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Record W4375852895 · doi:10.1139/cjce-2021-0554

Numerical study for the computation of critical submergence for side circular intake under uniform flow

2023· article· en· W4375852895 on OpenAlexvenueno aff
Muhammed Hashid, Ajmal Hussain, Zulfequar Ahmad

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
FundersIndian Institute of Technology Roorkee
KeywordsFroude numberMechanicsVortexFlow (mathematics)Computational fluid dynamicsComputationFree surfaceGeologyMathematicsGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper discusses a numerical model study for the simulation of flow characteristics and critical submergence for a laterally placed horizontal circular bottom intake under uniform flow. The proposed model simulates the free surface using the volume of fluid model to check the vortex formation at critical submergence. A new combined approach using phase volume fraction and swirl strength-based vortex identification mechanism is used to compute the critical submergence. The swirl strength-based vortex identification mechanism can show the vortex tube in approach flow with swirl generated at the free surface due to the axial flow withdrawal through side bottom intake at critical submergence. The computational fluid dynamics (CFD) model results were validated using experimental data, which showed a maximum error of less than ±10% in the prediction of the critical submergence. The effect of significant parameters like intake and approach flow Froude number and sill height of intake on the critical submergence is discussed. The results of this study help practitioners to adopt CFD-based numerical modelling for the design of water intakes instead of entirely relying on physical model studies, which require more finance and time.

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: none
Teacher disagreement score0.953
Threshold uncertainty score0.385

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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