1. Srinivasan Memorial Lecture The Aeronautical Society of India, Trivandrum VSSC K. Sudhakar Centre for Aerospace Systems Design & Engineering Department of Aerospace Engineering Indian Institute of Technology Mumbai 400 076 June 27, 2003

2. “I would love to visit IIT Bombay and get briefed” Dr. S Srinivasan May 17, 1999 Breakfast table at SHAR Guest House

3. Years 1996-1998 AR&DB Centres –CFD –Composites –Systems Design & Engineering ? ? Aerospace Design as a discipline in Universities –Specialization dropped –Courses had tapered off –Design, build Or Open ended problems shunned –No research interest among faculty IIT Bombay decides to take a plunge! –What made it fail earlier?

4. Aerospace Systems Design and Engineering in Universities System Level Studies Masters Level Specialisation Design Optimization / MDO At CASDE we also.....

5. System Level Studies MAV Instrumented. 2.5 kg, 1.6 m. 2000 Solar. 0.13 kg, 0.25 m. 2002 Videography. 0.9 kg, 0.6 m. 2001

6. MAV Challenges / Preparations 2 kg, 0.6 m Autonomous Video-platform Low Reynolds number flows Wind tunnel balance Miniaturisation using COTS Construction methods Propulsion system (60% weight) Autonomous missions  HILS Propeller test facility 50 gm force.

7. Launch Vehicle Simulator from VSSC

8. H/W In Loop Simulator for MAV Flight Dynamics & Sensor models On-board Computer ? Hobby grade actuators Way Point Navigation –ADDR –ADDR + GPS Out of window display Problem opened up for C&G specialists INS-GPS Module, M Tech thesis in EE 68332 @16 MHz RAM 1 MB, FLASH 256 kB 8 x 12 bit ADC @ 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

9. Flapping Wing Flight

10. Flapping Wing Vehicle Unsteady wing aerodynamics with prescribed motion in flapping & twisting -VLM. Coupled aeroelastic analysis. Arrive at structural definition. Tailoring to get desired twisting by only flapping actuation. Construction of the wing Design and build the flapping mechanism M Tech in robotics group..

11. Flapping to Induce Twisting Wing spar to be rigid rod. Used for flapping Outer sleeve has low and tailored torsional stiffness Wing strips mounted on outer sleeve

12. Flapping to Induce Twisting Wing spar & flapping hinge rigid and one piece. Wing surface - film

13. IMS Laboratory

14. M Tech Specialisation in Systems Design & Engineering Design Optimization - I Optimization laboratory Design Optimization - II Modeling & Simulation Applied Mechatronics Systems Engineering Principles 12 34 5 6 78 Takeoff at sea level d ≤ 2150 m Climb to 11000 m at best ROC ≥ 11 m/s Loiter 45 min (Reserve) Land at sea level d ≤ 1220 m Descend to 1500 m Cruise for 3000 Km at best range M ≥ 0.74

15. Design Optimization MDO

16. Design Optimization / MDO Airborne Early Warning System –Complex system, simple models. Maneuver Load Control –Existing system, database driven Hypersonic Launch Vehicle –New system, simple models, system analysis Aero-elastic Wing Design –Simple models –Intermediate level models –FEM + VLM

17. MDO System analysis –Ownership of disciplinary analysis? –Integration strategy? –Human & technical issues Strategies that will –Accommodate above concerns –Allow bringing in science based, compute intensive analysis

18. Why do you want my program? I have a new version of analysis software You have to know my code to be able to execute it! System Designer’s Nightmare! I cannot find the correct tuning parameters! Integration Issues

19. MDO Frameworks Commerical Frameworks –iSIGHT –Phoenix Integration –Dakota (Sandia labs) CASDE MDO-Framework http://www.casde.iitb.ac.in/MDO/framework/ Design Optimization Course during 2003 will be offered using CASDE MDO-Framework

20. Multi-disciplinary Design Optimization 3D-Duct Design –Parametrization, meshing, simple analysis –CFD (NS)? Wing or Vehicle –CFD (NS / Euler)? Hypersonic Nozzle Design –CFD (Euler)?

21. Optimization

22. Optimization – Design Space Search Brute force. Grid the space, evaluate function, sort to identify minima. Evolutionary. Still too many function calls. –Genetic algorithms –Simulated annealing Gradient based methods –Local optima –Small number of function calls if gradients good! –Suited for compute intensive problems.

23.           X1X1 X2X2 Brute Force Search

24. GA / SA Search       X1X1 X2X2

25. Gradient Based    X1X1 X2X2 Gradient of functions Required!

26. How to evaluate gradients? Consider design of wings; –Design variables, x = [b, C] –Objective function, f(x) = C L Analysis is CFD –Give values to x = [b, C]  Wing  mesh –Run a CFD code and generate pressure distribution –Integrate pressures on body  C L How to evaluate

27. Methods to Evaluate Gradients? Finite difference method. Easy to implement, but problematic? Complex variables approach, requires source ADIFOR – Automatic DIfferentation in FORtran; requires source. Analytical accuracy Surrogate Modeling – Surface fits –Response Surface Method (RSM / DOE) –Design & Analysis of Computer Experiments

28. Finite Differenced Gradients? Finite Difference Method n design variables  (n+1) CFD runs

29. Problem with Finite Differencing? Only (n+1) CFD runs? Correct step size for FDM is important! Will demand more CFD runs! b CLCL Iterative Convergence Criteria

30. Complex Variable Approach Evaluate f{x + i e} ; e << 1 f(x) = Real Part { f(x + i e) } - f”(x) e 2 / 2 df/dx = Imag Part { f(x+ i e) } / e - f ”’(x) e 2 / 6 CPU time up by 3, RAM up by 2 subroutine func (x, f) real x, f subroutine func(x, f) complex x, f

31. 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

32. 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

33. 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

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

35. Surrogate Modeling DOE / RSM modeling in physical experiments. experimental point RSM. Least Square Fit. y = a 0 + a 1 x + a 2 x 2... Fitted model is smooth and easily differentiable. Curse of dimensionality! 2 k function evaluations Sequential RSM. x y

36. 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

37. Building Models Using DACE - An Idea! 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

38. We Also.... Travelling course on design Schools Outreach Programme Design Competition - ‘Design, Build, Fly’ KVPY Scheme for encouraging innovators of tomorrow Practical training for other engineering college students

39. People Ashok Joshi PM Mujumdar SK Sane Kurien Isaac Prasanna Gandhi Sanjay Bhat Anil Marathe K Sudhakar GR Shevare D Henry Umakant Shamkar Sivan Shyam Geethai Hemendra Arya Amitay Isaacs