Near Real Time Prediction of Vibration in 3D Printed Flettner Rotor Demonstrator

This paper presents an investigation of the Flettner rotor, a wind propulsion system utilized in ships to decrease fuel consumption and reduce carbon emissions by leveraging the Magnus effect. Proper balancing and stability of the rotor are crucial to ensure high operational efficiency and low energ...

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Bibliographic Details
Published in2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA) pp. 1 - 6
Main Authors Parmar, Chetan, Wings, Elmar, Peetz, Thomas
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
anomaly detection
ARIMA
ensemble model
Flettner rotor
Machine learning algorithms
naive bayes forecast
near real-time modeling
Predictive models
rotor balancing
Rotors
Strain measurement
Three-dimensional displays
Vibrations
Wind speed
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Summary:This paper presents an investigation of the Flettner rotor, a wind propulsion system utilized in ships to decrease fuel consumption and reduce carbon emissions by leveraging the Magnus effect. Proper balancing and stability of the rotor are crucial to ensure high operational efficiency and low energy consumption. In the present work, a 3D printed rotor, which is a scaled-down version of the "Water Taxi", is developed to predict the unbalanced forces and vibrations of the rotor. Two methods are presented in this paper for the determination of the magnitude and direction of unbalanced forces by utilizing strain gauge readings. The force prediction model is implemented using machine learning algorithms. Furthermore, the paper discusses potential solutions for balancing the rotor once vibrations and unbalanced forces are detected. The results of this research could potentially contribute to the development of improved Flettner rotor designs, leading to more efficient and eco-friendly shipping.
ISSN:2768-7295
DOI:10.1109/INISTA59065.2023.10310361