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Record W2784002364 · doi:10.1139/cjce-2017-0490

Utilizing siphon for effective management of air in gravity-fed water pipelines

2018· article· en· W2784002364 on OpenAlexvenueno aff
Yujian Fang, Shouqi Yuan, Jinfeng Zhang, Yimeng Weng, Jianrui Liu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSiphon (mollusc)Pipeline (software)Pipeline transportInletMarine engineeringEngineeringHydraulic headHead (geology)Civil engineeringExcellencePetroleum engineeringMechanical engineeringGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

A novel siphon design has been invented and continuously optimized by Mr. Weng to adapt various pipe diameters since 1990. Within the past 30 years of practical experience, although its success is in increasing transmission capacity, delivering water to higher elevation, and longer distance without additional power input has drawn wide social attention. But this technology has not obtained the industrial acceptance so far due to the lack of theoretical knowledge to explain how the head loss is reduced significantly. This paper presents the experimental findings in the laboratory from an undulating water pipeline with a novel siphon inlet, and reveals the excellence of this design for air management in the pipeline. Together with the experimental findings and relevant literature, two engineering projects utilizing this novel siphon are investigated in detail to illustrate its superior hydraulic performance.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.182
Teacher spread0.177 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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