Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries...
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Transcript of Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries...
Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine
Blade Geometries
Lars Graening
[email protected]://www.honda-ri.de/
03 July 2009
International Workshop onMachine Learning for Aerospace
Marseille, France
Outline
Validation of the extracted knowledge
Universal representation of design modifications
Knowledge extraction from unstructured surface meshes
3D Turbine Blade
Knowledge extraction from a 3D turbine blade design data set
Ultra-low aspect ratio transonic turbine stator blade of a small Honda turbofan engine
Honda Business Jet
Part of the stator section 3D Blade Design
Pressure side
Suction side
Leading edge
Trailing edge
Knowledge Extraction
DesignDatabase
KnowledgeExtraction
Design data
(Design,Performance)
Knowledge
(e.g. Design Rules)
Pre-Processing
Optimizer
Design Generation& Modification
Evaluation
How can we extract knowledge from the design data resulting from various optimization runs where different shape representations
are used?
• CAD software
• Rapid Prototyping
• Splines,FFD / DMFFD
• …
• Aerod. Engineer
• Computer:
• Evo. Opt.
• Response Surface
• …
• CFD simulation
• Wind tunnel
• Real physicalenvironment
• …
Usually huge amount of design data is generated during the design and optimization process.
Universal Design Representation & Pre-Processing
ReferenceSurface Mesh
ModifiedSurface Mesh
Vertex AVertex A’
Displacement
Displacement:
,,, ri
ri
mj
mj
ri nvvvv
rmmr ff ,• Measure of the local deformation along the
normal vector of a reference design• Using performance difference instead of
performance values• An identification of corresponding vertices
is needed
The calculation of the displacement leads to a reduction in the number of parameters under consideration
Universal representation:
• Use unstructured surface mesh as a universal geometric representation
• Vertices sample the design surface, triangles define the neighborhood, normal vectors define local curvature
Knowledge Extraction from Aerodynamic Design Data
Where are unchanged and mostfrequently changed design regions?
What are the mean differences ofmultiple designs relative to a base design?
Which design regions are sensitiveto performance changes?
How similar are two designs?
N
r
N
rm
mrjiNNi
1 1
2,,1
2
ri
rr
iN
R
N
rmm
rmrri
mrjir
i
1,1
,,,
N
rmm
mrji
riN ,1
,,1
1
log
Knowledge Extraction from Aerodynamic Design Data
• A reduction of the parameters is needed• Assuming neighboring vertices with
similar sensitivity belong to the same design region
• Clustering vertices to sensitive design regions
• Cluster centers are used to analyze interrelations
How are distant design areasinterrelated?
Blade geometries
De
sig
n R
ule
s
Design Rules
A: A reduction of the blade thickness is expected to increase the performance0000 ,
7,
10,
8 THENANDANDIF ArCC
ArCC
ArCC
B: Surprisingly, reducing the thickness at the suction side but increasing the thickness at the pressure side is expected to decrease the performance
000 ,7
,8 THENANDIF Ar
CCAr
CC
Pressure Side:
Suction Side:
Investigate the consequences of an interrelated displacement of distant vertices to the performance
How reliability is the extracted information?
Knowledge Validation
Algorithm for Knowledge Validation:(using Direct Manipulation of Free Form Deformation)
Object Point
Control Point
Deform the design usingDirect Manipulation of Free-Form Deformation
Generate CFD mesh for thereference blade
Apply deformations to theCFD mesh
Simulate the flowusing CFD (HSTAR3D)
Dis
pla
cem
en
tP
erf
orm
an
ce
Kn
ow
led
ge
(e.
g. d
es
ign
ru
le)
Menzel, S., Olhofer, M., Sendhoff B.: Direct Manipulation of Free Form Deformation in Evolutionary Design Optimization, PPSN 2006
Knowledge Validation
Reference blade
Surface Deformation
CFD Grid DeformationResulting Blade Performance after CFD(Pressure Loss)
worse
better
CC7
Sensitivity
The displacement of vertex CC7 is positive
correlated with the aerodynamic pressure loss of the turbine blade!1. The hypothesis of the direct relation between
performance and displacement of CC7 is true
2. Using DMFFD for validation and utilization works out well
Knowledge Validation
A:
B:
738.9
499.11
Decrease in pressure loss of about -10.50 %
Increase in pressure lossof about 5.86 %
0000 ,7
,10
,8 THENANDANDIF Ar
CCAr
CCAr
CC
000 ,7
,8 THENANDIF Ar
CCAr
CC
The performed experiments confirm the expected outcome of the extracted hypothesis
Summary & Outlook
• framework for extracting knowledge from aerodynamic design data
• technique based on DMFFD for validating the extracted knowledge
• validation experiments provide evidence for the reliability of the knowledge extraction techniques
• outlook measurements are needed that allow to extract rules which are potentially interesting for the aero engineer
Thank You!