CFD Methodology for Evaluation of Hydrodynamic Coefficients of an Underwater Vehicle

2010 ◽  
Author(s):  
D Deepak ◽  
◽  
A Benjamin ◽  
V Seshadri ◽  
S N Singh ◽  
...  
Keyword(s):  
Underwater Vehicle ◽  
Hydrodynamic Coefficients

scholarly journals Prediction of Hydrodynamic Coefficients for Underwater Vehicle Using Rotating Arm Test

2016 ◽  
Vol 30 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Jae-Hun Jeong ◽  
Ji-Hun Han ◽  
Jihun Ok ◽  
Hyeong-Dong Kim ◽  
Dong-Hun Kim ◽  
...  
Keyword(s):  
Underwater Vehicle ◽  
Hydrodynamic Coefficients

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

2019 ◽  
Vol 234 (2) ◽  
pp. 520-533
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
Underwater Vehicle ◽  
Recursive Estimation ◽  
High Rate ◽  
Pole Placement ◽  
Estimation Methods ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients

The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

Study on Hydrodynamic Performance of a Dish-Shaped Underwater Vehicle

2010 ◽  
Author(s):  
Yumin Su ◽  
Zhaoli Wang
Keyword(s):  
Numerical Method ◽  
Processing System ◽  
Least Square Method ◽  
Underwater Vehicle ◽  
Least Square ◽  
Hydrodynamic Performance ◽  
Hydrodynamic Coefficients ◽  
Post Processing ◽  
Motion Mechanism ◽  
Planar Motion Mechanism

A new kind of dish-shaped underwater vehicle was designed. The maneuverability of the dish-shaped underwater vehicle (UV) is predicted in this paper. Hydrodynamic coefficients of the vehicle were calculated in numerically. The numerical method applied is one of the tools available in the commercial computational fluid dynamics software FLUENT. The dynamic mesh system and post-processing system are adopted in the numerical method. By simulating numerically straight motion, inclined motion and planar motion mechanism (PMM) experiment, the hydrodynamic performance in different states were obtained. Based on the least square method, the hydrodynamic coefficients of maneuverability were obtained. The calculated results indicate that the numerical method is suitable.

Identification of Underwater Vehicle Hydrodynamic Coefficients Using Model Tests

2010 ◽  
Author(s):  
B. Sadeghzadeh ◽  
H. Mehdigholi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Controller Design ◽  
Underwater Vehicle ◽  
Model Tests ◽  
Vehicle Design ◽  
Hydrodynamic Coefficients ◽  
Test Results ◽  
Engineering Research ◽  
University Of Technology ◽  
Marine Engineering

Predicting the hydrodynamic coefficients of an autonomous underwater vehicle (AUV) is important during vehicle design. SUT-2 is an AUV, being developed by the Marine Engineering Research Center of Sharif University of Technology in Iran (MERC). Model tests are done in the marine engineering laboratory towing tank. In this research, hydrodynamic coefficients are calculated using model test results of an autonomous underwater vehicle. Hydrodynamic forces are also analyzed. These coefficients are used for dynamic modeling and autonomous controller design.

The Mathematical Analysis of Underwater Vehicle Control System

2012 ◽  
Vol 490-495 ◽  
pp. 761-765
Author(s):  
Zhi Jie Tang ◽  
Jia Li Shen ◽  
Qing Bo He ◽  
Shu Ai Wang ◽  
Jun Luo
Keyword(s):  
Control System ◽  
Transfer Function ◽  
Mathematical Analysis ◽  
Kinematic Model ◽  
Underwater Vehicle ◽  
Vehicle Control ◽  
Hydrodynamic Coefficients ◽  
Vehicle Model ◽  
Acceleration Coefficient ◽  
Underwater Vehicle Control

In order to ensure that submersible has good performance of control , the kinematic model of underwater vehicle must be fairly accurate. At the same time, the accuracy of the kinematic model also is affected by hydrodynamic coefficients. Taking the underwater vehicle model as research object, the formula of the acceleration coefficient was deduced and hydrodynamic coefficients were calculated with the combination of theoretical and experiential formulas. Besides,the depth transfer function and heading transfer function were obtained. At last, we simulated the system and calibrated with PID

scholarly journals Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jiemei Zhao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Path Tracking ◽  
Input Delay ◽  
Hydrodynamic Coefficients ◽  
Lyapunov Theorem ◽  
Neural Network Predictive Control

A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

Estimation of the hydrodynamic coefficients of the complex-shaped autonomous underwater vehicle TUNA-SAND

2009 ◽  
Vol 14 (3) ◽  
pp. 373-386 ◽  
Author(s):  
Sulin Tang ◽  
Tamaki Ura ◽  
Takeshi Nakatani ◽  
Blair Thornton ◽  
Tao Jiang
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Hydrodynamic Coefficients

Identification of roll dynamics of an autonomous underwater vehicle

2018 ◽  
Vol 233 (4) ◽  
pp. 1056-1067 ◽  
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Underwater Vehicle ◽  
Hydrodynamic Coefficients ◽  
Proportional Integral Derivative ◽  
Unknown Parameters ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
And Performance

This article aims to identify the roll channel parameters of an autonomous underwater vehicle. These parameters include hydrodynamic coefficients, motor torque, eccentricity, misalignments and mounting imperfections. In the proposed method, an approximation of the hydrodynamic coefficients is made at first via semi-empirical methods. In the next step, a proportional–integral–derivative controller is designed with respect to the approximated coefficients. Since the approximations can be very uncertain, the robustness of stability and performance of the proportional–integral–derivative controller is evaluated throughout µ-analysis. Finally, the unknown parameters are identified using the recorded data of on-board sensors during motion of the vehicle. The identification is based on minimization of the one-step prediction error. The minimization problem is nonlinear in unknown parameters, and particle swarm optimization is used to find an optimal solution. The performance of the proposed method is exhibited through a 6-degrees-of-freedom simulation of an autonomous underwater vehicle.

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