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Record W7139729105

Development and assessment of a propeller shaft load measurement system

2025· article· en· W7139729105 on OpenAlexvenueno aff
Mohammed N. Islam, Moqin He, Edward H. Kennedy, James F. Sweeney, Heather Peng, Lorenzo Moro

Bibliographic record

VenueNPARC · 2025
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsnot available
Fundersnot available
KeywordsPropellerVibrationNoise (video)Drive shaftDynamic testingSystem of measurementMeasure (data warehouse)Forcing (mathematics)Limiting
DOInot available

Abstract

fetched live from OpenAlex

Accurate model-scale measurement of dynamic shaft loads remains a significant challenge in characterizing propeller-induced noise and vibration. Conventional test methods lack the ability to directly capture high-frequency, multi-axis forces at the propeller shaft, limiting the fidelity of validation data for predictive models. To address this gap, a novel shafting system has been developed to measure six-component propeller loads directly, facilitating high-frequency shaft vibration characterizations in a model-scale test setup. This isa unique sensory system to capture high-frequency propeller-induced vibration forces directly at the hub. At its core is an AMTI SP1-500load sensor integrated into a custom-designed shaft assembly capable of resolving axial thrust, torque, and lateral loads induced by the propeller. The system was qualified through in-situ testing, with data cross-compared to conventional propeller dynamometry, pressure sensors, and accelerometers. Frequency-domain analysis showed strong alignment of load and vibration signatures at propeller blade harmonics, primarily below 200 Hz. These direct shaft load measurements at the propeller hub provide critical insights into the dynamic forcing mechanisms that contribute to propeller noise and hull-excited vibrations, offering a powerful tool for validating computational fluid dynamics (CFD) models and improving quiet ship design.

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: none
Teacher disagreement score0.720
Threshold uncertainty score0.300

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 head0.246
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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