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Record W4409159864 · doi:10.1063/5.0264107

Air–water interactions during rapid filling of a closed horizontal pipe

2025· article· en· W4409159864 on OpenAlexaff
Yaohui Chen, Pengcheng Li, Zhaodan Fei, Han Zhang, David Z. Zhu, Shangtuo Qian

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsPhysicsMechanicsAir waterMeteorology

Abstract

fetched live from OpenAlex

Rapid filling processes in urban water infrastructure, involving open-channel to pressurized flow transitions with complex air–water interactions, require precise understanding of transient pressure surges, air entrapment and air discharge mechanisms. This study establishes the first experimental framework classifying rapid pipe filling into four distinct stages: advancing bore (stage I), reflection bore (stage II), air intrusion bore (stage III), and residual air release with subsequent inertial oscillations (stage IV). Quantified parameters including bore speeds, pressure and air discharge rate evolutions during each stage, are systematically correlated with gate opening and inlet head, establishing novel predictive relationships. The arrival of the reflection and pipe-filling bores at the shaft can induce significant pressure surges, with peak magnitudes reaching 2.3 times the inlet head through newly identified scaling law dependent on operational parameters. The advancing, reflection and air intrusion bores all cause significant air discharge through the shaft, and an expression for the maximum air discharge rate is obtained. Critically, over 90% of the initial air is expelled during stages I–III, with 28%–77% expelled in stage I, which accounts for less than 11% of the total filling duration. The air discharge volume in stage I increases as the gate opening and inlet head increase. These findings provide a critical foundation for the development of validated transient air–water flow models, predictive mitigation of pressure surge risks, and optimization of resilient urban water infrastructure.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.495

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 head0.006
GPT teacher head0.203
Teacher spread0.198 · 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 teacher head, 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

Citations6
Published2025
Admission routes1
Has abstractyes

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