reduction performance of pipeline with bioinspired microgrooved surface
Bibliographic record
Abstract
Novel surface morphology of pipeline with transverse microgrooves was proposed for reducing the pressure loss of fluid transport. Numerical simulation and experimental research efforts were undertaken to evaluate the drag reduction performance of bionic pipeline. The computational fluid dynamic calculation, using SST κ-ω turbulent model, shown that the “vortex cushioning effect” and “driving effect” produced by the vortexes in the microgrooves were the main reason for the drag reduction. The shear stress of the microgrooved surface was reduced significantly owing to the decline of the velocity gradient; then bionic pipeline achieved drag reduction effect in the pipe and concentric annulus flow. The primary and secondary order of effect on the drag reduction and optimal microgroove geometric parameters were obtained by orthogonal analysis method. The comparative experiments were conducted in a water tunnel, and a maximum drag reduction rate of 3.21% was achieved. The numerical simulation and experimental results were cross-checked and consistent with each other to verify that the utilization of bionic theory to reduce the pressure loss of fluid transport is feasible. Results can provide theoretical guidance for the energy saving of pipeline transportation.
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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) | 0.000 | 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 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".