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Record W4412736266 · doi:10.1115/1.4069236

Experimental and Numerical Investigation of a Needle-Ring Type of Ionic Wind Generator Acting Alone and as a Flow Enhancement Device

2025· article· en· W4412736266 on OpenAlexaff
Sichang Xu, G. W. Rankin

Bibliographic record

VenueJournal of Fluids Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIon windGenerator (circuit theory)Flow (mathematics)MechanicsRing (chemistry)Materials scienceVortex generatorChemistryPhysicsVortexThermodynamicsPower (physics)

Abstract

fetched live from OpenAlex

Abstract Experimental and numerical investigations are conducted to determine the operational characteristics of a needle-ring type of ionic wind generator for the case when it acts alone and for the case when it is used as a device assisting or enhancing the flow of a primary flow generator. The experimental study is conducted using a test facility designed according to the ANSI/AMCA 210-16 standard to determine the pressure rise, volume flowrate, and energy efficiency. The entire ionic wind generation and testing process is modeled by a hybrid multiphysics scheme for comparison with experimental results and to investigate details of the experiment that could not be measured. The performance of the ionic wind generator is numerically predicted by alternatively solving the fluid field equations using a finite volume computational fluid dynamic scheme and the electric field equation using a finite element simulation. The segregated steps are conducted sequentially in a loop system to reach a specified convergence level to obtain a steady-state solution. The experimental and numerical characteristic curves are in good agreement, and the associated static efficiencies are found to be much smaller compared to conventional fans. When the primary flow is in the same direction as the ion flow, the recovery efficiencies of the ionic wind generator are found to increase by supplying a larger primary flowrate.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.389

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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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
Published2025
Admission routes1
Has abstractyes

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