Real-Time Hydrodynamic Prediction of Underwater Vehicles Based on Pressure Sensing

Fish in nature use the lateral line system to sense surrounding flow fields in real time, enabling agile obstacle avoidance and efficient swimming. Inspired by this mechanism, integrating a similar sensing approach into underwater vehicles holds promise for enhancing maneuvering accuracy and environmental adaptability. A real-time prediction method based on surface pressure sensing and long short-term memory (LSTM) networks is proposed to realize real-time hydrodynamic prediction for underwater vehicles with high efficiency and accuracy, and to overcome the limitations of traditional methods in both speed and precision. The method employs a limited number of pressure sensors to capture local flow information. An LSTM network is then trained to establish a nonlinear mapping between surface pressure data and global hydrodynamic forces, so as to realize the real-time prediction with the local pressure information as the input and the global hydrodynamic force as the output. Using the SUBOFF submarine model as a numerical test case, key hydrodynamic coefficients—including resistance, lift, and moment—are predicted. Results demonstrate that the proposed approach achieves high prediction accuracy while maintaining computational efficiency. This work presents a new solution for real-time hydrodynamic force estimation in autonomous underwater vehicles.