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Record W1651652910 · doi:10.1109/ias.2011.6074281

3D numerical study of wire-cylinder precipitator for collecting ultrafine particles from diesel exhaust

2011· article· en· W1651652910 on OpenAlexaff
Niloofar Farnoosh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsWestern University
Fundersnot available
KeywordsDiesel exhaustCylinderUltrafine particleDiesel engineDiesel fuelExhaust gas recirculationAutomotive engineeringMaterials scienceMetallurgyEnvironmental scienceWaste managementEngineeringMechanical engineeringExhaust gasNanotechnology

Abstract

fetched live from OpenAlex

A precipitator section is modeled numerically in 3D to determine the collection efficiency for conductive diesel exhaust particulates. It consists of a circular tube and a wire electrode mounted at the center of the tube, supplied with a negative high dc voltage, while the tube is electrically grounded. The analytical solutions of Poisson and current continuity equations are implemented to obtain the ionic space charge density and electric potential distributions in the channel. Commercial CFD FLUENT software is used to solve the k-ε turbulent flow model, while also considering the electrical body forces. Particle charging and motion equations are solved using Discrete Phase Model (DPM) feature of the FLUENT and programming User Defined Functions (UDFs). Particles are assumed to be charged by combined field and diffusion charging mechanisms. Effects of some electrical characteristics of diesel exhaust particulates, such as charge-to-mass ratio and particle migration velocity, on collection efficiency are assessed. Patterns of particle deposition along the channel are evaluated and compared for different particle sizes. Numerical modeling of the 3D EHD flow pattern induced by corona discharge is demonstrated in the cross section of the tube when the corona wire is slightly off-center (eccentric) in an arbitrary direction.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.483

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 head0.050
GPT teacher head0.256
Teacher spread0.206 · 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 designBench or experimental
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
Published2011
Admission routes1
Has abstractyes

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