Design automation for aircraft design micro air vehicle application
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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

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Design Automation for Aircraft Design – Micro Air Vehicle Application

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Design automation for aircraft design micro air vehicle application

Design Automation for Aircraft Design – Micro Air Vehicle Application

David Lundström, Kristian Amadori


Mav micro air vehicle

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

Flygteknik 2010


Design automation for aircraft design micro air vehicle application

FluMeSFluid & Mechatronic Systems

Department of Management

and Engineering

Department of Computer and

Information Science

Flygteknik 2010


Mav design automation

MAV Design Automation

Flygteknik 2010


Design automation process

Performance Requirements

a.

Objective

Sensors and autopilot

b.

Component List

c.

Design Automation Process

Flygteknik 2010


Design framework

Spreadsheet model

Obj. function

Optimizer

Control variables

Weightwetted areaetc.

Geometry mesh

Component

specifications

Geometric

parameters

cD,

cm, cL

Propulsion system database

  • Motors

  • Motor controllers

  • Batteries

  • Propellers

    Database contains 300

    different “off the shelf” components

Database

Parametric CAD model

Aerodynamic model

Design Framework

Flygteknik 2010


Parametric cad model catia v5

Available

Thickness

Component

Component

x

x

X

X

User Def.Max X

User Def.Min. X

MIN

MIN

Total Allowed Range

Parametric CAD Model - CATIA V5

  • Model incorporates

    • External shape

    • Internal Structure

    • Internal Components

  • Key requirements

    • High flexibility

    • Robustness

Flygteknik 2010


Optimization

Optimization

  • Mixture of discrete and continuous variables, high coupling between variables, large solution space, numerous constraints.

     Genetic Algorithm

Flygteknik 2010


Sequential optimization

Step 1

Step 2

(Step 3)

(If geometry changes significantly)

Fast

Simple geometric and aerodynamic model

Expensive

Complex geometric and aerodynamic model

Geometry (continuous)

System Parameters (discrete and continuous)

System Parameters (discrete and continuous)

Fast

System and performance models

Fast

System and performance models

Sequential Optimization

Geometry (continuous)

Flygteknik 2010


Sequential optimization1

Step 1

Step 2

(Step 3)

(If geometry changes significantly)

Fast

Simple geometric and aerodynamic model

Expensive

Complex geometric and aerodynamic model

Geometry (continuous)

Geometry (continuous)

System Parameters (discrete and continuous)

System Parameters (discrete and continuous)

Fast

System and performance models

Fast

System and performance models

Sequential Optimization

Flygteknik 2010


Multi objective optimization

Pareto Front

Objective 2

Objective 1

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

Flygteknik 2010


Design framework mode frontier

Design Framework - Mode Frontier

Flygteknik 2010


Optimization results

Optimization Results

Example analysis with real components database

Flygteknik 2010


Pareto frontier designs

Mission Requirements:

Cruise speeed = 70km/h

Stall speed= 35km/h

Payload = 60g video camera

T/W ratio= 0.7

Endurance

Weight

Pareto Frontier Designs

Flygteknik 2010


Automated manufacturing

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


Validation and flight testing

Validation and Flight Testing

Flygteknik 2010


Conclusions

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


Future work

Future Work

  • Validation of aerodynamics and propulsion

  • Flight simulation – Control system design

  • Increased model accuracy (CFD)?

Flygteknik 2010


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