Eedi reduction by investigating the capability of ranse cfd for propeller, propeller– hull form performance calculation during ship optimization process

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EEDI reduction by investigating the capability of RANSE CFD for
propeller, propeller– hull form performance calculation
during ship optimization process
Dr. Tran Ngoc Tu (1), Msc. Nguyen Manh Chien (2)
1. Vietnam Maritime university, tutn.dt@vimaru.edu.vn
2. Vietnam Maritime university, chiennm.@vimaru.edu.vn
Abstract. In recent years, the concerning about environment protection has grown significantly,
especially about global warming and reduction of CO2 emission. Besides, there are considerable
development in marine transportation and activities: from offshore installation supply to the exploitation
of marine resources. It leads to the high increasing of fuel consumption for ship operation on the ocean.
Moreover, in 2010, International Maritime Organization (IMO) introduced Energy Efficiency Design
Index (EEDI) as a technical measure to limit pollution of the environment resulted by marine engines
[1]. EEDI is expressed by CO2 gram per ship’s capacity. So smaller EEDI means smaller CO2
exhausting to the environment.
With that reason, many efforts have been made to optimize ship’s fuel consumption, to save the
operation cost, on the one hand and to reduce the CO2 emission, or reduce EEDI on the other hand.
From the EEDI equations [2], according to Bazari & Longva, 2011 and IMO MEPC 63 (2011) [2], there
are 15 methods of EEDI reduction. Within these 15 methods, hull form and propulsion optimization are
common approaches for many designers and researchers.
To optimize the hull form and propeller, the designers need to carry many designs then select the best
one based on their performance. Estimation of hull form and propeller performance usingmodel tests
widely accepted as most reliable means, and could be considered as the closest method to reality.
However, due to time and cost for making testing models, it is not suitable for optimization process; it
is just only used to validate the result of optimization. Besides, with the rapid improvement of
computational resources, Computational Fluid Dynamic (CFD) is getting to become a useful tool in ship
design and power prediction. CFD method is able to look into local flow properties and providing a
room for designers to improve the design.
In this paper, the authors will investigate the capability of CFD method for propeller and propeller –
hull form performance calculation, during ship optimization process. The approach of CFD here is
Reynolds-averaged Navier–Stokes equations (RANSE). During the optimization process, many designs
have to be analyzed, so the level of accuracy and computational time of the calculation have to be taken
into account.
The paper has two major parts. For the propeller calculation in open water, the authors will perform 3
methods to model the rotation of propeller and select the best one in terms of accuracy and time
consumption. Later, the self-propulsion simulation is carried out. That is a setup with full rotating
propeller behind a ship. The ISIS - CFD code, integrated in the commercial software Numeca Fine
Marine is used. The simulation results will be compared with model test results.
Keywords: propeller, hull, CFD, optimization, RANSE, EEDI reduction, ISIS code.
1. Introduction
Propeller calculation using CFD method is not a new topic for researchers. Many authors have predicted
the performance of the propeller, both in open water and behind condition. Giulio Dubbioso et al [3] has
performed the open water simulation with INSEAN E779A propeller with fine mesh (1.31 million cells)
and in-house solver χ navis - a finite volume uRaNSe (unsteady Reynolds-averaged Navier–Stokes
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equations) solver. To investigate “the effect of turbulence models on RANSE computation of propeller
vortex flow”, Hongxuan (Heather)Peng,WeiQiu n, ShaoyuNi [4]did the simulation on David Taylor
Model Basin (DTMB) 5168 propeller. Three mesh sizes (1.92, 2.4 and 2.74 million cells) and 10
turbulence models (k-ε, k-ω, SST, Omega RSM ) has used during the simulation. In terms of Propeller
and hull interaction simulation, G. Dhinesh [5] used RANSE solver Star CCM+ with k-ε turbulence
model and sliding interfaces between propeller domain and ship domain. All the authors have presented
good simulation result in comparison with experiment result. However, almost the simulations has just
concentrated on the accuracy of the simulation, the computational time as well as the practical use of
the method has not been studied., although it plays an important role during ship optimization process
because many designs have to be considered in short period of time. Thus, this paper also presents the
balance between computational time and level of accuracy of propeller calculation. Some methods for
open water simulation are studied to choose the best one. The solver using in this paper is commercial
RANSE code ISIS Solver, integrated in Numeca Fine Marine software. The turbulence model which
mainly uses is k-ω SST. All the simulations are performed on cluster over 16 up to 96 cores.
The first part of this paper deals with open water simulation over 3 different methods: Sliding Grid,
Rotating Reference Frame, and the last one is whole calculation domain rotating with propeller (called
Rotating Domain in this paper). After selecting the best method to do open water simulation, the authors
are going to do the second part: simulation of propeller working behind the hull. At the end, the authors
give the assessments and evaluations about the computational resources, level of accuracy and the
practical use of simulation
2. Literature Review
2.1 ISIS Flow Solver
The ISIS flow solver is a solver based on incompressible unsteady Reynolds – averaged Navier-Stokes
equation (RANSE) and developed by Laboratoire de Mécanique des Fluides, Ecole Centrale de Nantes,
France. Finite volume method is used in the solver for discretization of fluid domain. The velocity field
and pressure field are obta ... ching connection face”. The grid of common face between two domains is not required point-to-
point matching each other. This connection enables the solver to compute flux through two domains.
For each time step, the inside domain rotates and changes its position, therefore the solver has to re-
calculate this connection each time step.
Figure 3 Calculation domain for Sliding Grid method
The mesh size after generation and inserting viscous layer is 3.9 million cells, similar to 2 other methods.
3.3 Computational Setup
The open water simulation is carried out with different advance coefficient J. We keep constant
revolution n = 15 rps for the propeller, J is changed by varying advance velocity Va. Particularly, 5
advance coefficients J is simulated:
Advance velocity Va (m/s) 2.25 3.00 3.75 4.50 5.25
Advance coefficient J 0.6 0.8 1.0 1.2 1.4
Turbulent models: k-ω SST. The same boundary condition is applied for all three methods as follows:
• Inlet and External boundary: Far field with advance velocity (Va) imposed;
• Outlet boundary: Prescribed pressure (frozen pressure);
• Solid parts: Wall function approach. When selecting this option, ISIS solver
automatically calculates the y+ to apply appropriate model: wall function or low Reynold number
approach. (low y+).
Calculation Domain: a Cylinder
Diameter = 2.5m
Length = 4.3m
Outlet
Propeller Domain: a Cylinder
Diameter = 0.28 m
Length = 0.52 m
Va
Inlet
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The major differences in setup of 3 methods are the time step and the number of iteration per time step.
This setup directly influences to time consumption or computer resources during simulation. The
Rotating reference frame method can be considered as a steady approach for open – water test, therefore
large time step and small numbers of iteration is used. Detail setup of time step is as follows:
Table 1 Time step setup for open water simulation
Method Number of Iterationper time step Time step
Rotating Domain 8 0.0003333s (200 time steps per round)
Rotating Reference Frame 4 0.00667s (10 time steps per round)
Sliding Grid 8 0.00013333s (500 time steps per round)
Computation of the simulation is performed parallel on cluster with 16 cores.
3.4 Result and discussion
The result is achieved by measuring the force in X direction (thrust) and the moment through X axis
(torque) on propeller blades and hub when convergence is reached. The thrust and torque are expressed
in non-dimensional forms by KT and KQ. After that, the open water efficiency ηO is also calculated.
Figure 4 Open water curves obtained from 3 different methods, comparing with experiment
result (EFD)
General view, compared to experiment data, the simulation results of three methods are good at J from
0.6 to 1.0 particularly, from 3% to 6% difference for all KT, KQ, and ηO . The result of KQ is also good
for all J, less than 5%. The difference just gets higher for KT, with J from 1.2 to 1.4, up to 7% and 13%,
respectively. The reason for that could be because the magnitude of KT is getting very small with
increasing J.
There is not much difference in terms of numerical result among 3 methods. The Rotating Reference
Frame method shows very good estimation of KQ, giving the best result compared to two other methods.
For KT, the Sliding Grid is the closest to experiment. The details of computational result are described
in the Table 2 below:
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Table 2 Open water simulation result of different methods
Experiment Sliding Grid Rotating Domain Rotating Reference Frame
J 10KQ 10KQ ΔKQ 10KQ ΔKQ 10KQ ΔKQ
0.6 1.396 1.451 3.94% 1.466 4.98% 1.432 2.53%
0.8 1.178 1.224 3.88% 1.242 5.41% 1.208 2.52%
1.0 0.975 1.002 2.78% 1.019 4.57% 0.988 1.35%
1.2 0.776 0.791 1.92% 0.803 3.50% 0.779 0.33%
1.4 0.559 0.559 0.10% 0.546 -2.36% 0.546 -2.38%
Experiment Sliding Grid Rotating Domain Rotating Reference Frame
J KT KT ΔKT KT ΔKT KT ΔKT
0.6 0.629 0.630 0.13% 0.630 0.12% 0.623 -0.99%
0.8 0.510 0.506 -0.74% 0.508 -0.33% 0.501 -1.74%
1.0 0.399 0.388 -2.97% 0.390 -2.35% 0.383 -4.08%
1.2 0.295 0.277 -6.06% 0.278 -5.72% 0.273 -7.39%
1.4 0.188 0.166 -11.37% 0.162 -13.69% 0.162 -13.71%
Experiment Sliding Grid Rotating Domain Rotating Reference Frame
J ηO ηO ΔηO ηO ΔηO ηO ΔηO
0.6 0.430 0.414 -3.66% 0.410 -4.63% 0.415 -3.43%
0.8 0.551 0.527 -4.44% 0.521 -5.44% 0.528 -4.15%
1.0 0.652 0.616 -5.59% 0.609 -6.62% 0.617 -5.34%
1.2 0.726 0.669 -7.82% 0.661 -8.91% 0.670 -7.69%
1.4 0.749 0.663 -11.44% 0.662 -11.59% 0.662 -11.59%
In terms of computational time, the simulation for all 3 methods is performed in parallel with 16 cores.
The mesh sizes are 3.9 million cells.
The average computational time is follows:
Table 3 Computational time of 3 different methods
Method Sliding Grid Rotating ReferenceFrame
Rotating
Domain
Computational time (average) 58.3 h 15 h 40h
Percentage (compared to Sliding
Grid method) 100% 25.% 68.6%
It is clear that Rotating Reference Frame takes least computational time, by less than one-third compared
to two other methods. Therefore, Rotating Reference Frame method has big advantage in practical and
daily use.
3.5 Assessment and conclusion of result for open water simulation
Rotating reference frame method proves that it is suitable method for open water simulation, concerning
computational time and level of accuracy, as well as convergence of result. However, this method is
only suitable for simulation with 1 domain, it cannot be used for simulation of propeller behind the ship.
In this case, Sliding Grid approach should be used. The investigation of setup for sliding grid approach
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in this section is very useful for doing simulation of propeller behind the ship in the next part of this
paper.
4. Propeller behind ship simulation
To have consistency with experiment, the simulation is carried at model scale for ship and propeller.
The ship is bulk carrier, with a 4-blade propeller [8], from a Chinese shipyard. The experiment result is
provided by China ship scientific research center (CSSRC) [8]. The output is wake fraction (wT), thrust
deduction factor (t), relative rotative efficiency (ηR), and hull efficiency (ηH). Besides, the factors that
represents performance of propellers also need to be taken into account: thrust coefficient (KT), torque
coefficient (KQ) (note that these two coefficients are calculated in the case of propeller behind the hull,
different from open water case).
In order to get all the output, it is necessary to use open water curve from open water test simulation.
Hence, the simulation steps and result of open water for this propeller will be shortly presented.
4.1 Ship and propeller geometry
Basic dimension of ship and propeller are described below:
Table 4 Basic dimension of ship and propeller
Ship (bulk carrier) Propeller
Length overall 7.5 m Diameter 0.2333 m
Length between Perpendicular 7.233 m Chord length at 0.75R 0.0502 m
Breadth moulded 1.0753 m Expanded blade ratio 0.3766
Design draft 0.4067 m Number of blades 4
Displacement 2.708 m3 Direction of turning Right handed
Block coefficient CB 0.855
4.2 Open water test result
The mesh generation and calculation setup for open water case has been described completely in the
previous chapter. Therefore, only brief information about this simulation is presented. The method using
is Rotating Reference Frame method, mesh size 2.1 million cells, turbulence model: k-ω SST. The open
water curve is presented in Figure 5 below.
Figure 5 Open water Curve - Self propulsion test
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4.3 Mesh setup for simulation of propeller behind the ship
The number of cells for Propeller domain and Ship domain are 2.9 and 2.1 million cells, respectively.
The total cells are 5 million cells. It can be considered as reason number for mesh size, because time
consumption for Sliding Grid is very high.
The simulation is performed at services speed 14.5 knots, corresponding with Froude number equals to
0.159.
Figure 6 Propeller domain and sliding interface
Figure 7 Mesh generation for propeller and ship
4.4 Setup of simulation
The basic setup of simulation is as follows: multi fluid approach (air and fresh water) and free surface.
Turbulence model is k-ω SST. For the boundary conditions, wall-function approach is used for solid
parts (hull, propeller, shaft, hub and cap), while the external boundary is set to Far field condition except
Prescribed pressure for Top boundary. The propeller is connected to the ship by “Pin” connection. At
first, large time step is applied to simulation: ∆t = 0.026 second (equal to 5 times step per propeller
revolution). The number of iteration per time step is 4. After the force acting on the ship becomes quite
steady (around 1000 time-step), we switch to second simulation using previous result, but much smaller
time step, ∆t = 0.000525 (250 time steps per propeller revolution), and 8 iterations per time step, to
stabilize propeller thrust. Time steps setup for propulsion test is described in the table 5 below:
Table 5 Time steps setup for propulsion test
Computational
case
Propeller
revolution (rps)
Time step (s)
1st computation 2nd computation
Vs = 14.5knots
7.623 0.026 0.000525
8.2 0.024 0.000488
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4.5 Result and discussion
The propulsion factors acquired from CFD simulation is shown below, in comparison with experiment
result:
Table 6 Simulation result of propeller behind the ship
Self - propulsion parameters CFD result Experiment
result [8]Compare to experiment
Thrust coefficient KT 0.154 -7.49% 0.166
Torque coefficient KQ 0.213 -3.14% 0.22
Revolution n 7.812 2.48% 7.623
Thrust deduction coefficient (1 - t) 0.808 3.06% 0.784
Advance coefficient J 0.5408 -0.04% 0.541
Open water coefficient 0.633 -0.92% 0.639
Relative rotative efficient ηR 0.980 -3.48% 1.015
Effective wake coefficient (1 - w ) 0.724 2.37% 0.707
Hull efficient ηH 1.116 -1.02% 1.128
Regarding level of accuracy, the result of simulation is quite promising. The difference is around, and
less than 5% for the propulsion parameters in behind-condition (thrust deduction, wake fraction, relative
rotative efficiency).
In terms of time consumption, the average computational time by using 16 cores on 1 node, the mesh
size is 5 million cells is below
First computation (large time step) Time: 1200 min = 20 hours
Second computation (small time step) Time: 10000 min = 167 hours = 7 days
There is extremely time consumption for second computation; it takes 160s to calculate 1 time step.
However, there is a solution for that. The computational time will reduce much if we run parallel on 96
cores over 3 nodes. It only takes around 70 second to calculate one time step. It means the speed
increases by 2.5 times. And if the calculation is performed in 128 CPU over 4 nodes, the speed can
increase by roughly 4 times, around 50 seconds per time step. Therefore, one simulation (including two
steps of computation) can be done within 1.5 days (36 hours).
5. Conclusion and further development
The paper presents the CFD approach using RANSE solver for propeller calculation, in both case: open
water and behind condition case, with concentration on the practical use of the method during
optimization process. Few methods have been tested for open water simulation. The investigation also
points out that Rotating Reference Frame method is the most suitable one for doing open water
simulation, considering level of accuracy and computational resources. Rotating Reference Frame
method could be applied in practical or daily use, to simulate propeller in open water condition.
The self-propulsion simulation (or simulation of propeller working behind ship) shows quite promising
result. The results of the parameters, characterizing “propeller behind ship performance”, such as thrust
deduction (t), effective wake coefficient (1 - w) or relative rotative efficiency (ηR) are good in
comparison with experiment: less than 5% difference.
However, the most difficulty of this simulation is computational time. Using Sliding Grid with large
number of cells (including full ship, propeller and rudder) is very time and resources consumption.
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Regarding the use of these approaches for hull form and propulsion optimization, due to quite large
computational time, currently, those methods should use to validate propulsion performance of
optimized hull form. For example, the hull form can be optimized by doing simulation with Potential
Flow theory (non-viscous fluid) for many designs. After that, few good performance hull forms are
selected to do the second simulation with viscous flow by applying those methods which are investigated
in this paper. However, with the significant development of computational resources, the authors believe
that in the short future, we are able to perform simulation of many design with RANSE solver and
methods described in this paper, to obtain optimized hull form and propeller.
Reference
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emissions from ships, MEPC 60/INF.18, 15 January 2010
[2] ZabiBazari, Tore Longva, Assessment of IMO mandated energy efficiency measures for
international shipping, MEPC 63/INF.2, 31 October 2011
[3] Giulio Dubbioso ,RobertoMuscari, Andrea Di Mascio, Analysis of the performances of a marine
propeller operating in oblique flow, 2012
[4] Hongxuan (Heather)Peng,WeiQiu n, ShaoyuNi , Effect of turbulence models on RANS
computation of propeller vortex flow, 2012
[5] G. Dhinesh∗, K. Murali and V. AnanthaSubramanian, Estimation of hull-propeller interaction of a
self-propelling model hull using a RANSE solver, 2010
[6] J.H.Ferziger, M.Peric, Computational Method for Fluid Dynamics 3rd version, 2002.
[7] Potsdam propeller test case, 
[8] Ji Shaopeng, China Ship Scientific Research Center, Powering Performance Model Test Report
for a 76000DWT Bulk Carrier with Design Propeller, 2013.