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Published byAbraham Warren Modified over 9 years ago
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Design Automation for Aircraft Design – Micro Air Vehicle Application
David Lundström, Kristian Amadori
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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
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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
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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
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MAV Design Automation Flygteknik 2010
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Design Automation Process
Performance Requirements a. b. c. Component List Sensors and autopilot Objective Flygteknik 2010
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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
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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
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Database – Propulsion System
Pb 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
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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
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Optimization Mixture of discrete and continuous variables, high coupling between variables, large solution space, numerous constraints. Genetic Algorithm Flygteknik 2010
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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
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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
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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 Flygteknik 2010
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Design Framework - Mode Frontier
Flygteknik 2010
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Optimization Results Example analysis with real components database
Flygteknik 2010
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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 Endurance Weight Flygteknik 2010
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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 Flygteknik 2010
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Validation and Flight Testing
Flygteknik 2010
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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 Flygteknik 2010
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Future Work Validation of aerodynamics and propulsion
Flight simulation – Control system design Increased model accuracy (CFD)? Flygteknik 2010
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