Transcript Slide 1

Design Automation for
Aircraft Design – Micro Air
Vehicle Application
David Lundström, Kristian Amadori
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
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Department of Management
and Engineering
Department of Computer and
Information Science
FluMeS
Fluid & Mechatronic Systems
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MAV Design Automation
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Design Automation Process
Performance
Requirements
a.
Component List
Objective
Sensors and
autopilot
b.
c.
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Design Framework
Spreadsheet model
Obj. function
Optimizer
Control
variables
Component
specifications
Geometric
parameters
Weight
wetted area
etc.
cD,
cm, cL
Geometry
mesh
Propulsion system database
•Motors
•Motor controllers
•Batteries
•Propellers
Database contains 300
different “off the shelf” components
Database
Parametric CAD model
Aerodynamic model
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Parametric CAD Model - CATIA V5
 Model incorporates
 External shape
 Internal Structure
 Internal Components
 Key requirements
 High flexibility
 Robustness
Available
Thickness
Component
User Def.
Min. X
XMIN
User Def.
Max X
XMIN
x
Total Allowed Range
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Optimization
 Mixture of discrete and continuous variables,
high coupling between variables, large solution
space, numerous constraints.
 Genetic Algorithm
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Sequential Optimization
Step 1
Fast
Simple geometric
and aerodynamic
model
Fast
System and
performance
models
Step 2
Geometry
(continuous)
Expensive
Complex
geometric and
aerodynamic
model
System
Parameters
(discrete and
continuous)
(Step 3)
Geometry
(continuous)
System
Parameters
(discrete and
continuous)
(If geometry
changes
significantly)
Fast
System and
performance
models
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Sequential Optimization
Step 1
Fast
Simple geometric
and aerodynamic
model
Fast
System and
performance
models
Step 2
Geometry
(continuous)
Expensive
Complex
geometric and
aerodynamic
model
System
Parameters
(discrete and
continuous)
(Step 3)
Geometry
(continuous)
System
Parameters
(discrete and
continuous)
(If geometry
changes
significantly)
Fast
System and
performance
models
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Objective 2
Multi-objective optimization
 Multi-Objective Genetic Algorithm (MOGA II)
Objective 1
 Software: Mode Frontier
 Objective function:
b
a





 WeightREF  
 Endurance 

 
  and max
max
Weight  
EnduranceREF  






 Constraints on: stall speed, max. speed, CG position, thrust-toweight ratio, component specifications
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Design Framework - Mode Frontier
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Optimization Results
Example analysis with real components database
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Pareto Frontier Designs
Mission Requirements:
Cruise speeed = 70km/h
Stall speed= 35km/h
Payload = 60g video
camera
T/W ratio= 0.7
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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
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Validation and Flight Testing
Geometrical Specs
Root Chord
208 mm
Tip Chord
56 mm
Wing Span
270 mm
LE Sweep
38 Deg
Twist
1 Deg
Nose Length
31 mm
Weight
185 g
Propulsion System Specs
Motor
Turnigy C1822
Battery
FlightPower EVO Light 3s350Mah
Propeller
APC 4.5x4.1
ESC
Turnigy Plush 6A
Predicted Measured
Maximum Speed
Error
[m/s]
26,4
25,0
5,3%
Endurance (VMax) [min]
6,1
6,0
1,6%
Weight
185
187
1,1%
[g]
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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
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Future Work
 Validation of aerodynamics and propulsion
 Flight simulation – Control system design
 Increased model accuracy (CFD)?
120,0
1,00
0,90
0,80
0,70
80,0
0,60
60,0
0,50
Eta
P(W) / n(rpm/100)
100,0
0,40
40,0
Pin
Put
20,0
n
Eta
0,0
4,000
5,000
6,000
7,000
8,000
0,30
0,20
0,10
0,00
9,000 10,000 11,000 12,000 13,000 14,000
U (V)
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