Pressure Drop for Fully Developed Turbulent Flow in Circular and Noncircular Ducts
Bibliographic record
Abstract
The objective of this paper is to furnish the engineer with a simple and convenient means of estimating frictional pressure drop in ducts and the original physical behavior can be clearly reflected. Fully developed turbulent flow frictional pressure drop in noncircular ducts is examined. Simple models are proposed to predict the frictional pressure drop in smooth and rough noncircular channels. Through the selection of a novel characteristic length scale, the square root of the cross-sectional area, the effect of duct shape has been minimized. The proposed models have an accuracy of 6% for most common duct shapes of engineering practice and can be used to predict pressure drop of fully developed turbulent flow in noncircular ducts. It is found that the hydraulic diameter is not the appropriate length scale to use in defining the Reynolds number to ensure similarity between the circular and noncircular ducts. By using the Reynolds number based on the square root of the cross-sectional area, it is demonstrated that the circular tube relations may be applied to noncircular ducts eliminating large errors in estimation of pressure drop. The square root of the cross-sectional area is an appropriate characteristic dimension applicable to most duct geometries. The dimensionless mean wall shear stress is a desirable dimensionless parameter to describe fluid flow physical behavior so that fluid flow problems can be solved in the simple and direct manner. The dimensionless mean wall shear stress is presented graphically and appears more general and reasonable to reflect the fluid flow physical behavior than the traditional Moody diagram.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".