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Record W3048969045 · doi:10.1139/juvs-2019-0019

Development of small-scale unmanned hydrofoil boats

2020· article· en· W3048969045 on OpenAlexvenueno aff
Noah T. Thompson, Phillip R. Whitworth, Konstantin I. Matveev

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
FundersOffice of Naval Research
KeywordsAutopilotThrustHullMarine engineeringAerospace engineeringEngineeringRange (aeronautics)Scale (ratio)Physics

Abstract

fetched live from OpenAlex

Unmanned boats have gained a lot of interest in the maritime community during the last decade. Most hydrodynamic platforms employed for unmanned boats are based on traditional relatively simple hulls. In the present study, small-scale hydrofoil-assisted unmanned boats (0.6–0.7 m in length and 3.5–5.5 kg in mass) have been developed and tested. Design calculations using a hydrodynamic transverse-strip engineering method with semi-empirical correlations were applied to determine suitable dimensions for hydrofoil systems. The boat hulls and hydrofoils were fabricated by laying up carbon-fiber cloth sheets on foam cores or 3-D printed profiles. The boats were instrumented with outboard propulsors and electronic equipment for operations in both remote control and autopilot modes. In addition, an in-situ thrust-measuring module was designed and installed at the hull sterns to gather thrust data at GPS-measured speeds in the range between 0 and 11 m/s. The developed boats proved to be robust platforms capable of going over 600 m distances at high speeds while autonomously following preset paths. The presented methods and results can assist engineers developing unmanned surface vehicles that utilize advanced hydrodynamic concepts.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.618

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 head0.022
GPT teacher head0.210
Teacher spread0.188 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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