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Record W3175603729 · doi:10.1139/tcsme-2020-0149

Dynamic mode decomposition of elliptical liquid jets in crossflow

2021· article· en· W3175603729 on OpenAlexafffundvenue
Keivan Mokhtarpour, Mehdi Jadidi, Ali Dolatabadi

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsUniversity of TorontoConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBody orificeMechanicsBreakupDynamic mode decompositionMomentum (technical analysis)PhysicsWeber numberHydraulic diameterJet (fluid)Capillary actionOpticsMaterials scienceThermodynamicsReynolds numberTurbulenceMechanical engineering

Abstract

fetched live from OpenAlex

The dynamics of round and elliptical liquid jets in subsonic crossflow was studied using high-speed imaging technique. The experiments were performed at constant gaseous Weber number and liquid–gas momentum flux ratios of 6.45 and 17.87, respectively, with orifices of different aspect ratios (AR) having an equivalent diameter of 0.43 mm. All of the cases were carried out inside an open-loop subsonic wind tunnel with a test section of 100 mm × 100 mm × 750 mm. For each case, dynamic modes were generated directly from the snapshots using a variant of the Arnoldi method known as the dynamic mode decomposition (DMD). The DMD results indicate that elliptical liquid jets have more small-scaled patterns with higher frequencies compared with the round liquid jets. As the first attempt to investigate the dynamics of elliptical liquid jets in crossflow, this study captures the dominant spatiotemporal structures. We also found that the orifice AR can significantly alter the jet wavelengths. The data from this study contribute to understanding the behavior of elliptical liquid jets exposed to gas crossflow in an enhanced capillary breakup regime.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.227
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

Citations1
Published2021
Admission routes3
Has abstractyes

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Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicNuclear Engineering Thermal-HydraulicsFrench-language works237,207