1. STAR Global Conference 2017
Parametric modelling and optimization of a marine propulsive system using STAR-CCM+,OPTIMATE+ and CAESES
Rolla SP Propellers SA
Claudio Ghirlanda

2. Rolla SP Propellers SA Twin Disc Group
Leadership in marine technology.
Since 1963 in Switzerland
Submerged and surface piercing marine propellers
Custom propellers for every application

3. CFD at Rolla More than 20 years CFD experience Propellers CFD
Rudders and other hull appendages
Resistance prediction of planing and displacement hulls
Always with CD-Adapco (Comet / STAR-CCM+)

4. Using CD-Adapco Optimate+ on marine field
Complex flow defined by propeller diameter, pitch, skew, camber, rake, thickness
Limitations (cavitation, hull obstructions and dimensions, etc)
Change propeller shape to optimize the efficiency
Propeller parametric model

5. Propeller parametric model
Friendship Systems CAESES software to generate a parametric CAD model

6. Software workflow
Friendship Systems CAESES software - generate a parametric model
STAR-CCM+ - Run simulation
Optimate+ - Optimise the solution

7. Propeller characteristics curves
Adimensional parameters to understand the behaviour and goodness of a propeller
J=Va/n*D
KT= T/(D^4*n^2*ρ)
10KQ= Q/(D^5*n^2*ρ)
η=(KT*J)/(10KQ*2π)

8. Propeller case characteristics
Fully submerged propeller
5 blades
Submerged propeller
Z [-] 5
D [mm]
1450
Nominal rotational speed [RPM]
693
Nominal water speed [Kn]
32.0
Nominal J [-]
0.9891

9. Parametric model variables
Using Friendship Systems CAESES - define variables
Optimate+ - manage to submit values
9 variables
Changes in chord, tip and camber
Submerged propeller
Min Value
Baseline
Max Value
Chord tip
0.4
0.45
0.65
Pitch tip
0.8
1.15
1.27
Chord mid
1.066
1.4
Pitch mid1 1
1.3
Pitch mid2
Chord hub
0.6
0.689
0.7
Pitch hub
0.9
Camber hub
0.0
0.01
Camber tip
0.037
0.006

10. Simulation physics and models
RANS
K-ε turbulence model
One fifth of the geometry with periodic interfaces
Newtonian constant density fluid (seawater)
Rotating reference frame approach
0 degrees inclination
No cavitation will be simulated
Not considering the wake effect of a hull upstream
Hub extended until the end of the computational domain

11. Calculation setup 20 minutes per case 40 runs 256 cores linux cluster
9 milions cells
Result = Mean value of last 500 iterations

12. Optimate+ results
Optimization of the geometry with efficiency as design target
Constrain on KT knowing the application for the propeller
Best efficiency after 30 deigns

13. Best case description Design 30 30 0.6803 1.0606 0.4417 1.1678 0.9800
Design Id
eta_0_mean
performance
chordtip
pitchtip
chordmid
pitchmid1
pitchmid2
chordhub
pitchhub 30
0.6803
1.0606
0.4417
1.1678
0.9800
1.0400
1.3000
0.6400
1.1200 28
0.6800
1.0602
0.5458
1.2700
0.9400
1.0600
0.6000
1.2400 35
0.6786
1.0580
0.4000
1.0861
1.0200 15
1.0579
1.0657
1.0000 34
0.6785
1.0578
1.1270 41
0.6774
1.0562
0.5042
0.6200 29
0.6761
1.0542
1.0452 40
0.6753
1.0529
0.6600 39
0.6749
1.0522
1.0248
0.8200 26
0.6743
1.0512

14. Results – Targets, comparison and limits of the model
Baseline case validated by open water tests
Efficiency
Lower limit on thrust to ensure hull propulsion
No parameters to control important propeller geometrical characteristics – eg: expanded area that is related to cavitation phenomena

15. Conclusions 6.1 % efficiency gain Torque 3.5% lower Thrust 1.5% higher
Changes bigger than 10% in values of chord at hub, chord and Pitch at mid blade section

16. Conclusions – Advantages
In the marine field
Fuel consumption
Less open water tests
In general
Powerful combination of tools to improve the product
New type of geometry

17. Future development Adding hull appendages
Adding cavitation model to the simulations
More variables to describe the propeller profile
Different advance coefficient J
Cavitation tunnel tests

18. Thank you