1. Design Automation for Aircraft Design – Micro Air Vehicle Application
David Lundström, Kristian Amadori

2. MAV – Micro Air Vehicle
DARPA definition: Physical size lesser than 15cm
“General” definition: Size <0.5m, Weight <500g
Unmanned aircraft small enough to easily be carried and operated by one person
Police, civil rescue, agriculture, meteorology, military
Defense Advanced Research Projects Agency
Flygteknik 2010

3. FluMeS Fluid & Mechatronic Systems
Department of Management
and Engineering
Department of Computer and
Information Science
FluMeS Fluid & Mechatronic Systems
Autonomous Unmanned Aircraft Systems Technologies
Flygteknik 2010

4. Characteristics of MAVs
Small size leads to limited performance and payload capacity  Mission specific design
Short design lifetime due to rapid development and miniaturization
Small series production
Low cost
Relatively simple design
Wide variety of ”off the shelf” components available
Automated design
Flygteknik 2010

5. MAV Design Automation
Flygteknik 2010

6. Design Automation Process
Performance Requirements a. b. c.
Component List
Sensors and autopilot
Objective
Flygteknik 2010

7. Design Framework Optimizer Parametric CAD model Aerodynamic model
Weight wetted area etc.
Geometric
parameters
Optimizer
Obj. function
Control variables
Parametric CAD model
Aerodynamic model
Spreadsheet model
Geometry mesh
cD,
cm, cL
Propulsion system database
Motors
Motor controllers
Batteries
Propellers
Database contains 300
different “off the shelf” components
Database
Component
specifications
Flygteknik 2010

8. Geometry Definition Tailless aircraft Wing parameters Fuselage Sweep
Dihedral
Twist
Wing profile
Chord lengths
LE and TE shape
Fuselage
Nose length
Cross section size
Tip Chord
Semi Span
LE Sweep
Nose Length
Root Chord
Flygteknik 2010

9. Database – Propulsion SystemPb Pd Pm
Pout
ηtot=ηb*ηd*ηm*ηp ηb ηd ηm ηp
Battery
Cell resistance
Cell capacity
Cell voltage
Nr. of serial cells
Nr. of parallel cells
Controller
Resistive losses
Losses depending on PWM signal, coupled to motor inductance
Classical electric motor model
Kv, I0, Rm
Blade element method Performance characterized by Thrust and Power coefficients as function of advance ratio v/nD
Prop Thrust and Power coefficients
0,00
0,02
0,04
0,06
0,08
0,10
0,12
0,20
0,40
0,60
0,80
1,00
1,20
v/nD 10 20 30 40 50 60 70 80 90
100 Cp Ct
? (%) Rb Ub Ib + T dT t Vs I0 Rm Um Im
Uemf M I +
Flygteknik 2010

10. Parametric CAD Model - CATIA V5
Model incorporates
External shape
Internal Structure
Internal Components
Key requirements
High flexibility
Robustness x
Available
Thickness
Component X
MIN
User Def. Min. X
Total Allowed Range
User Def. Max X
Flygteknik 2010

11. Optimization
Mixture of discrete and continuous variables, high coupling between variables, large solution space, numerous constraints.
 Genetic Algorithm
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12. Sequential Optimization
Step 1
Fast
Simple geometric and aerodynamic model
System and performance models
Step 2
Expensive
Complex geometric and aerodynamic model
(Step 3)
Fast
System and performance models
Geometry (continuous)
System Parameters (discrete and continuous)
(If geometry changes significantly)
Geometry (continuous)
Flygteknik 2010

13. Sequential Optimization
Step 1
Fast
Simple geometric and aerodynamic model
System and performance models
Step 2
Expensive
Complex geometric and aerodynamic model
(Step 3)
Geometry (continuous)
System Parameters (discrete and continuous)
(If geometry changes significantly)
Flygteknik 2010

14. Multi-objective optimization
Pareto Front
Multi-objective optimization
Multi-Objective Genetic Algorithm (MOGA II)
Software: Mode Frontier
Objective function:
Constraints on: stall speed, max. speed, CG position, thrust-to-weight ratio, component specifications
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15. Design Framework - Mode Frontier
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16. Optimization Results Example analysis with real components database
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17. Pareto Frontier Designs
Mission Requirements:
Cruise speeed = 70km/h
Stall speed= 35km/h
Payload = 60g video camera
T/W ratio= 0.7
Endurance
Weight
Flygteknik 2010

18. Automated Manufacturing
Test using FDM 3D printer: 270mm MAV
90g
60g
Benefits:
No ”craftsmanship” is needed
Geometric complexity – no influence on cost
Good accuracy and repeatability
Allows easy validation
Flygteknik 2010

19. Validation and Flight Testing
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20. Conclusions
Automated MAV design has been demonstrated and proven to be realistic.
Current modeling is a balance of accuracy and calculation speed. Propulsion system has highest impact on performance
Method can be seen as a stepping stone for improving conceptual design methods for larger UAVs and manned aircraft.
Key innovations to achieve automated design is:
Discrete propulsion system optimization using COTS-components
Unique composition of design framework
Sequential optimization process with increased model fidelity
Usage of Multi-objective optimization
Efficient method for internal component placement and balancing
3D printing for fabrication
Flygteknik 2010

21. Future Work Validation of aerodynamics and propulsion
Flight simulation – Control system design
Increased model accuracy (CFD)?
Flygteknik 2010