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Record W2149659540 · doi:10.1115/1.4005941

Simplified Fluid-Structure Model for Duckbill Valve Flow

2012· article· en· W2149659540 on OpenAlexafffund
J. Wang, D. S. Weaver, Stephen Tullis

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFinite element methodMechanicsHyperelastic materialFlow (mathematics)Fluid–structure interactionShell (structure)Structural engineeringMaterials scienceEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Duckbill valves (DBVs), made of a fabric reinforced layered rubber composite, are extensively used for nonreturn axial water flows with low back pressures. Fluid–structure interaction (FSI) is directly involved in the opening process of the DBV, with the opening depending on the pressure differential across the valve. In this paper, a simplified FSI model of the DBV is presented using a finite element method (FEM). The valve is modeled as a laminated thick shell structure with some simplifications to the boundary conditions. The pressure load acting on the shell surface of the DBV is a function of the variable valve cross-sectional area and determined, for preliminary analysis purposes, by using a simple potential flow model for the fluid mechanics. The hyperelasticity of the rubber and orthotropy of the fiber reinforcement, as well as large deflections of the DBV, are considered in the simulation. The valve is modeled as being closed when the upstream pressure is applied, and the transient opening process is tracked until a steady state opening is achieved. Several static cases of viscous flow passing through the deformed valve structure have also been carried out to compare the pressure and velocity fields of fluid flow with the corresponding pressure and velocity distribution predicted by the simplified model and to compare the hydraulic performance of the DBV predicted by both models.

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: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.494

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 head0.230
Teacher spread0.221 · 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
GenreMethods

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

Citations4
Published2012
Admission routes2
Has abstractyes

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