Optimisation of a Wing-Sail shape for a small...
Transcript of Optimisation of a Wing-Sail shape for a small...
Optimization of a Wing-Sail shape for a small boat
G. Lombardi, F. Cartoni, M. Maganzi
Dept. of Civil and Industrial Engineering of Pisa
Aeronautical Section
STAR Global Conference 2014
Vienna, March 17-19
34th America’s Cup… A new concept of wing sail
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The capabilities of the new configuration were deeply
analysed
Scientific Research and Sport
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The Sport at high level is a powerful engine for the Research, in particular for Aerodynamics
Typical fields:
• Race cars (F1 on the top)
• Motorbike
• Offshore
• Cycling
• Skiing
• Bob
• Kayak
• .......
• AMERICA’S CUP It is an important way to connect the basic
scientific research to the innovation in the
industrial world
Typical cycle:
scientific researches
implementation on sport
development and testing
application to commercial production
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
The Problem
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Realization of a Wing-Sail to be used on the boat that will take part to the next
edition of the inter-university race «1001 vele»
Huge range of Geometrical Parameters which can be modified in order to obtain an
improvement in the performances
Design of an Optimization Procedure The complexity of the flow that acts on
the Wing requires the use of a sophisticated
CFD solver to perform the Aerodynamic
evaluations inside the optimisation loop
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
The boat
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• Max Overall Length = 4,60 m
• Max Overall Width = 2,10 m
• 1 Centerboard
• 1 Rudder
• Mast Height = Free
• Sail Plan Max Surface = 33 m²
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Work Strategy
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Multi-Step Wing-Sail Optimisation Procedure
• 2-D Airfoil Shape Optimisation at different spanwise sections
The results obtained are used to fix the Airfoils Shapes at the analysed
sections, in order to realize the parametric geometry of the 3-D Wing-Sail
• 3-D Wing-Sail Optimisation
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2-D Airfoil Shape Optimisation
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Optimisation Procedure of the shape and the configuration of the Airfoils
at any spanwise section such that, at a fixed value of the Cl coefficient, it
is minimized the Cd coefficient, respecting a series of geometrical,
structural, technological and regulatory constraints.
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
2-D Airfoil Shape Optimisation
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modeFrontier Optimisation Software, developed by Esteco
• Intuitive management of the Logical Flow
• Set of Optimisation and DOE Generation Algorithms
• Statistical Analysis Tools
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2-D Airfoil Shape Optimisation
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Optimisation Procedure – Flow Diagram
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Grid Sensitivity Analysis
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• STAR-CCM+ – Grid Sensitivity Analysis
The Number of Cells inside the Calculation Domain is modified by controlling the Surface
Resolution on the walls of the Airfoils
Test Configuration :
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Grid Sensitivity Analysis
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0.260.270.280.290.3
0.310.32
0 20000 40000 60000 80000 100000 120000n.° celle (2D)
Cl
0.007
0.0075
0.008
0.0085
0.009
0 20000 40000 60000 80000 100000 120000n.° celle (2D)
Cd
Compromise choice between solution stability and computational costs
Number of Cells (2-D) Number of Cells (2-D)
Number of Cells (2-D) ~ 50k
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Grid Sensitivity Analysis
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Geometry
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Optimisation Parameters
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Rear Airfoil
• t_c_2
• x_tc_2
• 3 Shape Parameters
Global Parameters
• Chord
• GAP
• R
• Theta_2
14 Optimisation Parameters
Front Airfoil
• t_c_1
• x_tc_1
• 3 Shape Parameters
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
2-D Airfoil Shape Optimisation
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The number of Parameters with respect to which we are interested to conduce the
Optimisation needs a number of Initial Designs and Generations that is too large to
ensure the proper development of the process.
Optimisation-by-Steps
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
2-D Airfoil Shape Optimisation
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• Design of Experiments creation : SOBOL
• Genetic Algorithm : MOGA II
• Population : 250 designs
• Generations : 20
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Geometry
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Airfoils generation Script
Use of Bézier Curves to realize the shapes
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Geometry
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Constraints check
• Geometrical
• Structural
• Regulatory
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Geometry
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1/40 chord
25 chords
25 chords
10 chords 14 chords
Realization of the Calculation Domain
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CFD Analysis
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• Grid Generation
• 2-D Conversion of the Calculation Domain
• Imposition of the Physics Models
• Initial Conditions and Boundary Conditions
• Calculation of Cd at a fixed value of Cl
Authomatisation with Macro and Run on remote HPC
• 8 CPUs for each simulation (~ 15 minutes)
• 12 Concurrent Evaluations
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
2-D Airfoil Optimisation
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The genetic algorithm analizes the results and, when a
population is completed, the successive is realized focusing
the research in the range of each Parameter where
statistically there is the highest probability to find the
minimum of the objective function; this happens until the
end of the procedure.
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Root Airfoil Optimisation
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• Step 1 : Preliminary Optimisation of the Front Airfoil
• Step 2 : Preliminary Optimisation of the Rear Airfoil
• Step 3 : Full Optimisation
• Chord = 2400 mm
• Cl = 0.3
• Vapparent = 10 knots
• Standard Air, M.S.L.
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Root Airfoil Optimisation
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Step 1 – Preliminary Optimisation of the Front Airfoil
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Root Airfoil Optimisation
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Step 1 – Relative Optimum Configuration
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Root Airfoil Optimisation
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Step 2 – Preliminary Optimisation of the Rear Airfoil
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Root Airfoil Optimisation
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Step 3 – Complete Optimisation
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Analysis of the Results
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Global Parameters
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Analysis of the Results
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Front Airfoil
Rear Airfoil
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Analysis of the Results
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Design 2380
300
-300 300 600 900 1200 1500 1800 2100
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Analysis of the Results – Validation with finer grid
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Analysis of the Results – Validation with finer grid
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Analysis of the Results – Validation with finer grid
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Theta_2
Theta_2
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Analysis of the Results – Validation with finer grid
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Theta_2 Theta_2
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Analysis of the Results – Validation with finer grid
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Optimum Theta - Cl O
ptim
um T
heta
[deg
]
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Analysis of the Results – Validation with finer grid
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GAP = 0.015
Theta_2
Comparison between IDs 2380 - 255
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Analysis of the Results – Validation with finer grid
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Cl = 0.3
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Analysis of the Results – Validation with finer grid
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Cl = 0.3
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Analysis of the Results – Validation with finer grid
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Cl = 0.3
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
3-D Wing-Sail Optimisation
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• The 2-D Airfoil Shape Optimisation can be executed at any spanwise
position
• The objective functions and the constraints imposed can be modified
referring to the position considered
The shape of the Airfoils at different spanwise positions is fixed, depending on
the results of the 2-D Optimisation, in order to realize the 3-D wing
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3-D Wing-Sail Optimisation
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• Parametric representation of the Geometry
New set of Optimisation Parameters
New Objective Functions and constraints
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Geometry
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7 Optimisation Parameters
• Wing Height
• Root Chord
• Middle Chord
• Tip Chord
• α
• δ
• ε
Applied to the whole Main Wing with respect to the Wind
Applied to the whole Flap, hinged at R on the Main Wing
Twist Angle, opposite to δ and proportional to the Height
R_root
R_tip
R_middle
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Geometry
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7 Optimisation Parameters
δ
δ - ε/2
δ - ε
R_root
R_middle
R_tip
• Wing Height
• Root Chord
• Middle Chord
• Tip Chord
• α
• δ
• ε
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Physics Models
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Profile of the Apparent Wind
Vwa_Root
-Vb Vtw_Root
45 deg
Domain X Axis
30 deg
• The boat moves on a 30 degrees direction with respect to the Global X Axis
• The Wind blows at 45 degrees with respect to the boat moving direction
The True Wind Profile and the Boat Velocity are such that the Apparent Wind at the Root section
makes a 0 degrees angle with the Main Wing Chord when α = 0
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Physics Models
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• Vb = 2 m/s
• Vtw@10m = 5,36 m/s (Von Karman Profile)
Vwa components (Global Reference Frame)
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Grid
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The Mesh Settings are chosen with the same criterions described for the 2-D Optimisation
~ 1,7 MLN cells
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3-D Wing-Sail Optimisation
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Optimisation Parameters
Objective Functions and Contraints
• Maximum Thrust (Vb Direction)
• Roll Moment on the Mast Root not greater than 1000 N⋅m • Plan Surface at rest not greater than 12 m 2
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
The HPC
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• 1024 core cluster (~10 Tflops), AMD
• 1 GB Ram/Core
• Infiniband DDR fast network (20 Gb/sec)
Authomatisation with Macro and Run on remote HPC
• 128 CPUs for each simulation (~ 30 minutes)
• 4 Concurrent Evaluations
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3-D Wing-Sail Optimisation
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• Design of Experiments: SOBOL
• Genetic Algorithm: MOGA II
50 Designs
20 Generations
444 Designs Analized
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Analysis of the Results
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Analysis of the Results – Validation with finer grid
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Design 382
In the same way, an entire range of evaluations on the Wing-Sail behaviour
can be done at any value of the flap and twist angle, for different Moving
directions and True Wind Profiles; the Optimised Shape should in fact
guarantee its performances in the broadest possible range of race conditions.
0
20
40
60
80
100
120
140
160
0 1 2 3 4 5 6 7 8 9 10
Alpha
Thrust [N]
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
Analysis of the Results – Validation with finer grid
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6,5 MLN cells, Run on 256 CPUs
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Analysis of the Results – Validation with finer grid
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Analysis of the Results – Validation with finer grid
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Analysis of the Results – Validation with finer grid
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Conclusions and Future Developments
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The Optimisation Procedure described in this work is a very powerful
instrument to investigate on the influence of a wide range of geometrical
parameters on the Wing-Sail behaviour.
The Analysis can be conduced, thanks to the versatility of STAR-CCM+
and its strong possibility of customization of Macros, for many operative
conditions and for different Objective Functions and Constraints, in order
to increase the Wing-Sail performances in all the situations that can occur
during a race.
The next step of this study will be focused on the utilization of
the CD-Adapco ADJOINT FLOW SOLVER tool
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi
AKNOWLEDGEMENTS
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Thanks are due to A. Ciampa and E. Mazzoni (INFN of Pisa);
through their huge research activity on computer networks,
applied on our HPC, they made the computing system very
efficient and easy to use, enhancing its performances and
improving its reliability.
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THANK YOU
…fair winds and calm seas!
STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi