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.

翻译：扑翼式水下航行器推进系统在受限环境或需要高机动性的场景中，为螺旋桨驱动系统提供了替代方案。通过实验或高保真仿真测试新配置成本高昂，阻碍了新系统的开发进程，这在引入新型鳍片几何结构时尤为突出。本研究提出基于机器学习的推力预测方法，依据系统的鳍片几何形态与运动学参数进行预测。我们引入了数据高效的鳍形参数化策略，使神经网络能够在输入数据中鳍形有限的情况下，预测未见鳍片几何结构的推力分布。除加速系统开发外，可泛化的代理模型还能为无人水下航行器控制系统提供快速精准的推力预测。