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Dynamic pressure measurement of ionic wind, generated by multi-emitter discharge for drying systems

2025· article· en· W4412972448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFlow Measurement and Instrumentation · 2025
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIon windMaterials scienceCommon emitterMechanicsEnvironmental scienceOptoelectronicsPhysicsElectrode

Abstract

fetched live from OpenAlex

The ionic wind, generated by corona discharge from sharp emitters, is extensively used for propulsion and heat/mass transfer enhancement. Accurate speed measurement is critical to optimize the performance of ionic wind generators, especially in systems with multiple emitters where non-uniform flow is prevalent. To address this, a digital balance is used to measure the average speed of the ionic wind using dynamic pressure. The comparison with hot-wire and vane anemometers showed the advantages of dynamic pressure measurement of non-uniform ionic wind speed. • Electrohydrodynamic flow profile is different from mechanical flow. • Various methods of EHD flow measurements have been compared. • Spatial distribution of EHD flow is measurable with hot-wire anemometer. • The average speed of non-uniform flow is measurable by dynamic pressure.

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.

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: none
Teacher disagreement score0.855
Threshold uncertainty score0.649

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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