Experimental validation of openframe ROV model for virtual reality simulation and control
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Abstract
The hydrodynamic damping and added mass of a remotely operated vehicle (ROV) are difficult to model. This paper provided an intuitive modeling and simulation approach to obtain the hydrodynamic damping and added mass coefficients of an openframe ROV using computational fluid dynamic (CFD) approach in the preliminary design stage where extensive hydrodynamic test facilities are not available. The software MATLAB™, STAR CCM+™ and WAMIT™ are employed to compute the hydrodynamic damping coefficients and added mass coefficients of the ROV for control system design and virtual reality. Experimental validation for the heave and yaw responses in a water tank shows a close relation and insight to the simulation results for subsequent control system design.
Keywords
Modeling Simulation Remotely operated vehicle Virtual reality1 Introduction
Numerical modeling and simulation techniques are essential for many engineering applications. This technique evolved and played a major role in the industry and institutions for past few decades. The marine vehicles such as an underwater robotic vehicle (URV) for subsea exploration and installation have increased in the past few decades. The remotely operated vehicles (ROVs) are the major workhorses to carry out several tasks in deeper and riskier areas where the use of human divers is impractical. However, there are some challenges in operating the ROVs precisely; such as unpredictable disturbances like current and waves in its operating environment.
The maneuverability becomes essential tasks in designing the ROV. A typical marine vessel control system has three independent blocks denoted as guidance, navigation, and control system (GNC). A dynamics model of an ROV for designing the GNC [1] is required. Unfortunately, the six degrees of freedom (DoF) dynamics model of the ROV [2, 3, 4, 5] is harder to model than the streamlined autonomous underwater vehicle (AUV) [6, 7, 8, 9, 10, 11, 12, 13, 14, 15] which exists an analytical solution for the hydrodynamics parameters.
Traditionally, the hydrodynamic parameters and the underwater response of the ROV are determined using a labbased experimental approach. However, this method is quite costly, timeconsuming and subject to the availability of the test facilities and an adequate scale model. With the recent advancement in computer technology, the computational fluid dynamic (CFD) has been widely used for the URV [6, 8, 12, 13, 14, 16].
A towing tank test for seakeeping tests and other tests performed with freerunning models is used to determine the scale model dynamics. The Froude Similitude Laws are used on the scale model to study the flow pattern around it. A planar motion mechanism (PMM) and marine dynamic test laboratory facility are used on a real or scale model to determine the hydrodynamics coefficients of the ROV model in all DoF. Besides using the labbased approach, a recent approach which used a pulley system [17] attached near a water tank was designed to compute the hydrodynamics coefficients of a scale model. However, the errors in computation were around 30%. Also, it was more suitable for a small and streamlined design due to the constraints of the pulley system and water tank.
Recently, a pendulum type of freedecaying experiment [4, 5] on a scale ROV model was used to determine the hydrodynamic coefficients. The scaleup results were compared using the CFD software and pool test. The simulations have achieved a reasonable agreement with the experimental data in some DoFs. Subsequently, another freedecaying method using four springs [18] attached to the ROV was proposed. It enables the hydrodynamic coefficients in both longitudinal and lateral hydrodynamic coefficients to be determined. However, the linear hydrodynamics damping terms could only be estimated. The error between real and estimated value was approximately 20–30%. Another approach uses system identification way such as adaptive and leastsquarebased estimation to estimate the parameters of the ROV. It was applied to the following ROVs namely: ROV Hylas [19], ROMEO [2], Johns Hopkins University ROV (JHUROV) [20], CSCOUT AUV [21] and VideoRay ROV [22]. The results showed the adaptive method was able to predict the ROV motion better than the leastsquare method. However, both approaches required a sea trial that unfortunately depended on the test site availability and the presence of a completed ROV with the control system design implemented.
Hence, an alternative approach to determining the hydrodynamic model of the ROV. The following CFD software namely: ANSYSFLUENT™ [16], ANSYSCFX™ [12] and Phoenics™ [6] have been used. The CFD simulations have shown to be quite successful in simulating the streamlined underwater vehicles such as AUV but commonly performed on a complexshaped ROV. The estimation of the hydrodynamics coefficients of an ROV [3, 4, 5] was performed using ANSYSCFX™ and FLUENT™ and later verified by experiments in water with approximately 20% error. However, the prediction of the hydrodynamics parameters of the ROV continues to face difficulty due to the complexity and variability of the ROV’s geometry and fluid flow around its nonstreamlined body for the initial stage of control system design.
In this paper, the hydrodynamic damping parameters are obtained using STARCCM+™ followed by a systematic approach of using CAD software MULTISURF™ to model and discretize the ROV for the Wave Analysis MIT (WAMIT™) [23] to determine the hydrodynamic added mass. The MATLAB™ will provide a routine to extract all the information generated by WAMIT™ to determine the added mass coefficients of the vehicle. The completed hydrodynamic model will be validated in the water tank. Due to the limitation of the time of the testing, only the heave and yaw direction were validated with the numerical simulation. Nevertheless, there is no single paper published on identifying the hydrodynamic parameters of a complex–shaped ROV using both STAR CCM+™ and WAMIT™ then MATLAB™ for virtual reality simulation in 3D.
The paper is organized as follows. Section 2 gives an overview of the dynamic model of the ROV. It is followed by the hydrodynamic damping and added mass modeling and validation in Sects. 3 and 4, respectively. Sections 5 and 6 discuss the experimental results conducted in the water tank and virtual simulation of ROV model, respectively. Lastly, Sect. 7 concludes the paper with future works.
2 Numerical ROV modelling

Operating at slow speed (less than 1 m/s);

Rigid body and fully submerged in water (no wave and current disturbance);

Neutrally buoyant by design;

Tether (or umbilical cable) dynamics attached to ROV is not considered.
Notations used for ROV
DOF  Motion descriptions  Forces and moments  Linear and angular velocity  Positions and orientations 

1  Motion in the xdirection (surge)  X  u  x 
2  Motion in the ydirection (sway)  Y  v  y 
3  Motion in the zdirection (heave)  Z  w  z 
4  Rotation about xaxis (roll)  K  p  ϕ 
5  Rotation about yaxis (pitch)  M  q  θ 
6  Rotation about zaxis (yaw)  N  r  ψ 
3 Hydrodynamic damping model
Moment of inertia properties of ROV [24]
Moment of inertia (kg.m^{2})  

I_{xx}  2.51  I _{ xy }  0  I _{ xz }  0 
I_{yx}  0  I _{ yy }  3.38  I _{ yz }  0.01 
I_{zx}  0  I _{ zy }  0.01  I _{ zz }  1.73 
A submerged body experiences lift and drag effect while moving through the fluid. This drag component includes frictional and pressure drag. The frictional drag due to the boundary layers depends on the surface area in contact with the fluid. The damping function is a linear function of velocity, a quadratic function of velocity, a sum of both linear and quadratic terms (as used in this paper) and with higher order forms. The steady drag force experienced by the vehicle in its reference state is well known to depend on the square of the velocity and a coefficient that depends on Reynolds number (for a body sufficiently submerged in a fluid). The variation of that force on the body experienced during small perturbations to that motion has repeatedly been found to be better modeled by a linear function of velocity. The linear coefficients are therefore adequate to represent the strength and moments due to inviscid part of the flow for a lowspeed ROV.
The ROV hydrodynamic damping matrix D can be further simplified. The offdiagonal elements [1] in the hydrodynamic damping matrix D(v) are small compared to those diagonal elements on the underwater vehicle. Therefore, D(v) becomes a diagonal matrix: \({\text{diag}}\left[ {\left\{ {X_{u} ,Y_{v} ,Z_{w} ,K_{p} ,M_{q} ,N_{r} } \right\}} \right].\) A turbulence model with the unsteady 3dimensional flow was built for the Reynolds number flow condition greater than 1.0 × 10^{6}. The Shear Stress Transport (SST) model in CFD software STAR CCM+ was used. The kω SST model is one of the two common models for predicting the flow separation under adverse pressure gradient. It provides a highly accurate prediction of the amount of the flow separation under adverse eddyviscosity. To take advantage of the SST model, the boundary layer should be resolved with at least 10 mesh nodes. This is done by inspecting the y+ value on the surface of the ROV that must be around one.
Initial condition and solver control settings [24]
Setting  Value 

Initial conditions > turbulence intensity  0.1% 
Stopping criteria > maximum steps  60 
Physical timescale control  5 (s) 
Before performing the CFD, the mesh size needs to be defined properly as the shape of the boundary is important in creating pressure gradients which greatly influence the boundary layer. The mesh size is preferred to be sufficiently small to capture the geometry of the ROV. The flow near boundary layer flow can be captured using the layer inflation technique. A surface wrapping technique was used for geometry preparation before the surface meshes to ensure mesh error is kept minimal to improve the meshing quality.
The number of elements in the volume is approximately 2,069,270. The fluid domain has around 546,712 elements. Figure 3 shows the 3D of the volumetric mesh of the flow domain around the ROV. The mesh downstream of the body is finer in the wake region than near the domain boundary.
Mesh settings [24]
Mesh setting  Value 

No of mesh elements  Around 2.6 million 
Mesh spacing  0.0005 (m^{2}) 
Boundary growth rate (i.e., rate at which the boundary layer thickness grows)  Low 
Volumetric mesh type  Polyhedral, trimmer 
Damping coefficients of ROV in four principle directions [24]
Damping coefficients  Surge  Sway  Heave  Yaw  

K _{ L }  K _{ Q }  K _{ L }  K _{ Q }  K _{ L }  K _{ Q }  K _{ L }  K _{ Q }  
Values  3.221  105.3  3.291  139.6  5.682  273.8  0  6.079 
Drag moment versus angular velocity in yaw direction [24]
Angular velocity (rad/s)  Yaw moment (Nm)  Yaw moment coefficient 

0.100  0.067  0.204 
0.200  0.248  0.188 
0.300  0.542  0.183 
In summary, the results show that the lowest damping occurs in surge direction while the heave motion has the largest drag force due to its larger surface area in contact with water. The values of the linear damping coefficients are smaller than the nonlinear damping terms due to its square velocity term.
4 Hydrodynamic added mass model
The surfacebased computeraided design (CAD) software MULTISURF™ modeled the geometry of the ROV. The software MULTISURF™ aims to work with WAMIT™ for exporting the necessary files for analysis. The ROV is made of multibodies that can be created using MULTISURF™ and later use WAMIT™ to compute the added mass matrix as shown below.
Description of records used in WAMIT™ solver
File extension  Description 

.GDF  Geometry data file used to define the geometry in panel form 
.POT  Potential control file used to define input parameters in POTEN (length, gravity) 
.FRC  Force control file used to define input parameters in FORCE (desired hydrodynamic parameters) 
.OUT  Output from WAMIT 
readAM.m  Matlab mfile used to read added mass matrix 
Loworder method for sphere [24]
Panel number  Numerical results  Theoretical results  

Surge (m^{3})  Sway (m^{3})  Heave (m^{3})  Surge (m^{3})  Sway (m^{3})  Heave (m^{3})  
256  2.085  2.085  2.073  2.094  2.094  2.094 
512  2.084  2.084  2.087  
1024  2.083  2.083  2.091  
Errors  −0.5%  −0.5%  −0.1% 
Added mass of sphere at different depth [24]
Depth (m)  Added mass (kg) 

0  2.5910 
1  2.1419 
10  2.0838 
100  2.0835 
In summary, the added mass coefficients of the ROV are around 20 kg in surge direction, 53 kg in sway direction and followed by 126 kg in heave direction. The ROV has the larger added mass in heave, followed by sway and surge direction. In summary, the computations of the hydrodynamic damping and added mass were performed. The proposed numerical simulation can determine the hydrodynamic parameters.
5 Validation using experimental results
The results obtained from the simulations were validated with the experimental data [25] in a water tank. The heave and yaw motion were only validated due to the limited reliable sensor installed on the ROV. A depth sensor, Doppler Velocity Log (DVL) and Inertial Measurement Unit (IMU) were jointly used to measure the depth, velocity, and acceleration of the ROV, respectively. The water tank dimensions are: 10 m (L) × 4 m (W) × 1.8 m (D). It is equipped with an overhead crane to load and unload the ROV. The sample rate of 100 Hz (or sample period of 0.01 s) was used to sample the raw data from the sensor. The raw data are plotted against the sample count using a MATLAB script. Note that one sample count is equivalent to 0.01 s.
5.1 Heave model identification
Comparisons between simulation and experimental results in heave direction
Descriptions  Simulation  Experiment (recursive least square)  Error 

Added mass (kg)  126.14  136.40  8% 
Linear damping coefficient  5.6800  4.0874  28% 
Quadratic damping coefficient  273.805  –  – 
5.2 Yaw model identification
The added mass of the ROV in the yaw direction is then identified as shown. A sinusoidal control command (maximum torque corresponding to the heading velocity) is applied to the thruster T1 T2 T5 and T6 of the ROV. A sinusoidal control signal with 10 Hz frequency is applied to the thrusters with a value corresponding to +2.7 V to −2.7 V (for both forward and reverse direction).
Comparisons between simulation and experimental results in yaw direction
Hydrodynamic coefficients (in yaw direction)  Simulation  Experiment  Error 

Added mass (kg)  5.263  4.300  18% 
Linear damping (N.s/rad)  –  4.611  – 
Quadratic damping (Nm.s^{2}/rad^{2})  6.079  5.481  9.8% 
6 Simulation of ROV model in virtual reality
7 Conclusion
A systematic modeling of the hydrodynamic damping and added mass of a complexshaped remotely operated vehicle using few numerical software was presented. The computational fluid dynamic software STAR CCM+™ was used to determine the damping parameters of the ROV model. Additionally, potential flow code using WAMIT™ was used to predict the added mass on the ROV model obtained from MULTISURF™ using the panel method to solve the potential flow around the vehicle. The simulated results were verified with the experimental tests in the water tank. Due to test constraints, only the results on the heave and yaw direction were shown. The test results show quite a close match in the added mass for the heave direction and quadratic damping coefficient in the yaw direction. However, the remaining coefficients exhibit some errors as seen in the numerical results. Experimental tests were conducted in a water tank using the joystick as a control to move the ROV to certain desired locations in heave and yaw direction. The experimental tests exhibit some trends to the simulated results in the heave and yaw directions. In summary, the proposed method provides a viable alternative with reasonable results at an early design stage where the test facilities and workforce can be quite expensive to justify for a prototype ROV. It also provides a sufficient model and insight to the ROV behavior for better control the ROV instead of relying on “black box” approach of using nonmodel based artificial neural network.
Future works could improve the accuracy of the CFD results by comparing the numerical simulation with the ROV using realtime adaptive identification approach in sea trial. The rotating propeller modeling will be included to simulate the interaction effect between rotating propellers and propeller–hull interaction. The different control system design using various controllers will be performed.
Notes
Acknowledgements
This work was made possible by the support of the following staff involved in the research Project (KH134514/EQS0459/28 research contract). They were namely: Mr. Leonard Looi, Mr. Lim Jun Jie, Mr. Elvin Teh, Mr. Kum Hoong Cheong, Mr. Vincent Toh and all other staff who provided the joint supervision, mechanical design, technical support, test facilities, office space and feasibility study for the project.
References
 1.Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, New YorkGoogle Scholar
 2.Caccia M, Indiveri G, Veruggio G (2000) Modeling and identification of openframe variable configuration unmanned underwater vehicles. IEEE J Ocean Eng 25(2):227–240CrossRefGoogle Scholar
 3.Tang S, Ura T, Nakatani T, Thornton B, Jiang T (2009) Estimation of the hydrodynamic coefficients of the complexshaped autonomous underwater vehicle TUNASAND. J Mar Sci Technol 14:373–386CrossRefGoogle Scholar
 4.Eng Y, Lau M, Chin C (2014) Added mass computation for control of an open frame remotelyoperated vehicle: application using WAMIT and MATLAB. J Mar Sci Technol 22(2):1–14Google Scholar
 5.Chin CS, Lau MSW (2012) Modeling and testing of hydrodynamic damping model for a complexshaped remotelyoperated vehicle for control. J Mar Sci Appl 11(2):150–163CrossRefGoogle Scholar
 6.Sarkar T, Sayer PG, Fraser SM (1997) A study of autonomous underwater vehicle hull forms using computational fluid dynamics. Int J Numer Meth Fluids 25(11):1301–1313CrossRefMATHGoogle Scholar
 7.Tyagi A, Sen D (2006) Calculation of transverse hydrodynamic coefficients using computational fluid dynamic approach. Ocean Eng 33(5):798–809CrossRefGoogle Scholar
 8.Barros EA, Dantas JLD, Pascoal AM, Sa E (2008) Investigation of normal force and moment coefficients for an AUV at nonlinear angle of attack and sideslip range. IEEE J Ocean Eng 33(4):538–549CrossRefGoogle Scholar
 9.Nakamura M, Asakawa K, Hyakudome T, Kishima S, Matsuoka H, Minami T (2013) Hydrodynamic coefficients and motion simulations of underwater glider for virtual mooring. IEEE J Ocean Eng 38(3):581–597CrossRefGoogle Scholar
 10.Yeo DJ, Rhee KP (2006) Sensitivity analysis of submersibles’ manoeuvrability and its application to the design of actuator inputs. Ocean Eng 33(17–18):2270–2286CrossRefGoogle Scholar
 11.Perrault Doug, Bose Neil, O’Young Siu, Williams Christopher D (2003) Sensitivity of AUV added mass coefficients to variations in hull and control plane geometry. Ocean Eng 30(5):645–671CrossRefGoogle Scholar
 12.Yumin Su, Zhao Jinxin, Cao Jian, Zhang Guocheng (2013) Dynamics modeling and simulation of autonomous underwater vehicles with appendages. J Mar Sci Appl 12(1):45–51CrossRefGoogle Scholar
 13.de Barros EA, Pascoal A, de Sa E (2008) Investigation of a method for predicting AUV derivatives. Ocean Eng 35(16):1627–1636CrossRefGoogle Scholar
 14.Gh Zeng, J Zhu (2010) Study on key techniques of submarine maneuvering hydrodynamics prediction using numerical method, ICCMS ‘10. In: Second international conference on computer modeling and simulation, Hainan, pp 83–87Google Scholar
 15.Kim Kihun, Choi Hang S (2007) Analysis on the controlled nonlinear motion of a test bed AUV–SNUUV I. Ocean Eng 34(8–9):1138–1150CrossRefGoogle Scholar
 16.Zhang He, Yuru Xu, Cai Haopeng (2010) Using CFD software to calculate hydrodynamic coefficients. J Mar Sci Appl 9(2):149–155CrossRefGoogle Scholar
 17.Chan WL, Kang T (2011) Simultaneous determination of drag coefficient and added mass. IEEE J Ocean Eng 36(3):422–430CrossRefGoogle Scholar
 18.A Ross, TI Fossen, TA Johansen (2004) Identification of underwater vehicle hydrodynamic coefficients using free decay tests. In: IFAC conference on control applications in marine systems, pp 1–6Google Scholar
 19.Morrison, III AT, Yoerger DR (1993) Determination of the hydrodynamic parameters of an underwater vehicle during small scale, nonuniform, 1dimensional translation, OCEANS ‘93. Engineering in Harmony with Ocean, Victoria, BC, Canada, vol 2, pp II277–II282Google Scholar
 20.Smallwood DA, LL Whitcomb (2003) Adaptive identification of dynamically positioned underwater robotic vehicles. IEEE Trans Control Syst Technol 11(4), 505–515Google Scholar
 21.Evans Jason, Nahon Meyer (2004) Dynamics modelling and performance evaluation of an autonomous underwater vehicle. Ocean Eng 31:1835–1858CrossRefGoogle Scholar
 22.Mišković Nikola, Vukić Zoran, Bibuli Marco, Bruzzone Gabriele, Caccia Massimo (2011) Fast infield identification of unmanned marine vehicles. J Field Robot 28(1):101–120CrossRefMATHGoogle Scholar
 23.Lee CH, Korsmeyer FT (1999) WAMIT user manual, Department of Ocean Engineering, MITGoogle Scholar
 24.Lin JY (2014) Computation of hydrodynamic damping and added mass for a complexshaped remotelyoperated vehicle (ROV) for sufficient control purpose. Report. School of Marine Science and Technology, Newcastle University, Tyne and Wear, UK, MayGoogle Scholar
 25.Lin WP, Chin CS, Looi LCW, Lim JJ, Teh EME (2015) Robust design of docking hoop for recovery of autonomous underwater vehicle with experimental results. Robotics 4(4):492–515CrossRefGoogle Scholar
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