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Record W1968946969 · doi:10.1115/1.4002805

Design of a Multijet Omnidirectional Propulsion System for Small Jet-Boats

2010· article· en· W1968946969 on OpenAlexaff
David Foley, Jean‐Sébastien Plante

Bibliographic record

VenueJournal of Mechanical Design · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsJoystickNozzlePropulsionOmnidirectional antennaEngineeringSimulationDragKinematicsControl theory (sociology)Aerospace engineeringMechanical engineeringComputer sciencePhysicsControl (management)

Abstract

fetched live from OpenAlex

Jet-boats perform remarkably well at high-speed but lack low speed maneuverability for tight maneuvers such as docking. This paper presents a joystick controlled omnidirectional propulsion system for jet-boats. The concept uses a set of fixed jet nozzles disposed around the hull. When a force is commanded by the joystick, valves on each nozzle modulate the flow so that the sum of nozzle thrusts correspond to the commanded force. The positions and angles of the nozzles are optimized with an index of omnidirectionality quality based on the projection of a set of force solutions on a shell with the shape of a desired force space. The choice of valve positions and engine speeds is done by the numerical inversion of an internal viscous flow model. A 3D simulator, backed by experimental results, serves to (1) evaluate the ability of the proposed concept in meeting its design requirements and (2) develop control algorithms. Experimental results show that the proposed omnidirectional system is effective for low speed maneuverability with open-loop force control. The present work also offers an effective omnidirectional propulsion system that is easy to enhance with advanced control laws. Velocity feedback control is given as an example and shows important improvement of maneuverability and robustness to miscalibration.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.249
Teacher spread0.217 · 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
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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