Trans-media resistance investigation of hybrid aerial underwater vehicle base on hydrodynamic experiments and machine learning

Abstract

In this paper, the trans-media hydrodynamics of a multi-rotor hybrid aerial underwater vehicle (HAUV) is studied base on experiments and machine learning. First, experiments are conducted to obtain the hydrodynamic forces during water exit/entry. Then, based on the experimental data and phenomena, it is evident that the multi-rotor HAUV's total resistance, particularly the impact of the arms, should not be ignored. The overall resistance is primarily affected by the depth and velocity at constant velocity. The total resistance increases as the velocity increases. The maximum total resistance in water exit/entry can be 46%/64% of gravity. Finally, a two-layer feed-forward network with six sigmoid hidden neurons and linear output neurons is used for machine learning training. The training results show that using experimental data and a machine learning method, the trans-media resistance of HAUV can be accurately predicted within a certain range.