Time-sequenced hydrodynamics prediction system for underwater vehicles based on AI edge computing

The efficient and precise acquisition of hydrodynamic results in complex interaction scenarios for unmanned underwater vehicles (UUVs) presents a significant challenge. This study proposes a time-sequenced hydrodynamics prediction system of underwater vehicle based artificial intelligence edge computing. Firstly, a SECausalNet framework is proposed and a hydrodynamic prediction system for underwater vehicles is developed based on a neuromorphic computing chip. Secondly, the computational fluid dynamics (CFD) results of the Remus UUV are validated with experimental data. Two UUV motion cases are proposed to further elucidate hydrodynamics characteristics under the complex interference of submarines. Finally, the hydrodynamics of the UUV for two motion cases is analyzed, and four datasets are constructed to fully demonstrate the predictive capability of the system. Results show that the pressure field for the free offset path case differs remarkably from that of the fixed path case, and noticeable deviations are observed in both horizontal and vertical planes from the original fixed trajectory of the UUV. The prediction aligns well with CFD results, with a sample inference time within 0.3 s. The mean squared error of the four datasets is all below 2.6 x 10-4, indicating a rapid and highly accurate assessment of disturbance moments and trajectories.