Calm Water Resistance Prediction of a Container Ship ... - ScienceDirect

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ScienceDirect Procedia Engineering 194 (2017) 25 – 30

10th International Conference on Marine Technology, MARTEC 2016

Calm Water Resistance Prediction of a Container Ship using Reynolds averaged Navier-Stokes based Solver Hafizul Islama , Md. Mashiur Rahamanb , Hiromichi Akimotoc , M. Rafiqul Islamb a CENTEC, Instituto Superior Tecnico, Tecnico Lisboa, Portugal of Naval Architecture and Marine Engineering, BUET,Dhaka-1000,Bangladesh c Center for the Advancement of Research and Education Exchange Network in Asia (CAREN), Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan 565-0871 b Department

Abstract Maneuverability and resistance prediction with suitable accuracy is essential for optimum ship design and propulsion power prediction. This paper aims at providing some of the resistance and maneuverability characteristics of a container ship model, MOERI KCS in calm water using a computational fluid dynamics solver named Ship Motion. The solver is based on the Reynolds average Navier-Stokes method (RaNS) and solves overset-structured grid using the Finite Volume Method (FVM). This paper comprises the numerical results of calm water test for the KCS model with available experimental results. The calm water test results include the total drag coefficient, average sinkage, and trim data. Visualization data for pressure distribution on the hull surface and free water surface have also been included. The paper concludes that the present solver has the capability to predict the resistance and maneuverability characteristics of the container ship with reasonable accuracy utilizing minimum computational resources. c 2017 by Elsevier Ltd.is an open access article under the CC BY-NC-ND license 2017The The Authors. Published © Authors. Published by Elsevier Ltd. This (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 10th International Conference on Marine Technology. Peer-review under responsibility of the organizing committee of the 10th International Conference on Marine Technology. Keywords: container ship; CFD; calm water; RaNS; resistance

1. Introduction Prediction of ships maneuverability characteristics and resistance is essential at early phase of ship design to understand its design efficiency. Although model tests are most popular for such predictions, in recent years, CFD has gained high popularity for such predictions because of its high efficiency and economics. CFD is very efficient because it allows exploration of new engineering design frontiers in a very economical way. Furthermore, over the years, the accuracy of CFD prediction has improved significantly. Thus, more and more research is being done on how to predict the stability and maneuvering capabilities of a ship in the design phase using CFD. Maneuverability prediction in the design phase is nothing new for the shipping industries. According to reports of 20th 24th IITC[1–3], the first symposium on Naval Maneuverability was held in Washington DC in 1960. However, back then work was mostly experiment based. Numerical prediction for maneuverability using the boundary layer method was first introduced in 1980. However, it failed to predict the pressure distribution in the stern part properly. This had led to the development of various RaNS models throughout the 1980s. In 1990, it was found that RaNS ∗

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1877-7058 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 10th International Conference on Marine Technology.

doi:10.1016/j.proeng.2017.08.112

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Hafizul Islam et al. / Procedia Engineering 194 (2017) 25 – 30

models showed high promise but failed to predict detail shape of velocity contours in central part of wake. Later in 1993, Deng et al.[3] revealed that the problem was in inadequate turbulence modeling. The 1994 Tokyo workshop[3] brought a breakthrough in free surface calculation using RaNS. For The Gothenburg 2000 workshop[4], three new ship models- KCS, KVLCC and DTMB 5415 were introduced with experimental results for validation. The selfpropulsion system included CFD models were also introduced in this workshop. In The SIMMAN 2008 workshop[1], benchmarking was conducted for both EFD based and CFD based methods. The Gothenburg 2010 workshop[4] discussed global and local flow variables, grid dependency, and turbulence modeling. Over the years, CFD in maneuverability prediction has gone through and is still going through continuous improvement. However, with increase in prediction accuracy, computational time has increased as well, despite the fact that, computing capacity has increased significantly over the years. This is mainly because of our tendency to simulate exact real life conditions. The requirement of high computational resources has driven us to high performance computers and reduced the economy of CFD. The solver used for this paper uses the RaNS equation and the finite volume method with combined use of the Baldwin-Lomax and Dynamic Sub-grid scale turbulence model. This paper contains validation data for calm water test results of a KCS model using the solver. 2. Method Detailed numerical modeling of the solver has been discussed by several other previous publications by Orihara[5], Akimoto[6], Kim[7] and Islam[8]. Therefore, only a short description has been provided in this paper. 2.1. Numerical Modeling The developed solver, SHIP Motion, uses the Reynolds averaged Navier-Stokes (RaNS) equation and the continuity equation as the governing equations and can solve motions with 6 degrees of freedom (DOF). Interface modeling is done using the marker density function method. For turbulence modeling, it uses the Baldwin-Lomax and dynamic sub-grid model. The weighted average of the two models is used for local turbulence viscosity. Physical values are defined in the staggered manner in control volume, i.e., pressure is defined at the volume center, whereas velocity components are defined at the face centers of the primary hexahedral control volume. Tri-linear interpolation of flow variables is done between two domains. 2.2. Discretization Method SHIP Motion uses the finite volume method (FVM) for special discretization. The numerical discretization is performed using central difference and upwind scheme. SHIP Motion uses 3rd order upwind scheme for differencing of advection and 2nd order central differencing for other discretization in space. Temporal discretization is performed by Adams-Bashforth 2nd order explicit method. 2.3. Meshing In this research, an overset structured single block mesh system is used for simulation. The coarse rectangular outer mesh with high resolution around the free surface is used to capture the free surface deformation. The fine O-H type inner mesh around the hull surface is used for capturing the flow properties around the hull surface. x-z plane symmetry condition is used in head wave and calm water condition. Fig.1 below shows the mesh applied. 3. Ship model and particulars The model ship used for this research is the KRISO Container ship (KCS). It is a 3600TEU capacity container ship designed by KRISO (formerly MOERI) for research purpose. The ship is a very popular test model, as many experimental and CFD test results are open to public and have been discussed in many workshops and conferences like Gothenburg, Tokyo and SIMMAN workshops. Fig.2 shows the body plan of KCS model, and the ship specifications are provided in Table 1. The mesh used in the simulation is non-dimensional.

4. Computational Procedure The simple solver with zero-equation Baldwin-Lomax turbulence model and the structured mesh around the bare hull, make the present simulation light and fast. However, the OpenMP memory sharing model used in the solver

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Fig. 1: Overset structured mesh (inner mesh and outer mesh)

Fig. 2: Body Plan of KCS model Table 1: Specification of KCS Particulars

Unit

KCS ship (full scale)

Length between perpendiculars, Lpp Breadth, B Depth,D Draft,T Wetted Surface Area,S Displacement Volume,V LCB from midship(fwd+), LCB Kyy

[m] [m] [m] [m] [m2 ] [m3 ] [%] [%]

230.00 32.20 19.00 10.80 9530.00 52030.00 -1.48 0.25Lpp

limits it to distributing computation only in CPU cores of the same node, but not in multi nodes. The simulations were performed in a single node of an Intel(R) Core i7 CPU with 8 cores, clock speed 2.27 GHz and 4 GB of physical memory. The average time step used was 1.5 × 10-4 non-dimensional time and for simulating each non-dimensional time, the required physical time was about 3 hours per case. All the simulations were run up to 7 non-dimensional times for attaining stable results. 5. Results and Discussions Calm water resistance prediction is the estimation of drag force for a ship while moving forward in calm water. Ship’s drag resistance is summation of frictional resistance and pressure resistance. Frictional resistance arises from the hull surface friction and pressure resistance is mainly the wave making resistance encountered by ship during its forward motion. When ship moves forward, the bulbous bow create waves on the free surface, thus energy is transferred from ship to free surface and this loss is termed as wave making resistance. Thus, higher resolution at the bow and stern sections and also at the free surface near these sections are essential to attain a good prediction. As for mesh distribution, the emphasis is on capturing the boundary layer near the hull surface. To validate the present

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solver, calm water simulation has been performed for KCS model for different Froude and Reynolds numbers. The mesh resolution for simulation has been selected from previous experience with the solver and thus mesh dependency study has not been shown here. However, mesh dependency of the solver for different ship models has been shown in several publications by Ock[9] and Islam[8,10]. Thus, it wasnt repeated here. The mesh resolution used for simulation has shown in Table 2. Table 2: Mesh configuration for simulation of KCS in calm water Specification

Domain

KCS ship

Degrees of freedom Number of grid point (ni × n j × nk)

— inner outer inner (half domain) outer (half domain) inner outer inner outer inner outer

2DOF (Heave and Pitch) 185 × 27 × 106 192 × 45 × 63 1.65L × 0.30L(R) 3.8L × 1L × 1L 0.0012 0.0187 0.0008 0.0198 0.0008 0.00068

Computational region or domain size (×Lpp ) Minimum grid spacing in longitudinal direction Minimum grid spacing in radial/lateral direction Minimum grid spacing in girth-wise/vertical direction

Table 3: Comparison of EFD and CFD results of total drag for KCS Fn

Rn

C T × 10-3 (CFD)

C T × 10-3 (EFD)

Deviation (%)

0.108 0.152 0.195 0.227 0.260 0.282

5.23 × 1006 7.33 × 1006 9.42 × 1006 1.10 × 1007 1.26 × 1007 1.36 × 1007

3.580 3.500 3.330 3.368 3.830 4.490

3.796 3.641 3.475 3.467 3.711 4.501

5.69 3.87 4.17 2.86 -3.21 0.24

Table 4: Comparison of EFD and CFD results of sinkage and trim for KCS Fn

Rn

σ × 10-2 [m] (CFD)

σ × 10-2 [m] (EFD)

Deviation (%)

τ0 [deg.] (CFD)

τ0 [deg.] (EFD)

Deviation (%)

0.108 0.152 0.195 0.227 0.260 0.282

5.23 × 1006

-0.0989 -0.1012 -0.1030 -0.1081 -0.1104 -0.1150

-0.0900 -0.2750 -0.5990 -0.9440 -1.3940 -1.7020

-9.89 63.20 82.20 88.55 92.08 93.24

-0.02 -0.0423 -0.08 -0.11 -0.13 -0.1035

-0.017 -0.053 -0.097 -0.127 -0.169 -0.159

-17.65 20.19 17.53 13.39 23.08 34.91

7.33 × 1006 9.42 × 1006 1.10 × 1007 1.26 × 1007 1.36 × 1007

Total six cases were simulated for different Froude and Reynolds number and for validation; results were compared with the experimental data presented in the Tokyo 2015 workshop[11]. The simulation data has been processed to predict total drag coefficient, sinkage and trim values of the ship. The results have been shown in Table 3 and 4.The results have also been shown in Figs.3 and 4 to make it easier to comprehend. It can be seen from Fig.3 that the prediction of total drag are in good agreement with experimental data. The deviation margin decreases with increase in Froude number, Fn . However, at design speed, the deviation becomes negative. Generally, at design speed, ships perform optimally and the flow pattern around hull form changes slightly. Since a comparatively lower mesh resolution has been used for the presented cases, the solver was unable to capture these changes properly and ended up over predicting the value. Also from Fig.4 for sinkage result, although the


Fig. 3: Total drag co-efficient for KCS model at various Froude no., Fn

a

b

Fig. 4: Sinkage and Trim Prediction for KCS in Head Waves using Ship Motion (a) Sinkage ; (b) Trim

deviation is high in terms of percentage the actual difference is only around 2 centimeter but for the trim, results are well predicted as well.

Fig. 5: Pressure distribution on the hull surface and free surface in calm water motion of KCS at Fn =0.26

As mentioned before in this section, during calm water motion, maximum resistance is encountered by the bow front of the ship. Pressure distribution on hull surface during calm water motion is shown in Fig.5 to illustrate this

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phenomenon. Pressure distribution on water surface is shown. In the shown simulation result, the Froude number is 0.26 and Reynolds number is 1.26 × 107 . Another Fig.A.6 for pressure distribution is shown in Appendix. 6. conclusion Calm water simulation results for a container model, KCS, has been shown in the paper. The paper presented resistance, sinkage and trim results for different Froude numbers and showed comparison with experimental data. The average deviation in case of resistance prediction is found to be around 2%, which is very reasonable considering the used mesh resolution of around 1 million. The sinkage result shows a significant percentage of deviation, however, in actual, the deviation value is in centimeter scale. This deviation mainly comes from the mesh resolution and the prediction can be improved by applying a higher mesh resolution. However, considering the actual value of deviation, it may not be economical to do so. As for trim result, the average deviation is around 17%. In motion prediction, deviation with experimental data increases with increase in Froude number. This is mainly because of the increase in turbulence with increasing Reynolds and Froude number. Overall, it may be concluded that the solver SHIP Motion is efficient and economical in predicting ship resistance and maneuverability characteristics. It predicts ship resistance and motion with acceptable accuracy, within reasonable time, using minimum computational resources. Appendix A.

Fig. A.6: Pressure distribution on the hull surface in calm water motion of KCS

References [1] Summary of proceedings of SIMMAN 2008 Workshop, 2008. [2] Larsson, Lars, Stern, Frederick and Bertram, Volker, ”Benchmarking of Computational Fluid Dynamics for Ship Flows: The Gothenburg 2000 Workshop 2003”, Journal of Ship Research, Vol. 47, pp. 63-81. [3] Proceedings of CFD Workshop Tokyo 1994: An International Workshop for Improvement of Hull Form, 22-24 March, 1994, Japan [4] Lars Larsson, Frederic Stern and Michel Visonneau, ”CFD in ship hydrodynamics: results of the Gothenburg 2010 workshop”, In L.Eca et al. (eds.) MARINE 2011, IV International Conference on Computational Methods in Marine Engineering, Computational Methods in Applied Sciences, 2011. [5] Orihara, Hideo. Development and Application of CFD simulation technology for the performance estimation of ship in waves. Department of Environmental and Ocean Engineering, School of Engineering, The University of Tokyo. 2005. Ph.D. dissertation. [6] Akimoto, Hiromichi and Miyata, Hideaki, ”Finite-volume simulation method to predict the performance of sailing boat”, Journal of Marine Science and Technology, Vol. 7, 2002, pp. 31-42. [7] Kim Hyuncheol, Akimoto Hiromichi and Islam Hafizul, ”Estimation of the hydrodynamic derivatives by RaNS simulation”, Ocean Engineering, Vol. 108, 2015, pp. 129-139. [8] Islam, Hafizul. Prediction of ship resistance in oblique waves using RaNS based solver. Master’s Thesis, Division of Ocean Systems Engineering. s.l. : MS Thesis, Division of Ocean Systems Engineering, KAIST, 2015. [9] Ock, Yu Bin. numerical Simulations of Added Resistance around Ships in Regular Head Waves using Overset Grids. Department of Naval Architecture and Ocean Engineering, Pusan National University. 2014. Master’s thesis. [10] Islam, Hafizul and Akimoto, Hiromichi, ”Prediction of ship resistance in Head Waves Using RaNS based solver”, International Conference on Mechanical Engineering (ICME), BUET, Dhaka, December 2015. [11] Tokyo 2015 Workshop. [Online] 2015. http://www.t2015.nmri.go.jp/.