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Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System

Brian Zhou, Jason Geder, Alisha Sharma, Julian Lee, Marius Pruessner, Ravi Ramamurti, Kamal Viswanath
AAAI Symposium on Knowledge-Guided Machine Learning, 2022
arXivLink
BibTeX
@misc{zhou2023computationalapproachesmodelingpower,
      title={Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System}, 
      author={Brian Zhou and Jason Geder and Alisha Sharma and Julian Lee and Marius Pruessner and Ravi Ramamurti and Kamal Viswanath},
      year={2023},
      eprint={2310.14135},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2310.14135}, 
}
Computational Approaches for Modeling Power Consumption on an Underwater Flapping Fin Propulsion System

Abstract

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations. To optimize for different gait performance metrics, we develop a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, that is able to evaluate different fin designs and kinematics, and allow for comparison with other bio-inspired platforms. We create and train computational models using experimental data, and use these models to predict thrust and power under different fin operating states, providing efficiency profiles. We then use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. These comparisons provide a better understanding of how fin materials affect our thrust generation and propulsive efficiency, allowing us to inform control systems and weight for efficiency on an inverse gait-selector model.

See also