As a new concept of special AUV, the Ocean Bottom Flying Node (OBFN) can well complete the tasks of offshore oil exploration, water cruising and monitoring. However, due to the limitation of their own space, the OBFN can only carry a small amount of energy. In order to improve their navigation efficiency and better complete their operational tasks, the OBFN is required to have good hydrodynamic performance. In this paper, the drag reduction performance and volume space are used as optimization parameters, and the OBFN configuration is optimized through optimization theory. Taking into account the internal space volume, a better drag reduction configuration is obtained.

1. INTRODUCTION

In order to meet the needs of large-scale network deployment in seismic exploration applications, Harbin engineering University develops a new concept special Autonomous Underwater Vehicle (AUV)—Ocean Bottom Flying Node (OBFN), which combines Ocean Bottom Node (OBN) and AUV techniques. Equipped with multi-functional sensors, it can also meet multi-task requirements such as long-term sitting, sea cruise, marine resource exploration, and submarine seismic monitoring.

The original OBFN model is shown in Fig. 1.

The OBFN only relies on its own energy when sailing underwater. Limited by its internal space, the OBFN can only carry a small amount of fuel, so it is necessary to improve navigation efficiency and reduce energy consumption. Since the large number of internal parts and the complicated external molding, the optimization design of OBFN is a huge challenge.

At present, the traditional optimization methods are more from the personal experience of the designer, which is not only inefficient, but also often cannot obtain the global optimal solution. In recent years, breakthroughs have been made in Computational Fluid Dynamics (CFD) and modern optimization theory, which has greatly accelerated the development of hydrodynamic optimization. AUV hydrodynamic optimization presents the characteristics of multidisciplinary cross-cutting and multi-objective optimization. A large number of scholars have underwater vehicle optimization.

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