Computationally Efficient and Accurate Solution for Colebrook Equation Based on Lagrange Theorem
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Bibliographic record
Abstract
Abstract Computationally efficient solutions (less computation time) for the Colebrook equation are essential for simulating pipeline networks. However, the friction law resistance formula has an implicit form for the friction factor. In this study, a computationally efficient and accurate solution for the friction head loss in pipeline networks is developed using the Lagrange inversion theorem. The results are in the form of fast converging power series. Truncated and regressed expressions are obtained using two and three terms of the expanded series that have maximum relative errors of 0.149% and 0.040%, respectively. The proposed solution is as accurate as existing analytic solutions but is computationally more efficient in estimating the friction head loss.
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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