Zhou Brian, Viswanath Kamal, Geder Jason, Sharma Alisha, Lee Julian
Laboratories for Computational Physics and Fluid Dynamics, United States Naval Research Laboratory, Washington, DC 20375, USA.
Harvard College, Cambridge, MA 02138, USA.
Biomimetics (Basel). 2024 Jul 17;9(7):434. doi: 10.3390/biomimetics9070434.
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. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.
在过去几十年中，专注于仿生无人水下航行器（UUV）推进和控制系统的研究不断兴起，这类航行器在水下任务中为传统UUV提供了更具机动性的选择。最近的研究探索了使用时间序列神经网络替代模型，根据航行器设计和鳍的运动学来预测推力和功率。我们在此基础上进行拓展，创建了新的前馈神经网络模型，该模型封装了鳍的材料刚度对其运动性能、推力和功率的影响，并且能够对每种材料的全谱运动步态进行插值。值得注意的是，我们通过对保留数据的测试表明，我们开发的前馈模型能够以高分辨率捕捉与每组参数相关的推力和功率，通过适当的材料和运动学选择，能够对以前未见过的步态以及推力和性能指标（FOM）增益进行高度准确的预测。由于推进效率对于扑翼式UUV至关重要，它能扩展UUV在关键任务中的航程和续航能力，因此我们使用从推进效率测量中得出的无量纲性能指标（FOM）来评估不同的鳍设计和运动学，并与其他仿生平台进行比较。我们使用开发的FOM来分析最优步态，并比较不同鳍材料之间的性能。前馈模型展示了以良好精度捕捉最高推力和FOM的能力，这使我们能够通过适当选择鳍的刚度和运动学，将推力产生提高83.89%，效率提高137.58%，从而改进仿生鳍设计的材料选择。
