MétaCan
Menu
Back to cohort
Record W4311611318 · doi:10.1080/00221686.2022.2132309

Energy dissipation in a rapid filling vertical pipe with trapped air

2022· article· en· W4311611318 on OpenAlexaff
Ling Zhou, Yanqing Lu, Bryan Karney, Guoying Wu, Alain Joel Elong, Kun Huang

Bibliographic record

VenueJournal of Hydraulic Research · 2022
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesChina Scholarship CouncilFok Ying Tong Education FoundationNational Natural Science Foundation of China
KeywordsDissipationMechanicsEnergy (signal processing)Thermal management of electronic devices and systemsEnvironmental scienceGeologyMaterials scienceMeteorologyPhysicsThermodynamicsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

An energy-dissipation model is developed to simulate rapid filling having an entrapped air pocket within a vertical pipe. Both convective heat transfer and transient wall shear stresses are considered. The resulting predictions are compared both to those obtained via a conventional empirical polytropic model and to experimental data. The comprehensive model accurately reproduces the experimental pressure oscillations. Results reveal that the dynamic behaviour of air pockets in all tested cases approaches a purely adiabatic process over the first two oscillations, but also that the pressure variation then gradually evolves to an isothermal variation. Moreover, the high air temperatures predicted in the numerical simulations account for the observed phenomenon of white mist in the pipe as well as the notably hot pipe wall. Significantly, the conventional empirical polytropic model associated with the adiabatic assumption for the gas phase was sufficient to reproduce the pressures and temperatures during the first two oscillations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hydraulic ResearchSame topicWater Systems and OptimizationFrench-language works237,207