Three-Dimensional Passive Source Localisation using the Flank Array of an Autonomous Underwater Vehicle in Shallow Water

2013 ◽  
Vol 63 (3) ◽  
pp. 323-330
Author(s):  
Dexin Zhao ◽  
Keunhwa Lee ◽  
Woojae Seong ◽  
Zhiping Huang
Keyword(s):  
Shallow Water ◽  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Source Localisation

scholarly journals Optimal design of nose and tail of an autonomous underwater vehicle hull to reduce drag force using numerical simulation

2019 ◽  
Vol 234 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Mohammad Saghafi ◽  
Roham Lavimi
Keyword(s):  
Energy Consumption ◽  
Drag Force ◽  
Autonomous Underwater Vehicle ◽  
Turbulence Modeling ◽  
Stokes Equations ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Navier Stokes

In this research, the flow around the autonomous underwater vehicles with symmetrical bodies is numerically investigated. Increasing the drag force in autonomous underwater vehicles increases the energy consumption and decreases the duration of underwater exploration and operations. Therefore, the main objective of this research is to decrease drag force with the change in geometry to reduce energy consumption. In this study, the decreasing or increasing trends of the drag force of axisymmetric bare hulls have been studied by making alterations in the curve equations and creating the optimal geometric shapes in terms of hydrodynamics for the noses and tails of autonomous underwater vehicles. The incompressible, three-dimensional, and steady Navier–Stokes equations have been used to simulate the flow. Also, k-ε Realizable with enhanced wall treatment was used for turbulence modeling. Validation results were acceptable with respect to the 3.6% and 1.4% difference with numerical and experimental results. The results showed that all the autonomous underwater vehicle hulls designed in this study, at an attack angle of 0°, had a lower drag force than the autonomous underwater vehicle hull used for validation except geometry no. 1. In addition, nose no. 3 has been selected as the best nose according to the lowest value of stagnation pressure, and also tail no. 3 has been chosen as the best tail due to the production of the lowest vortex. Therefore, geometry no. 5 has been designed using nose and tail no. 3. The comparison made here showed that the maximum drag reduction in geometry no. 5 was equal to 26%, and therefore, it has been selected as the best bare hull in terms of hydrodynamics.

Modelling of Full-Actuated Autonomous Underwater Vehicle

2014 ◽  
Vol 568-570 ◽  
pp. 917-921
Author(s):  
Hong Bin Zhang ◽  
Jian Yuan
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Dimensional Space ◽  
Gravity Current ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Modelling Method ◽  
Moment Of Resistance ◽  
Dynamics Models ◽  
Three Dimensional Space ◽  
Current Resistance

The modelling method of a full-actuated autonomous underwater vehicle is investigated.The kinematics and dynamics models of the full-actuated autonomous underwater vehicle in three-dimensional space are constructed. Gravity and moment of gravity,current resistance and moment of resistance, buoyancy and moment of buoyancy and thrust and moment of thrust are constructed, respectively. Experiment results show the effectiveness of the proposed modelling method.

scholarly journals Numerical simulations of flow past an autonomous underwater vehicle at various drift angles

2012 ◽  
Vol 9 (2) ◽  
pp. 135-152 ◽  
Author(s):  
Sreekar Gomatam ◽  
S Vengadesan ◽  
S K Bhattacharyya
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Symmetry Plane ◽  
Underwater Vehicle ◽  
Navier Stokes ◽  
Structure Variation ◽  
Flow Past ◽  
Computational Fluid Dynamics Cfd ◽  
Marine Engineering ◽  
Flow Variables

Three dimensional (3D) flow past an Autonomous Underwater Vehicle (AUV) is simulated using a Computational Fluid Dynamics (CFD) approach at a Reynolds (Re) number of 2.09x106. A non-linear k-? (NLKE) turbulence model is used for solving the Reynolds Averaged Navier-Stokes (RANS) equations. The effect of control surfaces over the flow, the flow interaction between the hull and the appendages at various Angles of Attack (AoA) and the effect of the symmetry plane is studied. Flow structure, variation of flow variables and force distribution for various AoA are presented and discussed in detail.DOI: http://dx.doi.org/10.3329/jname.v9i2.12567 Journal of Naval Architecture and Marine Engineering 9(2012) 135-152

scholarly journals Multiautonomous underwater vehicle consistent collaborative hunting method based on generative adversarial network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523
Author(s):  
Lei Cai ◽  
Qiankun Sun
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Laplacian Matrix ◽  
Underwater Vehicle ◽  
Underwater Acoustic Communication ◽  
Constant Modulus Algorithm ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
The Ideal

The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.

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