1. Marcello Tobia Benvenuto
MASTER DEGREE DISSERTATION IN MECHANICAL, AERONAUTICAL ENGINEERING
Development of an automatic shape optimization platform for a laminar profile
March - September 2013
Relatori :
Prof. Jan Pralits
Ing. Thomas Michon
Studente :
Marcello Tobia Benvenuto
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2. single turboprop aircraft:
Introduction
Daher Socata produces
the world’s fastest
single turboprop aircraft:
TBM 850.
As each aeronautic company, Reduce the consumption
it works every day to improve
the aircraft performance Increase the max. speed
Reduce the drag
on the surfaces:
WING
Fluid mechanics
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3. A thin layer arises close to the shape, called boundary layer.
Physical phenomenon
When a body is in motion in a flow, the flow adhere to it because of the viscosity.
A thin layer arises close to the shape, called boundary layer.
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4. Laminar boundary layer: Turbulent boundary layer:
Physical phenomenon
External disturbances can enter the boundary layer and
generate a turbulent flow through a Transition process.
Skin Friction
X/C
Laminar boundary layer:
Thin with regular streamlines;
low skin friction.
Turbulent boundary layer:
Thick with irregular fluctuations;
high skin friction.
The transition phenomenon is very sensitive to the shape variations
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5. Objective
Reduce the friction drag on an airfoil by keeping the flow laminar over the largest possible portion of the surface.
Automatic Shape Optimization
Advantages:
1) Save time during a process
2) Run multiple repetitive simulations
3) Analyze automatically the good results, finding the optimum
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6. Contents Optimization platform for 2D Geometry
2D optimization High and High/Low speed
- results
- discussion
Creation wing
Conclusions
Future works
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7. Why a 2D geometry? The wing’s behaviors are given by its profiles.
Relative Thickness: 16%
Chord: m
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8. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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9. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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10. Create the 2D geometry
To limit the number of the geometric design variables
Describing the shape with a small set of inputs
9 Polynomial approximations of curves
CAD Software: Catia V 5
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11. Create the 2D geometry Constraints Design Parameters
Radius of the circle
Chord = 1 meter
Position of point 2 and 9 inside square
Thickness at 25% and 75%
of the chord fixed.
Tension of points 2,3,8,9
Thickness of trailing edge
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12. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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13. Create the domain and the mesh
Different domains and meshes have been investigated
to find the best grid in terms of time and quality
O-type domain
Radius = 90 meters
Grid close to the profile:
Grid
Profile
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14. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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15. Key point for the stability analysis
Flow solver
Numerical solution of the Navier-Stokes’s equations
FLUENT
Velocity and pressure distribution
Pressure Coefficient distribution on the root airfoil of TBM 850. Cruise conditions.
Key point for the stability analysis Cp
X/C
Smoothness
Good quality
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16. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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17. Boundary layer and its stability: bl3D
It calculates the parameters of the boundary layer from the Cp distribution
bl3D code
Laminar Boundary Layer's Equations
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18. Streamwise Wave number
Boundary layer and its stability: NOLOT
NOLOT is based on the Linear Stability:
Flow decomposed in mean flow and unsteady disturbances
u = U + u'
The unsteady disturbance is represented by a wave with infinitesimal amplitude
Frequency
Streamwise Wave number
Spanmwise Wave number
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19. Boundary layer and its stability: NOLOT
Semi-empirical eN method
Mack’s Law:
N factor
Turbulence intensity
N = – 2.4 ln(Ti) < Ti <
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20. Objective functions To maximize the position of transition
A change of the shape of a profile can lead to different value of Cl and Cm
Changes of global repartition of lift
Stability problems
Stalling problems
To minimize ∆Cl = |Cl – ClTBM|
To minimize ∆Cm = |Cm – CmTBM|
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21. Optimization steps and tools
Create the 2D geometry
Catia V 5
Create the domain and the mesh
ANSYS: Design Modeler and Mesh
Optimization platform
Flow Solver
ANSYS: Fluent
Boundary layer and its stability
bl3D and Nolot code
Mode Frontier
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22. Optimization platform: Mode Frontier
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23. Optimization platform: Mode Frontier
Lift and Mom. coeff
∆Cl ∆Cm
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24. Contents Optimization platform for 2D Geometry
2D optimization High and High/Low speed
- results
- discussion
Creation wing
Conclusions
Future works
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25. Optimization 2D High speed
high speed (cruise): M=0.51; h=26000 feet; aoa=0 degrees
Strategy optimization
- explore all the domain of input parameters DOE
- optimize the best profiles found by DOE with genetic algorithm
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26. TBM (trans. 26% of the chord)
Pareto front opt. 2D high speed
399 profiles have been explored in 8 days
Max ∆Cl 3%
∆Cl
Max trans. 47% of the chord
TBM (trans. 26% of the chord)
Transition location
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27. Best solution opt. 2D high speed
BLACK = TBM RED = BEST
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28. c A big influence of the leading edge on the transition
Robustness solution for manufacturing?
0.07% of 1765 mm = 1.19 mm
c A big influence of the leading edge on the transition
Solution not robust
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29. Drag evaluation with transition model
To evaluate the difference of drag, the SST-transition model is used in Fluent to study the natural transition:
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30. Optimization 2D High/Low speed
To analyze stall characteristics at low speed,
the profile has been optimized also at take-off conditions
- High speed (cruise): M=0.51; h=26000 feet; aoa=0 degrees
- Low speed (take-off): M=0.18; h=0; aoa= > 15 degrees
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31. Objective functions Cruise condition:
To maximize the transition location
To minimize ∆Cl and ∆Cm
Take-off condition:
Maximize the max Lift coefficient
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32. Pareto front 2D opt. High/low speed
Cl low speed
Transition high speed
The objective functions are in opposition one with the other
The same optimization has been done for the tip profile of the wing
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33. Discussion optimization 2D
High speed
Big sensibility of the phenomenon by the shape variations
Transition moved from 26% to 47% of the chord
Viscous drag reduced of 14.26%
Improvements limited by the constraints of the shape: transition occurs close to the maximum thickness
High/low speed
Each flight condition requires a different optimal shape
The presence of a new O.F. has not penalized the transition (42%)
Improvements limited by the constraints of the shape
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34. Contents Optimization platform for 2D Geometry
2D optimization High and High/Low speed
- results
- discussion
Creation wing
Conclusions
Future works
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35. Creation wing
Creation of a wing with the optimal root and tip profile obtained previously
Wing parameters:
The same of the wing of TBM 850
- span: mm
- dihedral: 6.5 degree
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36. CFD Simulation 3D
To compare the wing of the TBM 850 with the wing using the optimal profiles.
TBM
NEW
Skin Friction
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37. Skin friction on profile at 50% of the span
Results 3D
Wing
Visc. drag
Press. drag
Total drag
Lift coeff
TBM
0.1919
New
0.1903
New
Skin Friction
TBM
Chord
Skin friction on profile at 50% of the span
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38. Discussion
The validation on the wing has given unexpected results in terms of drag:
The effects of the flows on 2D and 3D geometry are different
- trailing vortex
- cross flow disturbances
X - Wall shear stress
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39. Discussion
The validation on the wing has given unexpected results in terms of drag:
The effects of the flows on 2D and 3D geometry are different
- trailing vortex
- cross flow disturbances
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40. Contents Optimization platform for 2D Geometry
2D optimization High and High/Low speed
- results
- discussion
Creation wing
Conclusions
Future works
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41. Conclusions
I am familiar with software like Catia V 5, Fluent (2D and 3D), Fortran, Python, modeFRONTIER
I created an automatic shape optimization for 2D geometry
The strategy used, has allowed to obtain good results for 2D geometry
- transition phenomenon delayed from 26% to 47% of the chord
- Viscous drag reduced more than 14%
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42. Future work and suggestions
Optimization 2D:
New parameterization (CST) with other constraints can be tested
More time for the iterations can lead a better results
3D Validation:
To consider 3D effects we can run the following loop:
Study the flow around the wing
Take Cp distribution of three profiles of the wing (root, middle, tip)
Run optimization platform for the three profiles
To rebuild the wing with the three new profiles and study the flow on the wing
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43. Thank you for your attention
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