Comparative CFD Simulations of a Soft Robotic Fish for Undulatory Swimming Behaviors

Abstract

Studies on autonomous underwater vehicles (AUVs) have gained momentum in recent years, and a special type of AUV, the robotic fish, has become a significant topic, with a superior maneuverability to traditional AUVs. In this paper, a prediction strategy for the hydrodynamic performance of a robotic fish to analyze undulatory swimming behaviors is proposed. The two-dimensional robotic fish model for computational fluid dynamics (CFD) simulations is constructed, and a dynamic network method is applied to orient the generated network based on the wavy motion. For the thrust force of the fin, a body traveling wave is derived. In the simulations, the effects of kinematic parameters such as flapping frequency and speed on swimming efficiency and drag are analyzed, and thrust force production, power expenditure, and overall efficiency of swimming are examined. Later, a deep learning-based prediction model is designed from the obtained parameters, and force predictions are performed. Long short-term memory (LSTM)-, convolutional neural network (CNN)-, and gated recurrent network (GRU)-based time series prediction models are used, and their variations are compared. In these experiments, while the CNN-GRU achieves the higher prediction performance for the root mean square error, with 0.0228, other approaches give a lower performance, between 0.0233 and 0.0359. The proposed method demonstrates a superior performance in CNN and LSTM models and exhibits lower prediction errors.