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Record W4206460984 · doi:10.14796/jwmm.c479

Multiphase Rapid Filling Conditions of Tunnel System in Columbus, Ohio

2022· article· en· W4206460984 on OpenAlexvenueno aff
José G. Vasconcelos, Hazem Gheith, Robson Leo Pachaly, Mohamed Abdellatif, R. W. Herr

Bibliographic record

VenueJournal of Water Management Modeling · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsInflowEnvironmental scienceMarine engineeringVentilation (architecture)Combined sewerSurgeEngineeringCivil engineeringMeteorologySurface runoffMechanical engineering

Abstract

fetched live from OpenAlex

The City of Columbus, Ohio is implementing a tunnel system to reduce the number of episodes of combined sewer overflows into the Scioto River. The tunnel systems provide relief to the existing Olentangy Scioto Interceptor Sewer. Two new tunnels being implemented are the OSIS Augmentation and Relief Sewer (OARS), in service since July 2017, and the Lower Olentangy Tunnel (LOT) that is planned to be in service in 2025. The performance of these tunnels in respect to high inflow conditions was investigated with the use of the HAST mixed flow model and the OpenFOAM CFD model to determine the magnitude of surges, the possibility of air pocket entrapment, air–water surging, and the consequences of uncontrolled air pocket releases through shafts. Inflows into the systems were obtained from a calibrated collection system SWMM model. Modeling results quantified surging in the tunnel dropshafts and their mitigation from built-in surge control chambers. HAST simulations also pointed to locations where air pockets could form. These results were used in OpenFOAM to determine the effects of uncontrolled air release through the shaft that links the two tunnels. It was shown that proper ventilation at the shaft will mitigate the growth of air phase pressure to damaging levels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.358

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 head0.010
GPT teacher head0.200
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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