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Structural and Operational Optimization of A Flapping Fin Used as A Self-Propulsor for AUV Propulsion

China Ocean Engineering 2022年第1期36卷 86-99页

作者： FAN Shuang-shuang CI Xiao-han neil bose CHENG Xiao School of Marine Sciences Sun Yat-sen UniversityZhuhai 519082China Southern Laboratory of Ocean Science and Engineering Zhuhai 519000China Department of Ocean and Naval Architectural Engineering Memorial University of NewfoundlandSt.John’sNL A1C 5S7Canada School of Geospatial Engineering and Science Sun Yat-sen UniversityZhuhai 519082China

Marine mammals could directly harvest energy from waves and obtain propulsive force through oscillating flapping fins or horizontal tail flukes,which in many cases have been observed and proved to be substantial.The propulsion generated by the flapping fin has been analyzed by many researchers from both the theoretical and experimental prospects;however,the structural and operational optimization of a flapping fin for the optimal propulsion performance has been less studied,such as the investigation of the effects of the phase difference between heave and pitch motion,maximum oscillation angle,fin shape,oscillation centre of the fin and the operating sea state on the generated propulsion.In this paper,the flapping fin is used as a self-propulsor to propel an autonomous underwater vehicle(AUV)for propulsion assistance.For the optimization design of the flapping fin,its propulsion effect is numerically investigated with different structural parameters and under various operation conditions using computational fluid dynamics(CFD)approaches.Verification and validation study have been implemented to quantify the numerical uncertainties and evaluate the accuracy of the proposed CFD method.Then,a series of case studies are thoroughly conducted to investigate the effects of different structural parameters and operational conditions on the generated propulsion of a flapping fin by CFD simulations.The simulation results demonstrate that different structural parameters and operation conditions would significantly impact the magnitude and distribution state of the fluid pressure around the flapping fin surface,thus,affect the propulsion performance of the fin.The findings in this study will provide guidelines for the structural and operational optimization design of a flapping fin for self-propulsion of mobile platforms.

关键词： autonomous underwater vehicle(AUV) flapping fin self-propulsion computational fluid dynamics(CFD) optimization design

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Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 33-50页

作者： Hui Sheng Lim Christopher K.H.Chin Shuhong Chai neil bose National Centre for Maritime Engineering and Hydrodynamics Australian Maritime CollegeUniversity of TasmaniaLauncestonTAS7250Australia Memorial University of Newfoundland St.John’sNLA1C 5S7Canada

This paper presents an online AUV(autonomous underwater vehicle)path planner that employs path replanning approach and the SDEQPSO(selective differential evolution-hybridized quantum-behaved particle swarm optimization)algorithm to optimize an AUV mission conducted in an unknown,dynamic and cluttered ocean environment.The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time.The optimization was based on the onboard sensor data measured from the environment,which consists of a priori unknown dynamic obstacles and spatiotemporal currents.Different sensor arrangements for the forward-looking sonar and horizontal acoustic Doppler current profiler(H-ADCP)were considered in 2D and 3D simulations.Based on the simulation results,the SDEQPSO path replanner was found to be capable of generating a time-optimal path that offered up to 13%reduction in travel time compared to the situation where the vehicle simply followed a path with the shortest distance.The proposed replanning technique also showed consistently better performance over a reactive path planner in terms of solution quality,stability,and computational efficiency.Robustness of the replanner was verified under stochastic process using the Monte Carlo method.The generated path fulfilled the vehicle’s safety and physical constraints,while intelligently exploiting ocean currents to improve the vehicle’s efficiency.

关键词： Autonomous underwater vehicle path planning particle swarm optimization sonar detection Monte Carlo methods

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