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 Defense Advanced Research Projects Agency Flygteknik 2010

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

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

MAV Design Automation Flygteknik 2010

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

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

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

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

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

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

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

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

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

Design Framework - Mode Frontier Flygteknik 2010

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 Flygteknik 2010

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 Flygteknik 2010

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