Form and Friction Drag Coefficients of Fine Screens: Streamlined and Rectangular Bar Profiles
Bibliographic record
Abstract
Two different bar geometries of the fine screens used at the water intakes of hydropower plants, namely the streamlined Oppermann profile and the conventional rectangular profile, were numerically investigated to determine the corresponding form and friction drag coefficients.For both bar profiles, the bar thickness and the total bar length were s = 0.006 m and L = 0.083 m, respectively.FLOW-3D software was used to perform the simulations, where the Large Eddy Simulation (LES) was applied with a uniform mesh size of 0.001 m.Three different bar spacings of 4, 6, and 10 mm were tested under eight different approach velocities corresponding to bar Reynolds number and Reynolds number defined for bar length ranges of 300<Reb<2400 and 4150<ReL<33200, respectively.It was revealed that both form and friction drag coefficients are highly dependent on the shape of the bar.Accordingly, the average form drag coefficients for the Oppermann and rectangular bar profiles were found to be 1.20 and 3.23, respectively.Similarly, the average friction drag coefficients were obtained as 0.31 and 0.57, respectively.Also, for both bar geometries, it was shown that the form drag coefficient decreased until a certain limit of around Reb=103 and remained almost constant despite the ongoing increase in the bar Reynolds number.However, as the ReL increased, we observed a continuous reduction in the friction drag coefficient, which aligns with the analytical solutions.Moreover, both bar profiles yielded significantly higher form drag coefficients for narrower bar spacing.This result points out a strong correlation between the form drag coefficient and the head losses generated by fine screens at water intakes.Numerical analysis revealed that the form drag accounts for approximately 80% of the total drag, significantly contributing to the head losses at fine screens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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.
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