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Record W2990266898 · doi:10.1002/cjce.23687

Effect of substrate geometry and flow condition on the turbulence generation after a monolith

2019· article· en· W2990266898 on OpenAlexafffundvenue
Iván Cornejo, Petr A. Nikrityuk, Robert E. Hayes

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Alberta
FundersComisión Nacional de Investigación Científica y TecnológicaNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceMonolithPressure dropMechanicsMaterials scienceAmplitudeDrop (telecommunication)GeometryPhysicsOpticsChemistryEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Abstract This paper reports a study of turbulence generation after a monolith honeycomb. Large eddy simulation is used to analyze the turbulence generated when steady, turbulent, or pulsating flow leaves a monolith channel. Substrates with different cell densities, wall thicknesses, and channel cross‐sectional shapes are investigated. The results show that the magnitude of the turbulence generated depends on the wall thickness and monolith void fraction, though not much on the cell density. For pulsating flow, different frequency of the pulsations produced only slightly different results, however, the amplitude of the pulsations is proportional to the magnitude of the turbulence generated. The outflow of the channels can act as a jet and trigger turbulence along a distance from 10 to 30 channel diameters, significantly affecting the total pressure drop and the inlet conditions for elements in series downstream, such as particulate filters or other substrates.

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 score0.008
Threshold uncertainty score0.243

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 head0.003
GPT teacher head0.160
Teacher spread0.157 · 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

Citations15
Published2019
Admission routes3
Has abstractyes

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