Data-Driven Approaches for Thrust Prediction in Underwater Flapping Fin Propulsion Systems

TL;DR

This paper presents machine learning methods to accurately predict thrust in underwater flapping fin propulsion systems, enabling rapid development and control of new fin designs with limited experimental data.

Contribution

It introduces data-efficient fin shape parameterization and surrogate models for thrust prediction, improving design speed and generalization to unseen geometries.

Findings

Accurate thrust prediction for unseen fin geometries.

Data-efficient shape parameterization enhances model generalization.

Surrogate models enable fast, reliable thrust estimation.

Abstract

Flapping-fin underwater vehicle propulsion systems provide an alternative to propeller-driven systems in situations that require involve a constrained environment or require high maneuverability. Testing new configurations through experiments or high-fidelity simulations is an expensive process, slowing development of new systems. This is especially true when introducing new fin geometries. In this work, we propose machine learning approaches for thrust prediction given the system's fin geometries and kinematics. We introduce data-efficient fin shape parameterization strategies that enable our network to predict thrust profiles for unseen fin geometries given limited fin shapes in input data. In addition to faster development of systems, generalizable surrogate models offer fast, accurate predictions that could be used on an unmanned underwater vehicle control system.