Aerospace Education & Research in the Area of Design Diamond Jubilee Lectures Department of Aerospace Engineering Indian Institute of Science, Bangalore K. Sudhakar Centre for Aerospace Systems Design & Engineering Indian Institute of Technology Mumbai October 30, 2003

Year 1997 “Technology Perspective – The Next Decade” Aeronautics Research & Development Board AR&DB/TC/001, May 1997 Suggested centres of excellence to be supported by AR&DB are; –CFD –Advanced Composites –Systems Design & Engineering ? ?

Years Aerospace Design as a discipline at IITB –Specialization dropped –Courses had tapered off –Design, Build Or Open ended problems shunned –No research interest among faculty March 1998 : AR&DB Sanction arrives

CASDE : July 1998 Mission CASDE shall strive to develop and retain strong links with Indian Aerospace Industry and shall engage in R&D activities with worldwide visibility

Objectives of CASDE M. Tech Programme in Systems Design & Engineering Modeling & Simulation Laboratory System Design Methodologies Awareness Creation

M. Tech in Systems Design & Engineering What do others teach? What can draw / retain student interest? What will faculty want to teach? What will industry want? What all should be taught? Form a composite team Brain-storming session

M. Tech in Systems Design & Engineering Courses of study –System Modeling & Simulation $ –Optimization for Engineering Design $ –Systems Engineering Principles $ –Statistical Methods for Analysis & Design –Multi-disciplinary Design Optimization # (MDO) –Applied Mechatronics $ (hands on course) System Level Studies – RC Model Aircraft $ Also available as short courses # Coordinates a Special Interest Group on MDO (SIG-MDO)

Laboratory and Other Infrastructure Wind tunnel balance Propulsion system test facilities IM&S Laboratory  –COTS sensors, actuators,.... –R/C Model construction facilities, training –Data acquisition cards –Software Propeller test facility 50 gm force.

IM&S Laboratory

Launch Vehicle Simulator from VSSC

Applied Mechatronics Hands on course 2 hrs lecture + 3 hrs lab per week 2 projects

Student Projects Instrumented. 2.5 kg, 1.6 m Solar kg, 0.25 m Videography. 0.9 kg, 0.6 m Appreciated

ME Dual Degree Project : HILS Flight Dynamics & sensor models RTLinux + Comedi Real time simulation Choose WP NGC On-board Computer? Use hobby grade actuators Out of window display MHz RAM 1 MB, FLASH 256 kB 8 x 12 bit 100 kHz 15 PWM / 25 DIO 30 gm; 50 x 75 x 12 mm 4 RC servo actuators Aileron, elevator, rudder, throttle Overflying Mumbai Autonomous Flight : 4 Way Points

Flapping Wing Flight Dual-Degree Project in Aero: Flapping wing Unsteady aerodynamics for prescribed motion Aero elastic analysis for prescribed actuation Wing construction - Polyurethane foam. (IDC) Actuation mechanism for testing. (Robotics) B. Tech Project in Robotics (Robotics group) Mechanism design Kinematics prescribed Loads prescribed

Awareness Creation January - CEP in Applied Mechatronics February - 3rd Meeting of SIG-MDO April - Workshop on August - Brainstorming on System Analysis September - Int. Conf. MSO-DMES

We also! Traveling Course on Design, Build and Fly. (CASDE+ADA) Student Projects as Case Studies. –Naval Institute of Aeronautical Technology, Cochin. –Dept. of Aerospace Engineering, MIT, Chennai –Dept. of Aerospace Engineering, Parks College of Eng.,Coimbatore AeSI Wright Flyer Design Competition. (CASDE+ADA)

We also! AeSI Schools Outreach Programme. (CASDE+ADA) 30 events, 103 Schools, 5,600 students (Good part of events by CASDE) Arya explaining the intricacies of flight mechanics

Systems Engineering Process Requirements to lower level Context Solution from lower level Super System Sub System Level-3 Analysis Level-2 Analysis Level-1 Analysis Focus of CASDE Level – 1 : Good understanding of system; knowledge base, heuristic; Computationally less expensive; Usually not available for new systems. Level – 3 : Physics based modeling; computationally intensive, applicable to new systems (V&V?)

CASDE Activities Research activity –High fidelity models in design loop ( CFD,..) –Multi-Disciplinary Analysis (MDA) leading to Multi-disciplinary Design Optimization (MDO) Level-3 Analysis Level-2 Analysis Level-1 Analysis

Challenges! Human / Admin –People coming together (Design, A/D, Str, Prop, Controls) –Synchronizing funds (taken care of by ARDB) Technical –Non-availability of disciplinary codes –Neither here nor there!

MDO Elements Architectures Sensitivity Analysis Surrogate Modeling Variable Complexity

MDO Elements Architectures Sensitivity Analysis Surrogate Modeling Variable Complexity Optimizer How are the couplings handled? System Analysis

MDO Elements Architectures Sensitivity Analysis Surrogate Modeling Variable Complexity How are the couplings handled? System Analysis X F dF/dX?  f/  X,  f/  Y...  dF/dX How to evaluate  f/  x? ~ Finite difference ~ Continous/Discrete Adjoint ~ Automatic Differentiation

User Supplied Gradients Complex Analysis Code in Fortran Manually extract sequence of mathematical operations Code the complex derivative evaluator in Fortran Manually differentiate mathematical functions - chain rule FORTRAN source code that can evaluate gradients

User Symbolic Maths Manually extract sequence of mathematical operations Use symbolic math packages to automate derivative evaluation Code the complex derivative evaluator in Fortran Complex Analysis Code in FORTARN FORTRAN source code that can evaluate gradients

Automatic Extraction of Formulae Parse and extract the sequence of mathematical operations Use symbolic math packages to automate derivative evaluation Code the complex derivative evaluator in Fortran Complex Analysis Code in FORTARN FORTRAN source code that can evaluate gradients

Gradients by ADIFOR Complex Analysis Code in FORTARN FORTRAN source code that can evaluate gradients Automated Differentiation Package

ADIFOR Ver 2.0 First applied to VLM Recently to 3-D Euler –Multiblock, Structured grid –Central difference, FVM –JST scheme of artificial dissipation. –Multistage Runge-Kutta schemes. –Implicit residual smoothing and local time stepping Original CodeADIFORed Code No of lines4,09011,889 Exec. time (min) Code xf ADIFORed Code x f, df/dx ADIFOR

ADIFOR Ver 2.0 3D Euler ONERA M6 Wing 3D Euler  (L/D) ADIFORed 3D Euler  (L/D), d(L/D)/d   Error in Finite Difference Estimate of d(L/D)/d   =0.2  =0.02  =0.002  =

MDO Elements Architectures Sensitivity Analysis Surrogate Modeling Variable Complexity Response Surfaces? Design of Experiments Design & Analysis of Computer Experiments Designs?

MDO Elements Architectures Sensitivity Analysis Surrogate Modeling Variable Complexity Mix high & low fidelity methods VLM & Euler thumb rules + analysis

Design / MDO Studies –WingOpt –3D Duct –Hypersonic Vehicle Design under uncertainty +

MDO of Transport Aircraft Wing Analysis Block Aeroelasticity Iterator Optimizer FSQP INTERFACEINTERFACE History Block Input Processor Output Processor Aerodynamics (VLM) Structures MSC/ NASTRAN NASTRAN Interface

3-D Duct Design Entry Exit Location and shape known Geometry of duct from Entry to Exit ? Pressure Recovery? Distortion? Swirl?

3D-Duct Design Using High Fidelity Analysis  Low Fidelity Design Criteria - Wall angle < 6° - Diffusion angle < 3° - 6 * R EQ < ROC Fluent for CFD RSM / DOE DACE X 1-MIN X 1-MAX X 2-MAX X 2-MIN

Parameterization of HSTDV Body Design variables X D : {  1,  2,  3,  n_plan,  wc, w fac_pl, t fac_pl,, H cruise }

Hypersonic Vehicle – Discipline Interactions Ext. Compression Model : AM1 Ext. Configuration Model : AM2 Aero Model : AM3 Trim Model : AM4 Thrust Model : AM5 Performance Model : AM6 Y1: l 1, l 2, l 3, h 1, h 2, h 3 Y2: m a, M I, , p st Y3: (X,Y,Z) Y4: TOGW, C.G., Vol, Fuel mass Y5: C N, C m, C A Y6: TOGW_up,  T,  T, D Y7: Th_deliv, L p, M p Y8: Cruise Range 11 22 33  n_pl  w_c SWSW STST H cr Input variable Analysis Model Output  n_pl, S W  1…, H cr Variables not shared Shared variables Y1 Y1… Response from AM1 required as input in AM2

MDO-Framework Database Configuration Server Execution Manager MDO Controller Name Server Data Server OPT1 Optimizer Manager OPT2OPT3 AM1 Analysis Manager AM2AM3 GUI Control Data

Publications Journal International Conferences 0165 Core faculty = 4

Analysis for Design Design z = design variables f = objective h = equality constr. g = inequality constr. R = residue Interface Analysis R(z,p)=0 Optimizer z f, h, g z p Optimization Analysis Optimizer z f, h, g z = design variables f = objective h = equality constr. g = inequality constr MDO z = design variables f = objective h = equality constr. g = inequality constr. R = residue Analysis-1 R 1 (z,p 1 )=0 Interface Optimizer z f, h, g z p Analysis-2 R 2 (z,p 2 )=0 Y12 Y21

MDO-Architectures MDO z = design variables f = objective h = equality constr. g = inequality constr. R = residue Interface Optimizer z f, h, g z p Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 Y12 Y21 Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 Y12 Y21 z p

MDO-Architectures Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 Y12 Y21 z p p Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 Y12, Y21 z  1 = Y12 - Y12 *  2 = Y21 - Y21 * Analysis   Evaluator Y12 * Y21 *

MDO-Architectures MDO z = design variables f = objective h = equality constr. g = inequality constr. R = residue Interface Optimizer z f, h, g z p Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 Y12 Y21 MDO z = design variables f = objective h = equality constr. g = inequality constr. R = residue Interface Optimizer z, y 12, y 21 f, h, g  1,  2 p  1,  2 Analysis-1 R 1 (z,p 1 )=0 Analysis-2 R 2 (z,p 2 )=0 z, y 12, y 21

3D-Duct Design Using High Fidelity Analysis X 1-MIN X 1-MAX X 2-MAX X 2-MIN  Low Fidelity Design Criteria - Wall angle < 6° - Diffusion angle < 3° - 6 * R EQ < ROC Fluent for CFD RSM / DOE DACE

3D-Duct Design Using High Fidelity Analysis X 1-MIN X 1-MAX X 2-MAX X 2-MIN  Low Fidelity Design Criteria - Wall angle < 6° - Diffusion angle < 3° - 6 * R EQ < ROC Fluent for CFD RSM / DOE DACE

MDA - System Analysis Performance -ilities? life cycle? Cost, etc.? Parameters A-2 A-4 A-1 A-5 A-3

Design & Analysis of Computer Experiments Regression fit + Stochastic process Single global fit Variability in prediction known and exploitable x x x x x Estimates of Predictive error x Computer exp DACE Fit

Building Models Using DACE x x x x x 5% predictive error x = Computer exp DACE Fit x x x Use multi-modal GA to identify ‘n’ highest peaks. Test if they are higher than 5% Add computer experiments at those spots

Design - Publications Journal of Aircraft Volume 40 Number 4 July 2003 Total number of papers - 22 Number of papers addressing design - 4