Unsteady simulation of AUVs approaching seafloor by self-propulsion using multi-block hybrid dynamic grid method
Unsteady simulation of AUVs approaching seafloor by self-propulsion using multi-block hybrid dynamic grid method
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DOI:
10.1016/j.jfluidstructs.2022.103728
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发表时间:
2022-09-22
影响因子:
3.6
通讯作者:
Hu, Weifeng
中科院分区:
文献类型:
--
作者:
Wu, Lihong;Li, Shuo;Hu, Weifeng
Autonomous underwater vehicles (AUVs) have been widely applied in the vicinity of the seafloor for various purposes, including pipeline, cable, and structure inspection; bottom detection and following; and bottom-mounted docking systems. Squatting is a crucial problem that occurs when cruising in the vicinity of the seafloor, which affects the maneuverability and safety of an AUV. Therefore, it is necessary to have a high-accuracy prediction of the AUV hydrodynamic performance to obtain an automatic guidance controller for safety. In this study, the hydrodynamic performance was investigated by a physics-based unsteady simulation of a self-propelled AUV approaching seafloor using multi-block hybrid dynamic grid method, combined with six degrees of freedom (6DOF), user-defined functions (UDFs), and Arbitrary Lagrangian-Eulerian (ALE) approach. This simulation was used to explore the principle of the complex unsteady motions experienced by an AUV operating at the vertical plane in the vicinity of the seafloor. The results indicated that, for an AUV speed higher than the critical speed (0.5 m/s at H/D = 1.832, where R-e = 5.49 x 10(5)), the AUV experienced a speed loss and increased resistance, thrust, and suck force. The speed loss was due to the larger increment in resistance than that in thrust. The magnitude and direction of the trim moment were highly sensitive to variations in AUV speed, resulting from the two low-pressure regions. One region was at the AUV bow neck induced by the bow vortex and the other was at the AUV stern neck induced by the rotating propeller. The unsteady interaction between the AUV and seafloor during AUV self-propulsion was elucidated by flow analysis. (C) 2022 Elsevier Ltd. All rights reserved.