Shape optimization of an autonomous underwater vehicle with a ducted propeller using computational fluid dynamics analysis

Joung, Tae-Hwan; Sammut, Karl; He, Fangpo; Lee, Seung-Keon

doi:10.2478/ijnaoe-2013-0077

Cited by 53 publications

(19 citation statements)

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“…In this part, some of the necessary preparatory work for the hydrodynamic simulation is depicted in detail. All work in this paper was processed with CFX [23,24] associated with ANSYS WORKBENCH [32].…”

Section: Hydrodynamic Simulation Details Of the Thrustermentioning

confidence: 99%

“…In order to analyze the influence of the nozzle and find the most appropriate nozzle diameter, the thrust of the thruster with different nozzle diameters is predicted. It is inconvenient to build the 3D model of the water-jet thruster for each nozzle diameter in the simulation, therefore, SOLIDWORKS and ANSYS-Design Exploration [32] are combined to compute thrust in different cases. The nozzle diameter is set as a recognizable parameter in SOLIDWORKS.…”

Section: Simulation Analysismentioning

confidence: 99%

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Hydrodynamic Analysis-Based Modeling and Experimental Verification of a New Water-Jet Thruster for an Amphibious Spherical Robot

Hou

Guo

Shi

et al. 2019

Sensors

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Thrusters are the bottom actuators of the amphibious spherical robot, and play an important role in the motion control of these robots. To realize accurate motion control, a thrust model for a new water-jet thruster based on hydrodynamic analyses is proposed in this paper. First, the hydrodynamic characteristics of the new thruster were numerically analyzed using computational fluid dynamics (CFD) commercial software CFX. The moving reference frame (MRF) technique was utilized to simulate propeller rotation. In particular, the hydrodynamics of the thruster were studied not only in the axial flow but also in oblique flow. Then, the basic framework of the thrust model was built according to hydromechanics theory. Parameters in the basic framework were identified through the results of the hydrodynamic simulation. Finally, a series of relevant experiments were conducted to verify the accuracy of the thrust model. These proved that the thrust model-based simulation results agreed well with the experimental results. The maximum error between the experimental results and simulation results was only 7%, which indicates that the thrust model is precise enough to be utilized in the motion control of amphibious spherical robots.

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Section: Hydrodynamic Simulation Details Of the Thrustermentioning

confidence: 99%

Section: Simulation Analysismentioning

confidence: 99%

Hydrodynamic Analysis-Based Modeling and Experimental Verification of a New Water-Jet Thruster for an Amphibious Spherical Robot

Hou

Guo

Shi

et al. 2019

Sensors

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“…In order to obtain a low-drag shape, the Myring Equation [17] is used to design the shape of the AUV nose. The Myring Equation is widely used in the design of AUV hulls [10]. The expression for the nose shape is determined by…”

Section: Geometry Configurationmentioning

confidence: 99%

“…Kim et al proposed a Computational Fluid Dynamics (CFD) method for the optimization of monohull ships with the minimum drag [9]. Joung et al proposed a procedure using the CFD method and a Design of Experiments (DOE) optimization for the hull optimization [10]. Sun et al established an energy consumption model for an underwater glider and optimized the shape of the glider with a combined CFD and Efficient Global Optimization (EGO) method [11].…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

et al. 2017

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This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a backpropagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

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“…Myring [2] used the data of the Royal Aviation Association in his study to confirm the numerical analysis results obtained for a series of In this case, a method has been developed to determine the resistance that affects the shape of the body in the axial symmetric flow [2]. Additionally, design of different AUVs in terms of body resistance, propulsion efficiency and other hydrodynamic properties are investigated using the CFD methods and/or tests or both in detail in [3][4][5][6][7][8][9][10]. The main objective of this study is to develop a proper unmanned underwater surveying vehicle to conduct research on the geomorphological, geological and geophysical aspects of the structure of the sea bottom up to the deep of 2,000 m and on the Earth's mantle beneath the seas as well oceanographic opinions, with the maximum und service speed in operation 8 kn and 2-3 kn, respectively.…”

Section: Introductionmentioning

confidence: 99%

Form Development and Validation of an Autonomous Underwater Vehicle

2017

SBNA

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Shape optimization of an autonomous underwater vehicle with a ducted propeller using computational fluid dynamics analysis

Cited by 53 publications

References 1 publication

Hydrodynamic Analysis-Based Modeling and Experimental Verification of a New Water-Jet Thruster for an Amphibious Spherical Robot

Hydrodynamic Analysis-Based Modeling and Experimental Verification of a New Water-Jet Thruster for an Amphibious Spherical Robot

Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

Form Development and Validation of an Autonomous Underwater Vehicle

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