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Record W2968653638 · doi:10.2166/wst.2019.269

Numerical investigation on bottom shear stress induced by flushing gate for sewer cleaning

2019· article· en· W2968653638 on OpenAlex
Haoming Yang, David Z. Zhu, Yiping Zhang, Yongchao Zhou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWater Science & Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlushingShear stressGeotechnical engineeringEnvironmental scienceComputational fluid dynamicsVolume (thermodynamics)Volumetric flow rateFlow (mathematics)MechanicsPetroleum engineeringHydrology (agriculture)EngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

One of the most common strategies for sewer cleaning is to generate flushing flows using flushing gates to store water in the upstream sewer pipe. Therefore it is important to obtain the flow information on the flushing waves and their eroding effects. In this study, the flow characteristics of the flushing wave and the flushing effect were investigated by a transient flow calculation using a commercial computational fluid dynamics (CFD) code. The values of bottom shear stress were obtained and the effect of several factors are discussed. The water depth and the slope were related to the release rate of the storage volume, while the flushing volume determined the flushing distance at long sewer distances. The initial downstream water level was found to dramatically reduce the flushing effect. Equations based on the storage depth were developed to estimate the flushing effect, and suggestions for the installation and operation of flushing gates are provided.

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.

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 score0.050
Threshold uncertainty score0.402

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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