Explicit solutions for turbulent flow friction factor: A review, assessment and approaches classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Colebrook –white equation is widely used in many fields, like civil engineering for calculation of water distribution systems and in all fields of engineering where fluid flow can be occurred. Numerous formulas have been proposed since 1947 in order to simplify the computation of the friction factor, to avoid the iterative procedures methods and to alter the Colebrook-white equation in practice. most of the existing explicit formulas for computation of the friction factor for turbulent flow in rough pipes proposed are cited, where thirty three “33” equations have been inventoried. The goal of this paper is to assess the accuracy of each model and to propose an arrangement from the best to the lower accuracy according to a proposed method combined of three criteria which are: simplicity of the formula, maximum deviations and the coverage of the entire range of Moody diagram. Keywords: Friction factor, Explicit solutions, Moody diagram, Maximum deviation, Turbulent flow
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it