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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. 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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  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 Flygteknik 2010

  3. FluMeSFluid & Mechatronic Systems Department of Management and Engineering Department of Computer and Information Science Flygteknik 2010

  4. MAV Design Automation Flygteknik 2010

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

  6. 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

  7. 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

  8. Optimization • Mixture of discrete and continuous variables, high coupling between variables, large solution space, numerous constraints.  Genetic Algorithm Flygteknik 2010

  9. 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

  10. 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

  11. 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

  12. Design Framework - Mode Frontier Flygteknik 2010

  13. Optimization Results Example analysis with real components database Flygteknik 2010

  14. 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

  15. 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

  16. Validation and Flight Testing Flygteknik 2010

  17. 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

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

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