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Record W4407223891 · doi:10.1063/5.0247625

Flow and thrust vectoring characteristics of underwater high-speed gas jet

2025· article· en· W4407223891 on OpenAlexaboutno aff
Zhang Shao-qian, Bao Wenchun, Shichao Feng, Hao Xu, Tiezhi Sun

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhysicsThrust vectoringJet (fluid)UnderwaterMechanicsFlow (mathematics)Aerospace engineeringThrustThermodynamicsOceanography

Abstract

fetched live from OpenAlex

When utilizing high-speed gas jet for the propulsion of underwater vehicles, complex flow phenomena such as ventilated cavitation, bubble expansion, and contraction are formed, along with corresponding complex thrust characteristics. In this paper, an experimental study was conducted on the thrust and flow field evolution characteristics of vector-deflected high-speed gas jets produced by a Laval nozzle under co-flow conditions. Under the experimental conditions of this study, the venting position of the pulsating foam tail cavity shifts with the increase in the nozzle vector angle θ. The axial component of thrust exhibits a noticeable loss as the vector angle θ increases, and its oscillation is correlated with the pressure pulsation of the tail cavity. The nozzle thrust vector angle operates within an optimal range, with the lateral force peaking at θ = 6°. Beyond this angle, the lateral force diminishes as θ progresses further. The amplitude of the lateral force is related to the vent channel, with unobstructed channels corresponding to the peak values of the lateral force. This paper can provide a reference for the design of vector jet propulsion systems for underwater vehicles. The unique phenomena and patterns of underwater vector jets revealed through experiments lay the foundation and offer insights for more in-depth mechanistic studies.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.460

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 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

Citations4
Published2025
Admission routes1
Has abstractyes

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