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Explicit Solution for Pipe Diameter Problem Using Lambert <i>W</i> -Function

2022· article· en· W4283807199 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Irrigation and Drainage Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPipe flowPlug flowPipe network analysisLambert W functionNominal Pipe SizePipeline transportFlow (mathematics)Function (biology)MechanicsPipeline (software)MathematicsMathematical analysisMaterials scienceEngineeringMechanical engineeringPhysicsComposite materialTurbulence

Abstract

fetched live from OpenAlex

Determining the pipe diameter is one of the principal problems encountered in designing and analyzing pipe flow lines. However, the direct determination of a pipe’s diameter is not possible because of the implicit form of the Colebrook resistance flow formula through commercial pipes. Traditionally, the pipe diameter is determined using a trial procedure. In this paper, the pipe diameter problem was solved using explicit equations in terms of the Lambert W-function. The maximum relative errors of the developed solutions are less than 0.013% for the rough and smooth flow regimes and less than 0.8% and 0.6% for the transition flow region between them. In addition, a method for determining pipe diameter under uncertainty, including design graphs, is presented. It is hoped that the developed solution for predicting pipe diameter will be helpful in the analysis of pipe flow and the design of pipelines and water distribution networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.187
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it