Artificial Intelligence Aided Design (AIAD) of Hull Form of Unmanned Underwater Vehicles (UUVs) for Minimization of Energy Consumption

Designing an excellent hull to reduce the path energy consumption of UUV sailing is crucial to improving UUV energy endurance. However, due to the relative velocity and attack angle between the UUV and the ocean current will frequently change during the entire path, realizing a path energy consumption-based UUV hull design will result in a tremendous amount of calculation. In this work, based on the idea of articial intelligence-aided design (AIAD), we have successfully developed a data-driven design methodology for UUV hull design. Specically, we first developed and implemented deep learning (DL) algorithm for predicting the resis- tance of the UUV with different hull shapes under different velocities and attack angles. By mixing the proposed DL algorithm and introducing the particle swarm optimization (PSO) algorithm into the UUV hull design, we proposed a data-driven AIAD methodology. A path energy consumption-based experiment has been conducted based on the proposed method- ology, where the design results showed that the proposed design methodology maintains eciency and reliability while overcoming the high design workload.