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

STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi 2

The capabilities of the new configuration were deeply

analysed

Scientific Research and Sport

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

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

The boat

• 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

Work Strategy

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

2-D Airfoil Shape Optimisation

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.

modeFrontier Optimisation Software, developed by Esteco

• Intuitive management of the Logical Flow

• Set of Optimisation and DOE Generation Algorithms

• Statistical Analysis Tools

Optimisation Procedure – Flow Diagram

Grid Sensitivity Analysis

• 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 :

0.260.270.280.290.3

0.310.32

0 20000 40000 60000 80000 100000 120000n.° celle (2D)

0.0075

0.0085

0 20000 40000 60000 80000 100000 120000n.° celle (2D)

Compromise choice between solution stability and computational costs

Number of Cells (2-D) Number of Cells (2-D)

Number of Cells (2-D) ~ 50k

12 STAR Global Conference 2014– G. Lombardi, F. Cartoni, M. Maganzi

Geometry

Optimisation Parameters

Rear Airfoil

• t_c_2

• x_tc_2

• 3 Shape Parameters

Global Parameters

• Chord

• GAP

• Theta_2

14 Optimisation Parameters

Front Airfoil

• t_c_1

• x_tc_1

• 3 Shape Parameters

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

• Design of Experiments creation : SOBOL

• Genetic Algorithm : MOGA II

• Population : 250 designs

• Generations : 20

Geometry

Airfoils generation Script

Use of Bézier Curves to realize the shapes

Geometry

Constraints check

• Geometrical

• Structural

• Regulatory

Geometry

1/40 chord

25 chords

10 chords 14 chords

Realization of the Calculation Domain

CFD Analysis

• 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

2-D Airfoil Optimisation

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.

Root Airfoil Optimisation

• 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.

Step 1 – Preliminary Optimisation of the Front Airfoil

Step 1 – Relative Optimum Configuration

Step 2 – Preliminary Optimisation of the Rear Airfoil

Step 3 – Complete Optimisation

Analysis of the Results

Global Parameters

Front Airfoil

Rear Airfoil

Design 2380

-300 300 600 900 1200 1500 1800 2100

Analysis of the Results – Validation with finer grid

Theta_2

Theta_2 Theta_2

Optimum Theta - Cl O

GAP = 0.015

Theta_2

Comparison between IDs 2380 - 255

Cl = 0.3

3-D Wing-Sail Optimisation

• 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

• Parametric representation of the Geometry

New set of Optimisation Parameters

New Objective Functions and constraints

Geometry

• 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_middle

Geometry

δ - ε/2

δ - ε

R_root

R_middle

• Wing Height

• Root Chord

• Middle Chord

• Tip Chord

• α

• δ

• ε

Physics Models

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

Physics Models

• Vb = 2 m/s

• Vtw@10m = 5,36 m/s (Von Karman Profile)

Vwa components (Global Reference Frame)

The Mesh Settings are chosen with the same criterions described for the 2-D Optimisation

~ 1,7 MLN cells

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

The HPC

• 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

• Design of Experiments: SOBOL

• Genetic Algorithm: MOGA II

50 Designs

20 Generations

444 Designs Analized

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 1 2 3 4 5 6 7 8 9 10

Thrust [N]

6,5 MLN cells, Run on 256 CPUs

Conclusions and Future Developments

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

AKNOWLEDGEMENTS

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.

THANK YOU

…fair winds and calm seas!

Optimisation of a Wing-Sail shape for a small...

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