Shen and Korpus - 2015 - Numerical Simulations of Ship Self-Propulsion and

NUMERICAL SIMULATIONS OF SHIP SELF-PROPULSION AND MANEUVERINGUSING DYNAMIC OVERSET GRIDS IN OPENFOAM Zhirong Shen (American Bureau of Shipping, USA) Richard Korpus (American Bureau of Shipping, USA) 1. SUMMARY Dynamic overset grids provide a powerful tool for CFD calculations that model relative motion between body components. This paper presents a new overset method utilizing OpenFOAM to simulate the selfpropulsion and maneuvering of ships. The method uses one set of overset cells fixed to the Earth, a second set fixed to the hull, a third set rotating with the propellers, and a fourth set rotating with the rudders. Prescribed motions move the propeller and rudder blocks relative to the hull, and the entire hull assemblage moves relative to Earth using 6-DOF motions derived from CFD forces. A digital autopilot controls rudder rotation for course keeping. Overset boundary conditions and hole cutting are updated dynamically at run time for every time step. The method is demonstrated using two ship models for which extensive validation data exists: the Japan Bulk Carrier (JBC); and the Office of Naval Research Tumblehome ship (ONRT). JBC self-propulsion results include cases with and without an Energy Saving Device (ESD). ONRT results include free running maneuvers in regular head seas and quartering waves. 2. INTRODUCTION Predictions of ship self-propulsion, maneuvering and course keeping, whether in calm water or waves, are one of the most demanding challenges in ship hydrodynamics. Interactions between the hull, rudder, and propeller all have to be accurately resolved, and this proves impossible for potential flow codes and other linear methods. Computational Fluid Dynamic (CFD) is one of the most effective alternatives because it resolves the complex viscous turbulent flow around a stern using the Naiver-Stokes equations. However, relative motion between the various components (propellers, rudders, heaving hulls) remains a critical challenge because traditional dynamic grids distort (causing a loss of accuracy), and sliding mesh methods lack robustness (e.g. rudders moving close to rotating propellers). The dynamic overset grid technique has a long history of accurate, efficient and robust applications (Rogers, et al., 1994, Korpus, et al., 1997, 2005, 2007, Meakin, 1999, Suhs, et al., 2002). Individual geometry elements are usually moved using a nested hierarchy approach wherein any group of objects moves relative to its parent using prescribed motions, free dynamics, or some combination of the two. Propellers, for example, can rotate around one ship-fixed axis while rudders rotate about a different one. The whole collection of elements can either be made to translate at a known speed (such as during a ship resistance test), or under the influence of forces from waves, propellers, or towing forces. The type of movement can even change part way through a simulation – as might be required for a stores separation problem. The potential permutations are endless. More recent developments with dynamic overset grids include demonstrations of hull-propeller-rudder interactions and zig-zag maneuvering for the KCS CFD validation model (Mofidi and Carrica 2014), Carrica et al. (2015) extended that work to include the ability to perform maneuvering simulations in waves, and demonstrated a great step forward in computational ship hydrodynamics. Shen et al. (2014a, 2014b, 2015) implemented overset grid technique in the open source toolkit OpenFOAM by coupling with the SUGGAR library (Noack, 2005). The resulting dynamic overset grid technique was validated for self-propulsion, seakeeping and maneuvering using the KCS, KVLCC2 and DTMB 5415M CFD validation data sets. The present paper introduces a new implementation of dynamic overset gridding for OpenFOAM solvers. Domain Connectivity Information (DCI), hole cutting, and overlap minimization are provided using a standalone overset grid assembler. Dynamic linking of the grid assembler and OpenFOAM solvers allows computations of complex ship hydrodynamic problems including self-propulsion and free-running maneuvering simulations. III - 221 Corresponding CFD simulations include both the resistance and self-propelled configurations. Switching from Case 1.7e+6) for the conditions without propeller (Cases 1.5 and 1. ESD and Propeller III .4e+6) for the conditions with propeller (Cases 1. Each component grid is created using HEXPRESS from NUMECA. The grid is composed of separate components for the hull (including ESD). 3. 4.2). and therefore allows grid importation from a wide range of mesh generation tools. the model is free to sink and trim and the computational domain moves forward at ship speed. which is sufficient for the self-propulsion and maneuvering simulations presented herein. including general polyhedron. Additions are under development to improve stability and run time efficiency. (a) Surface Grid of ESD and Propeller JAPAN BULK CARRIER (JBC) 4. An overlap minimization procedure reduces duplicated grid regions to the smallest allowable for a given order of accuracy. The model has a length of 7. Reusing the propeller and background grids (as indicated in Table 3) saves immeasurable time.222 . The assembler is capable of processing OpenFOAM grids with arbitrary cell shape.179 meters per second (corresponding to Fr = 0.6). and from O(1. This first category covers self-propulsion simulations of the Japan Bulk Carrier (JBC) with and without an Energy Saving Device (ESD). The second category covers course-keeping simulation of the ONR Tumblehome ship (ONRT) in waves. twin rudders. Minor modifications to the standard interDyMFoam solver are necessary to enable inclusion of dynamic overset grids. The � � � SST model provides turbulence closure.1 and 1. The method uses a stand-alone DCI grid assembler to establish connectivity among independent moving overset grids. requires changing only the hull grid.Two categories of simulations are performed to demonstrate the method. InterDyMFoam discretizes the incompressible Reynolds-Averaged Navier-Stokes (RANS) equations using a finite volume technique. Figure 1 illustrates an assembled grid for the case with ESD and propeller.1 Test Conditions JBC is a new self-propulsion benchmark model for the Tokyo 2015 Workshop. 4. 1 Overset Grids of JBC. propeller and background – each shown in a different color.00 meters and test speed of 1.6. The software currently handles up to two-levels of grid block hierarchy.3. and therefore improves interpolation accuracy. Test conditions include self-propelled cases with and without a duct-like ESD mounted upstream of the propeller. Tables 2 and 3 demonstrate the results of grid sizes from a grid convergence study performed for JBC. Coding uses the new C++11 standard and dynamic library calls OpenFOAM to fetch and return grid information for fast DCI searches and boundary condition specifications. (b) Global View (c) Close Up of Stern Region Fig. The test series also includes resistance measurements made in calm water with the propeller removed. Changing between cases only requires swapping out one or two component grids. In all cases. and then assembled using stand-along DCI software. Table 1 summarizes the test conditions. COMPUTATIONAL METHODS All simulation results presented in this paper were created using OpenFOAM version 2. Results for three grid levels are shown to demonstrate how a refinement ratio of √2 affects accuracy.1. Total cell counts range from O(1. The ONRT model is equipped with twin propellers.142).2 Overset Grids The computational grids consist of several overset component cell groups. and captures the free surface using the Volume of Fluid (VOF) method to resolve interfaces between air and water. Simulating the four separate cases demonstrates one advantage of the overset technique. each performed with and without the ESD.0e+6) to O(5. and a digital autopilot for course keeping.5e+6) to O(7.5 to Case 1. for example. 88% 1.81 7.232 2. both medium and fine grids obtain III .452.673. Medium Grid) Table 4 Resistance of JBC without ESD (Case 1.611 1. The controller monitors the difference between ship speed and target speed and updates propeller rotation rate accordingly. For the self-propulsion tests.02835 1.446 7.1936 6. the maximum error is less than 2.76% 8.520 With ESD (Case 1. 2 Time History of Propeller RPS and Ship Speed for Self-Propelled JBC with ESD (Case 1.517 562.179 m/s) and propeller at zero revolutions per second (RPS).0873 -0.520 Total 1.039 4.085 -0.0279 Fine Medium 4. Table 5 shows resistance predictions with the ESD (Case 1.866 -0.507% -0.582% 4.271.573% Table 5 Resistance of JBC with ESD (Case 1.94% 7.027% 6.6.6). All quantities come from after the solution converges to a steady state solution.103 3.034 1. Table 2 Overset Grids for JBC without Propeller Grid Coarse Medium Fine Coarse Medium Fine Hull Background Without ESD (Case 1.344% 5.380 Fine 2. Once ship speed reaches its Fig.1195 1. Fine grid results are only marginally better. the thrust catches up and ship speed approaches its target.165 2.191 7.1 3.60% Coarse 5.287 4.752 Table 3 Overset Grids for JBC with Propeller Grids Hull Propeller Background Without ESD (Case 1.435. Note that ship speed first drops due to insufficient propeller rotation. the propeller RPS defines the self-propulsion point.208. a PI controller adjusts propeller speed during the simulations in order to achieve target speed and thrust/drag balance.754.842 7.818.371.332 4.663.088 -0. The initial condition is with the ship at target speed (1.146% 1.6197 -0.223 .500 1.5).477 3.351.2) 500.184 1.726.520 With ESD (Case 1.517 562.06% -1.667 1. CP and CV show monotonic convergence. CT.29 Fine Medium Coarse 4.064% 7.175 1.879.500 Medium 1.173% Table 6 Self-Propulsion of JBC without ESD (Case 1.637% 5.5) Coarse 608.380 Fine 2.1) CT (x103) Error CP (x103) CV (x103) Sinkage [%LPP] Error Trim [%LPP] Error EFD 4.182 -0.195 -0.99% 0.455 843.588% 4.26 Fine Medium Coarse 4.514 2.2125 0.024.223 843.5 Case 1.818.0895 1.07% 0.728 562.906 349.68% 1.482.0888 2. and shows how ship and propeller speed eventually reach a steady state. After a few seconds.02836 0.208.479 0.Table 1 Summary of JBC Test conditions Case ID Case 1. Although KT fails to converge.6) Coarse 773.923 3.17% 7.219.3 Results Table 4 shows resistance predictions for JBC without ESD (Case 1.1851 1.097.879.5) CT (x103) Error n (RPS) Error KT Error KQ Error EFD 4.2928 4.908.275 4. Figure 2 demonstrates the method for the self-propelled JBC with ESD (Case 1.358 1. As for the total resistance and propeller speed. and demonstrates that CT. but are notably small values at this low Froude number. propeller speed (n) and KQ all show monotonic convergence.0006 seconds (~ 200 time steps per propeller revolution). Errors of CT for medium and fine girds are less than 1%.1 Case 1.1426 3.18 -0.142.05% 0.685.24% Table 6 summarizes results of self-propulsion tests for the JBC without ESD (Case 1.1) 461.1077 3.500 Medium 1.8 0.208.684% 1.818.520.247 2.351% 0.043 1.194 -0.069% -0.1).818.380 2.611 1.2138 -2.217 0.65% 0. In order to capture blade-rate force fluctuations accurately.6 No Yes Yes Yes target.134 -0. They will have limited impact on resistance.2) CT (x103) Error CP (x103) CV (x103) Sinkage [%LPP] Error Trim [%LPP] Error EFD 4.086 -0. The medium grid obtains good resistance predictions with less than 1% relative error for CT.305.520 Total 1.962 0.165 2.591 1.0871 -0.1925 -0.0882 -0.214 7.768 4.346 3.2148 -0.747 562.047 5.02825 1.513% 3.383.063.647 1.380 2.194 5.2 ESD No Yes Propeller No No Case 1.4489 3. Predictions of sinkage and trim are diverged.547% 2.500 1.48% 0.2).1708 -0.1%.971 5.405% 6.54% 2.208.460 349.1915 -0. the time step for self-propulsion simulations is set to 0.2621 4.228 2. For Cases 3.80 RPS. indicating that it is relatively more difficult to achieve convergence for propeller forces and moments than for hull resistance.224 .9).911.13.07% (a) Side View ONR TUMBLEHOME (ONRT) 5. For free-running simulation in calm water (Case 3.626 424. Each overset grouping of cells can move relative to all the other groupings and is generated using HEXPRESS.178. we initialize the calculation by making self-propelled simulations to determine the propeller speed needed for thrust balance at a ship speed of 1.10% 0.02933 0. Table 10 shows the III .89% 0.9% less than the model-test value of 8.380 6. EFD 4.13 Quartering wave (135o) 6DoF Yes Active 5. the ship model has full 6-DOF motion and a constant propeller speed of 8. and shows a similar trend as Case 1.782 1. For ONRT the final predicted propeller speed is 8.2256 -2.786 4. Case 3.9 & 3.2266 0. Figure 3 shows a (c) Overset Component Grids at Stern Region Fig.13 1.673 7.951 1.02966 0.147 meters and speed of 1.56% Coarse 5.50% -0.178.2 Overset Grids The computational grid for ONRT consists of six overset cell groupings as listed in Table 9.11 m/s.762 7.6).02952 0. Rudders are activated by a PID controller for course keeping.12 and 3. Both total resistance and propeller speed are well predicted by the medium and fine grid.9 Case 3.18% 0. The simulation follows the same procedure as described in Section 4.11 meters per second (corresponding to Fr = 0.12 Condition Calm water Head wave Motion 6DoF Propellers Yes Rudders Active 6DoF Yes Active Case 3. ONRT is a new benchmark ship model for Tokyo 2015.50% 7. The components are prepared using the described overset grid assembler.1 Test Conditions (b) Hull Surface Grid Similar to the JBC.383.54% -0. ONRT has twin rudders and twin propellers.103 5.649 1.876.3 Results Before any free-running simulations start.632 2. Note that a different background grid in the shape of a cylindrical is used in Case 3.233 0.better results than the coarse grid.5.752 0. The comparable model tests tow a model at a fixed attitude for a prescribed period and then release it from the carriage into six degree-of-freedom motion controlled only by the rudder autopilot. Table 8 Free-Running Test Conditions for ONRT Case No.5 0.300 6.06294 meters. two rudders and two propellers.80 RPS.467. or 1.0295 Fine Medium 4.6) CT (x103) Error n (RPS) Error KT Error KQ Error 5.147 meters and wave height of 0. KT shows oscillatory convergence and KQ is diverged.12 1. Table 7 lists the self-propulsion results with ESD (Case 1.3. the moving components include the hull.96 6.20).024 5.75% 0. the model is sailing in regular waves of length 3.951 1.208.316 Case 3. 3 Overset Grids for ONRT Table 9 Summary of Overset Grids for ONRT Grid Name Hull Propeller x 2 Rudder x 2 Background Total Case 3.75% -3.327.13 for quartering wave simulations Table 7 Self-propulsion of JBC with ESD (Case 1. and close ups of the relevant details. In addition to the background grid.30% 1. global view of the complete grid.626 424. Table 8 lists the three free-running maneuvering cases simulated using the overset system described herein.2286 -1.97 RPS.22% 7. The model has a length of 3. 90% For Case 3.001 0.0386 8. 8 Time History of Ship Motion and Rudder (CFD: solid line. OpenFOAM’s third party library. and matches well given the limited data acquisition rate. 6 Four Snapshots of Vortical Structures near the Stern during One Encounter Period Fig.01). Predicted sinkage is 2.9 Speed ���� Sinkage σ×102(m) Trim ��(deg) n (RPS) EFD 1. 5 Four Snapshots of Free-Surface and Ship Motion for One Encounter Period Fig. creates waves in the far field region and applies a relaxation zone to transmit them into the near field. 4 Time History of Ship Motions (CFD: solid line. Waves breaking at the bow and violent motions at the stern are both apparent in Figure 5. Figure 6 demonstrates the complex interactions between propeller vortices. the ONRT model sails in oblique quartering regular waves.89% larger than the experimental result and trim angle has a slightly worse error at 6.225 . 7 Wave Generating Zone for Quartering Waves For Case 3.6%.89% 6. The predicted heave and pitch motions match well with measurements. Experiment: circle) III .13. Table 10 Free-Running Test of ONRT in Calm Water Case 3. Fig.2327 -0.01 0. The speed loss is due to added resistance induced by the incident waves. 2012). the model is sailing in head waves and Figure 4 shows the time history of ship motions.63% -1. The solid lines represent CFD predictions and the circles experimental measurements. the absolute differences in value are small.27% less than the experimental value of 1.0411 8. Fig.8 Error -1. A relaxation method blends the far field analytic solution with the near field computed one. waves2Foam (Jacobsen et al.001 (1. As with JBC though. and indicates a non-dimensional final speed of 1.27 % 2. Figures 5 and 6 show four instantaneous snapshots of free-surface motion and vortices near the stern (evenly spaced over one encounter period). ship motions and incoming waves. Figure 7 demonstrates the wave generation region and computational domain.226 -0..12.97 CFD 1. Experiment: circle) Fig.results. rudders. (2015). San Francisco.G. Rome. Wan. R. Proceedings of SIMMAN 2014. “Accuracy Enhancements for Overset Grids Using a Defect Correction Approach”. Hubbard. (2007). Mofidi. P.M. Korpus.... R. Netherlands. (1998). and Rogers. “PEGASUS 5: An Automatic Pre-Processor for Overset-Grid CFD”... (1994). Since the rudder angle is dependent on the yaw deviation angle. B.. Jones. Greece. The heave and pitch motions are in good agreement with measurements. Fuhrman.. Fig.. and Pulliam. and Bennett. Stuart E.. Volume 108. “SUGGAR: a general capability for moving body overset grid assembly”. The ship is undergoing large-amplitude roll motions. D.226 . Fredsøe. R. (1999). Korpus.. but the deviation angle returns to an average of zero (with oscillations) due to the countering effects of waves and autopilot. S. A. “Ship motions of KCS in head waves with rotating propeller using overset grid method”.. Wan. E. D. T. No 9. Stromgren. “Dynamic Overset Grids in OpenFOAM with Application to KCS Self-Propulsion and Maneuvering”. D. Free-running course keeping simulations with the ONRT model in both calm water and regular waves reveal a good match with model measurements. International Journal for Numerical Methods in Fluids. Italy. P.. the curve presents a similar trend as that of the yaw angle. “Progress toward direct CFD simulation of manoeuvres in waves”.M. Suhs. Proceedings of the 18th CSYS. USA. “Hydrodynamic Design of Integrated Propulsor/Stern Concepts by Reynolds-Averaged Navier-Stokes Technique”. pp 1073–1088. Figure 10 shows a close-up view of the vortical structures in the stern region. C. Chapter 11.. Wan. (2012). “Performance Prediction without Empiricism: A RANS-Based VPP and Design Optimization Capability”.M. W. (2015). Mofidi. R. Z. AIAA Paper 2005-5117. “Simulations of zigzag maneuvers for a container ship with moving rudder and propeller”. H. Shen. Proceedings of OMAE 2005. Ocean Engineering. CONCLUSIONS This paper presents numerical simulations of two new benchmark ships using an overset grid technique developed for OpenFOAM. Computers & Fluids. Z. The Hague. J. and Liapis. The vortices are represented using an ISO-surface of Q and colored by the axial velocity. Carrica.. (2002). Proceedings of MARINE 2015. which may be due to an inaccurate vertical center of gravity.. III . 9 Free-Surface and Ship Motion at the Moment of Maximum Roll Angle Jacobsen. 6.. Fig. Noack.. Korpus. Proceedings of OMAE 2014. D. AIAA Paper 94-0523.. Denmark. P. Lyngby. Halkidiki.. Shen. S... pp 191–203. Figure 8 shows time histories of ship motion and rudder angle resulting from the simulation. Self-propulsion simulations of the JBC model indicate total resistance and propulsion points are in good agreement with experimental results.R. 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