Development and assessment of a propeller shaft load measurement system
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".