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Record W2907715366 · doi:10.1080/07373937.2018.1531291

Numerical investigation of the multi-pin electrohydrodynamic dryer: Effect of cross-flow air stream

2019· article· en· W2907715366 on OpenAlexaff
Chongshan Zhong, Alex Martynenko, Patrick R. Wells, Kazimierz Adamiak

Bibliographic record

VenueDrying Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsWestern UniversityDalhousie University
Fundersnot available
KeywordsElectrohydrodynamicsMechanicsFlow (mathematics)Computer simulationAirflowElectric fieldMaterials scienceIntensity (physics)Range (aeronautics)MoistureEnvironmental scienceSimulationMechanical engineeringEngineeringPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

This article presents the results of numerical simulation and experimental study of a multi-pin electrohydrodynamic (EHD) dryer. Combined effect of EHD flow and the external air cross-flow on drying performance was investigated with 3-D numerical model, which accounts for electric field, electric charge transport, external air cross-flow and material-gas moisture transport. Effect of cross-flow air stream on drying was positive in the range of low velocities, changing to negative at high velocities due to counteracting with EHD flow. Numerical simulation predicted previously unknown effect of EHD flow on the cross-flow air stream, which was quantified as an increase of airway resistance. This prediction was fully validated by experiments. Both numerical simulation and experiment proved that for given intensity of EHD flow there is an optimum value of the cross-flow, resulting in maximum drying performance. The numerical model can be applied to determine the optimal operating parameters for multi-pin EHD dryer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.225
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 source (direct Gemma or distilled Codex), 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

Citations25
Published2019
Admission routes1
Has abstractyes

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