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Record W4386072910 · doi:10.11159/htff23.180

Jet Direction Control Using Active Switching Nozzle

2023· article· en· W4386072910 on OpenAlexvenueno aff
Taisei Suzuoka, Koichi Nishibe, Kotaro SATO, Donghyuk KANG

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleJet (fluid)PhysicsControl theory (sociology)MechanicsControl (management)Aerospace engineeringMaterials scienceComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Many studies have been conducted on fluidic thrust vectoring, which uses a secondary flow to deflect a primary jet, instead of mechanical thrust vectoring including a variable exhaust nozzle, to improve maneuverability and maintainability by simplifying, downsizing, and reducing the weight of the entire thrust vectoring system [1].In recent years, research has also begun on the utilization of synthetic or hybrid-synthetic jets as secondary flows for further increasing the deflection angle [2].In addition, Mair et al. [3] proposed a novel fluidic valve with active switching using acoustic signals and a splitter; they determined the flow switching mechanism from experimental and numerical results.These technologies find applicability in not only aircraft development but also in a wide range of other fields, including heating, ventilation, and airconditioning systems.However, in the conventional methods, there is a dead zone where the deflection angle cannot be adjusted, and a technique to adjust the deflection angle precisely without a splitter, which is an issue in practical use, has not been established.Therefore, in this study, the control of the direction of the generated flow using a new active switching nozzle in which the face-to-face control port portion of a conventional flip-flop jet nozzle was replaced by compact speakers that can be adjusted to any frequency, oscillation amplitude, and phase difference, was studied experimentally.The geometry of the tested nozzle was fabricated with reference to the flip-flop nozzle of Koso et al. [4].The influence of the spread angle of the side wall of the duct, where the continuous jet fed inside the tested nozzle attaches and detaches periodically owing to the oscillation of compact speakers, on the deflection angle of the generated flow was investigated experimentally.The velocity fields inside and downstream of the tested nozzle were measured by a hot-wire anemometer and two-dimensional particle image velocimetry to investigate the vorticity distributions and the deflection angle of the generated flow.Computational fluid dynamics assuming a two-dimensional incompressible viscous flow was also performed, with unsteady Reynoldsaveraged Navier-Stokes as the governing equation, to supplement the experiments.The results obtained show that the relationship between the spread angle of the expanding duct and the oscillation frequency and amplitude of the continuous flow inside the nozzle affects the deflection angle of the generated flow.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0010.000
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 head0.201
Teacher spread0.195 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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