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Record W3167410629 · doi:10.1061/9780784483466.046

Numerical Study of Storm Geyser Mechanism in the Multi-Inlet System at Laboratory and Prototype Scale

2021· article· en· W3167410629 on OpenAlexaff
Jiachun Liu, Biao Huang, David Z. Zhu

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2021 · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInletInflowStormScale (ratio)Computer simulationEnvironmental scienceScale modelMechanism (biology)GeologyMechanicsMarine engineeringMeteorologyHydrology (agriculture)Geotechnical engineeringGeomorphologyEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Storm geysers increasingly occur in sewer systems under climate change and rapid urbanization. The geyser mechanism in a single-inlet system has been extensively studied, while the geyser mechanism in multi-inlet systems has yet to be discovered. In this study, three-dimensional computational fluid dynamics models with multi-inlets at laboratory and prototype scale were established to investigate geysering induced by rapid filling. The numerical results at laboratory scale indicate that compared to the single-inlet model under identical inflow conditions (rapid increase the same total flow in the same time), the geyser pressure generated in the multi-inlet model is less than that of a single-inlet model, but more water is ejected out, and the geyser process is closer to the actual geyser event. The numerical results of the prototype model show that the equivalent density of the water-air mixture during geysering in the prototype scale model is about 212 kg/m3, which is less than the equivalent density in the laboratory scale model.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.345

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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