Three dimensional synthetic and real aperture sonar technologies with Doppler velocity log and small fiber optic gyrocompass for autonomous underwater vehicle

Author(s):  
A. Asada ◽  
T. Ura
Author(s):  
Mohammad Saghafi ◽  
Roham Lavimi

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.


2020 ◽  
Vol 54 (6) ◽  
pp. 77-83
Author(s):  
David G. Aubrey ◽  
Jennifer Wehof ◽  
Stephen O'Malley ◽  
Rajai Aghabi

AbstractFloating LiDAR systems (FLS) and other moored environmental monitoring systems are used extensively for wind and environmental assessments in offshore wind projects. In addition, wave energy converters (WECs) are being evaluated for more extensive use in coastal and deeper waters, most of which also require anchoring to the seabed. Since these systems must be moored, heavy anchors and typically heavy chain are used to secure the mooring and measurement/WEC buoy to the seabed. Disadvantages of present mooring technology include 1) damage to the seabed and benthic communities in vicinity of the mooring, as chain sweeps over the sea bottom; 2) an unnecessarily large watch circle at the water's surface; 3) slightly increased likelihood of marine mammal entanglement; 4) mooring damage from nearby fishing activity; and 5) likelihood of mooring failure due to self-entanglement within the mooring itself. This study presents an alternative mooring using mechanically compliant, elastomeric hoses to connect the buoyed system to the bottom anchor. Modeling the two mooring types with a typical buoy used in wind resource assessments shows a significant decrease in anchor drag area and surface watch circle with the use of the elastomeric hose versus the traditional chain and polyethylene line mooring. The hose also is equipped with copper conductors and/or fiber-optic conductors, providing power and data transmission between the bottom and the surface. For WEC solutions, the elastomeric hose provides similar benefits as for FLS and environmental monitoring systems, with the added advantage of being able to transmit power to the seafloor for distribution. For one WEC application, we have developed an elastomeric solution containing not only larger copper conductors to enable power transmission but also fiber-optic conductors to permit data transfer from a garage mounted on the bottom (servicing an autonomous underwater vehicle [AUV] or unmanned underwater vehicle [UUV], for instance) to the surface buoy for onward transmission to shore.


2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880173 ◽  
Author(s):  
Ziye Zhou ◽  
Yanqing Jiang ◽  
Ye Li ◽  
Cao Jian ◽  
Yeyi Sun

This article presents a navigation method for an autonomous underwater vehicle being recovered by a human-occupied vehicle. The autonomous underwater vehicle is considered to carry underwater navigation sensors such as ultra-short baseline, Doppler velocity log, and inertial navigation system. Using these sensors’ information, a navigation module combining the ultra-short baseline positioning and inertial positioning is established. In this study, there is assumed to be no communication between the autonomous underwater vehicle and human-occupied vehicle; thus, to obtain the autonomous underwater vehicle position in the inertial coordinate, a conjecture method to obtain the human-occupied vehicle coordinates is proposed. To reduce the error accumulation of autonomous underwater vehicle navigation, a method called one-step dead reckoning positioning is proposed, and the one-step dead reckoning positioning is treated as a correction to combine with ultra-short baseline positioning by a data fusion algorithm. One-step dead reckoning positioning is a positioning method based on the previous time-step coordinates of the autonomous underwater vehicle.


2014 ◽  
Vol 568-570 ◽  
pp. 917-921
Author(s):  
Hong Bin Zhang ◽  
Jian Yuan

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.


2012 ◽  
Vol 9 (2) ◽  
pp. 135-152 ◽  
Author(s):  
Sreekar Gomatam ◽  
S Vengadesan ◽  
S K Bhattacharyya

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


2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523
Author(s):  
Lei Cai ◽  
Qiankun Sun

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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