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Multi-Objective Design Exploration (MODE) - Aerodynamic Applications
at Tohoku University
Dr. Daisuke Sasaki
Aerodynamic Design Lab.Department of Aerospace Engineering
Tohoku [email protected]
Contents• GCOE• MODE Application• Data Mining for Aerodynamics• Conclusion
GCOE: Overview• Global COE Program at Institute of Fluid Science and Dep’t of Aerospace
Engineering at Tohoku University.• Integration of Knowledge: Establishment and Expansion of International
Education and Research Center for Flow Dynamics.
GCOE: Activities• International Conference on Flow Dynamics (Nov. 4-6, 2009)• Internship Internship project• International Joint Laboratory – Design Exploration
MODE: Introduction• Progress of Computational Fluid Dynamics (CFD) for an aircraft
enables reliable prediction of aerodynamic performance at steady state.• Aerodynamic optimization has been frequently conducted.• We have proposed Multi-Objective Design Exploration for further
improvement of designs through knowledge discovery:– Multi-Objective Optimization, Data Mining, Visualization, etc.
MODE: importance• Multi-Objective Design Exploration
– Design exploration rather than optimization• Knowledge discovery from optimization results is more important than
obtaining optimal solutions– Optimization results contain
• Relationship between design variables and objective function• Importance of design variables• Trade-off between multiple objective functions
– Our approaches are • Efficient search - Kriging approximation model• Multi-Objective Optimization – Genetic Algorithms• Data Mining – ANOVA, SOM, visualization, etc.
f2
f1
Trade-off
MODE: Procedure• Flowchart
– Construct initial samplings for approximation model using Latin Hypercube Sampling
– Build Kriging approximation model for each objective
– Search non-dominated solutions on approximation model with MOGA by considering objective and its EI value
– Analyze additional sample points to update accuracy of approximations
– Data Mining based on ANOVA
CFD
Hybrid Mesh Generation
START
END
Generation of initial sample pointsby the Latin Hypercube Sampling
Update of the Kriging modelSelection
Generation of initial populationand evaluate each individual
Construction of initial Kriging models
Selection of additional sample pointsfrom non-dominated solutions
Evaluate new individuals
Crossover and mutation
NGEN>100
Continue?
Generation of a fixed-slat airfoil shape by Free-Form Deformation
CFD
Data Mining
Initial Model
Model Update
Application: PAV
• Application of Multi-Objective Design Exploration method to mild-stall wing for PAV (Personal Air Vehicle) or UAV (Unmanned Aerial Vehicle) use– Multi-Objective Optimization by MOGA– Approximation objectives by Kriging Method– Functional Analysis of Variance (ANOVA) for Data Mining
© Terrafugia Inc. 2006http://www.cafefoundation.org/v2/pav_home.php
PAV Wing: Introduction• PAV (Personal Air Vehicle) for leisure and future commuter
– 2-6 seats– 150-200 mph– akin to drive a car
• Demands of PAV for popularity– Operability: simple – Comfort: noise, quiet– Efficiency: performance– Safety: stall characteristics– Reliability: – Cost:
Stall characteristics is quite important for small vehicles as stall often leads flight accidents: taking-off and landing failure, spin in flight
Mild stall
Sudden drop
CL
Angle of attack
PAV Wing: Introduction 2• Targets of our research are to
– Accomplish mild-stall characteristics for safety enhancement– Achieve high Cl at high AOA for landing and taking off– Improve cruising performance for practical use
Idea to realize mild-stall characteristics is to prevent sudden drop of CL by making use of two airfoils having different stall characteristics
NACA4415SLAT4415
Tip Root
2/3 Original
PAV Wing: Introduction 3• Original – slotted wing
– Inboard: NACA4415– Outboard: SLAT4415
• Simple – rectangular wing– NACA4415
Tip
Mach contours at 20% and 80% span station at aoa of 24
TipSimple Original
NACA4415SLAT4415
TipRoot 2/3
Due to the different stall characteristics, slotted wing achieved mild stall characteristics.
Original
PAV Wing: Introduction 4• A slotted wing (Original wing) has favorable
stall characteristics, but the aerodynamic performance at cruise is seriously low.
170count
To improve cruise performance is necessary to realize efficient PAV
SLAT4415 (based on NACA4415)
Original
Design Problem• Objectives
– Maximization of L/D at Cl of 0.4 (cruise efficiency)– Maximization of Cl at aoa of 16 (take off performance)
• Constraint– Cl > 1.9 at aoa of 16
• Design variables: 30– 15 control points for x,y direction
• Optimizer – MOGA coupled with Kriging model– 120 initial samples by Latin Hyper-cube Sampling– 12 updates for improving Kriging model accuracy
• CFD evaluation– 2 CFDs per design by TAS 2D NS with SA– Mach number: 0.2– Reynolds number: 1.5x106
SLAT4415 (based on NACA4415)
fixed
Optimization Result
• Optimization Result
Mopt1 (L/Dcruise Max)
SLAT4415
NACA4415
airfoil L/D @ Cl0.4 Cl @ aoa16
Mopt1 26.77 1.96
NACA4415 29.99 1.58
SLAT4415 16.21 1.92
cruise
PAV: Sharp nose effect• Sharp nose effect
– ANOVA shows the influence of leading edge shape is small for objective function (L/D @ Cl0.4)
– Mopt1B is modified to blunt nose.– Mopt1S has sharpened leading edge nose.– L/D for 3 shapes are more or less similar.
Mopt1 C l = 0.4 Mopt1B Mopt1S
airfoil L/D @ Cl0.4 Cl @ aoa16
Mopt1 26.77 1.96
Mopt1B 26.61 1.96
Mopt1S 26.55 1.92
CP9_YCP1_YCP9_XCP2_YCP8_YOthers
PAV: ANOVA • Effect of design variables to L/D at Cl of 0.4 (objective1)
CP9_Y
CP1_Y
To achieve high L/D, leading edge and trailing position of a slat is deformed to flow directions
Positive directionNegative direction
Positive directionNegative direction
DM: Procedure • Data Mining Procedure
– Data Preparation
– Mining Methods & Thresholds Selection
– Mining Result Visualization
– Knowledge Evaluation and Validation• Testing
– Knowledge Fusion• Cross validation, bootstrap
– Decision Strategies
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.008 0.01 0.012 0.014 0.016 0.018
CD(supersonic)
CD(transonic)
DM: Data Quality Mining • Data preparation: Low Quality Data Problem
– Inconsistent Data– Duplicate Data– Missing Data– Outliers– Censored and Truncated Data– Time-dependent anomalies
• Data Quality Mining: data quality measurement and improvement through Data Mining techniques– Exploratory Data Analysis– Multivariate Statistics– Classification– Clustering– Visualization– Quantitative Data Cleaning
DM: Visual Analytics• Visual Analytics: Science Discovery Method
– 70s – 80s CAD/CAM, 3D modeling, animation– 80s – 90s Scientific visualization – 90s – 2000s Information visualization– 2000s – Visual Analytics
• Science of analytical reasoning facilitated by interactive visual interfaces
• Interactive Visualization for knowledge discovery in complex data• Tight integration of Visual and Automatic Data Analysis Methods
with Database Technology for a scalable decision support
Data
Visualization
Models
Knowledge
Visual Data Exploration
Automatic Data Analysis
DM: Applications and Future• Potential fields
– Data Quality Management for Approximation model– Data Mining for optimization results
• Classification, pattern analysis, clustering• Association rules, causal analysis• Time-series data feature detection
– Uncertainty assessment– Unsteady data analysis, feature capturing– Decision-Making support
Applications at NASA (Dr. Srivastava, NASA Ames)• Virtual sensors in Earth Science and Astrophysics• Decision-Making support of flight readiness review or
aviation safety (vehicle health management)
• Future ideal framework is to integrate– Physics-based model– Machine Learning-based model
DM: Joint-Lab.• GCOE Joint Laboratory of Design Exploration
“To advance optimization and data mining techniques for real-world applications through international collaboration”– International workshop (MLA09 – 2nd workshop) – Internship program for PhD students– Collaborative Research
• To improve accuracy of approximation model by applying different techniques to various test problems and applications.
• To investigate the characteristics and usefulness of various data miniig techniques by applying them to optimization results.
• To develop a useful data fusion method for reliable numerical computation and optimization.
– Dataset of aerodynamic optimization results will be available for applying various tools of data mining techniques.
Please contact Sasaki ([email protected])
Conclusion• Multi-Objective Design Exploration is important and useful for
aircraft designs to extract important information, particularly when– Design space or problem is unknown – innovative configuration– Design space is large – conceptual design
• There are many Machine Learning techniques useful to improve aerospace design exploration system:– Data Mining– Data Quality Management– Visual Analytics– Etc.
Last Slide• Thank your for your attention and participant
1/2b out
c root
Λ in =automatically
Λ out
c kink1/2b in
γ kink
γ tip
c tip=automatically
t/c root
t/c tipFLOW
Λ in_TR =0 deg
t/c kink
1/2b out
c root
Λ in =automatically
Λ out
c kink1/2b in
γ kink
γ tip
c tip=automatically
t/c root
t/c tipFLOW
Λ in_TR =0 deg
t/c kink
TAS-Code Development
and Its Applications
Next-Generation CFD (Building-Cube Method)
Multi-Disciplinary Analysis and Design Methods
Conceptual Aerodynamic Design of New Aircrafts
Research on CFD
Research activities at Aerodynamic Design Laboratory
Announcement
• Post-workshop proceeding:– Deadline : August 3rd– Maximum 4 pages– To [email protected]
– Proceeding will be sent to you directly, so please write down your name and postal address on the sheet.
• Dinner tonight– If you want to join for dinner, please tell David now.
We will meet at 7:30 pm at hotel lobby.
• GCOE invited speakers– Please give me the documents now for reimbursement..
Optimal Design (Mopt1) • Optimal design (Mopt1)
– As estimated by ANOVA result, L/D max design (Mopt1) is modified to lean the shape for flow direction.
– Separation behind the slat is suppressed, though large separation observed for slat4415.
– Lift is maintained at high angle of attack.
Mopt1 C l = 0.4 Slat4415 at Cl of 0.4Mopt1 aoa = 16
airfoil L/D @ Cl0.4 Cl @ aoa16
Mopt1 26.77 1.96
NACA4415 29.99 1.58
SLAT4415 16.21 1.92
Mopt1 and SLAT4415
Characteristics of MOpt1 • Mopt1_single is designed by connecting the outline of Mopt1.• Mopt1 shows higher Cl at low aoa but sharp drop of Cl at high
aoa compared to SLATT4415.• Mopt1_single shows lower Cl compared to NACA4415.
airfoil L/D @ Cl0.4
Cl @ aoa16
Mopt1 26.77 1.96
NACA4415 29.99 1.58
SLAT4415 16.21 1.92
Mopt1 and Mopt1_single
Mopt2 and Mopt1 airfoils • Mopt1_3D did not show mild-stall characteristics.• From the solutions, an airfoil having relatively milder and higher Cl at high
aoa airfoil is chosen (Mopt2).• Mopt2 is superior to SLAT4415. airfoil L/D @ Cl0.4 Cl @ aoa16
Mopt1 26.77 1.96
Mopt2 24.57 1.92
NACA4415 29.99 1.58
SLAT4415 16.21 1.92
Mopt1@24 Mopt2@24
Approach: ANOVA
• The functional analysis of variance (ANOVA )– In order to identify the effect of each design variable to
the objective functions, the total variance of this model is decomposed into the variance component due to each design variable.
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