1. Multidisciplinary Design and Optimization (MDO) Natural Evolution of that Other Engineering Activity. Dr. Rob McDonald Lockheed Martin Endowed Professor Cal Poly, SLO UT Austin AIAA

2. Core Engineering Activities Analysis –Given a system, how do we expect it to perform? Test –Given a system, how does it perform? Design –Given a desired performance, what system do we want? Design is an inverse problem. Design is inherently different from Analysis & Test.

3. Complex Systems Systems are becoming more complex –Larger systems –Systems of systems –Networks, connections, & interactions –Longer life cycles – longer development cycles –Higher cost There are more constraints than ever before –Emissions –Noise –Safety Systems perspective not just for the system

4. Reconnaissance/Observation Federal observation balloon Intrepid being inflated. Battle of Fair Oaks, Va., May 1862. National Archives.

5. Multi-mission Aircraft Data compiled from Borer 2006

6. Stick and Rudder?

7. Communications

8. US Soldiers

9. Complex Systems Systems are becoming more complex –Larger systems –Systems of systems –Networks, connections, & interactions –Longer life cycles – longer development cycles –Higher cost There are more constraints than ever before –Emissions –Noise –Safety Systems perspective not just for the system Q: How do you analyze & design complex systems? A: SDAO / MDAO

10. NASA's Aeronautics Plan -Lisa Porter

11. “The Systems Analysis, Design, and Optimization team has identity at Levels 2 through 4...” - SFW Reference Document, Collier et.al. NASA's Aeronautics Plan -Bill Haller

12. “The Systems Analysis, Design, and Optimization team has identity at Levels 2 through 4...” - SFW Reference Document, Collier et.al. NASA's Aeronautics Plan -Lisa Porter

13. Not Just NASA DARPA ONR NAVAIR AFRL FAA Industry –Lockheed –Boeing –Northrop Grumman –Pratt & Whitney –General Electric etc.

14. Core Engineering Activities Analysis –Given a system, how do we expect it to perform? Test –Given a system, how does it perform? Design –Given a desired performance, what system do we want? Design is an inverse problem. Design is inherently different from Analysis & Test.

15. Analysis A – Model. X – Input Vector. A – Output Vector. Design Given a system, how do we expect it to perform? Given a desired performance, what system do we want? ΔX – Change Mechanism. X 0 – Initial Guess. A* – Desired Output.

16. Multidisciplinary Analysis (MDA)‏ – System. A – Component. X – Input Vector. A – Output Vector. a 1 – Feedforward Interaction. b 2 – Feedback Interaction. MDA Techniques focus on the challenges of this problem. System Decomposition & Integration Convergence & Consistency Model Approximation Information/Data Management Parallelization & Acceleration Error Propagation Validation etc.

17. Multidisciplinary Design Optimization (MDO)‏ MDO Techniques focus on the challenges of this problem. All of the challenges of MDA. + Design Exploration Optimization Constraints & Requirements Tradeoff Robust Design Decision Making Visualization Sensitivities & Growth etc.

18. Familiar Challenges Has anyone never… …performed an analysis?

19. Familiar Challenges Has anyone never… …changed an input and analyzed multiple cases? …wished it was easier? Parametric Analysis & Automation.

20. Familiar Challenges Has anyone never… …fit a curve to the points? …plotted the resulting curve? …estimated the curve’s error? Metamodeling / Surrogates & Visualization. Response Surface Equation, Least Squares Regression, Spline Interpolation, Neural Networks, Gaussian Processes, Radial Basis Functions.

21. Familiar Challenges Has anyone never… …wanted to explore a space more dimensions, but thought “There must be a better way to pick the points”?

22. Familiar Challenges Has anyone never… …wanted to do the same in more dimensions, but thought “There must be a better way to pick the points”? Design of Experiments. Face Centered Cubic, Orthogonal Arrays, Latin Hypercube, Monte Carlo. Not to mention parallelization.

23. Familiar Challenges Has anyone never… …estimated a derivative? …used that derivative to predict behavior? Sensitivity Analysis. Finite Difference, Adjoint Methods, Automatic Differentiation, System Sensitivity Analysis.

24. Familiar Challenges Has anyone never… …looked for the maximum or minimum of the curve? Subject to constraints? Optimization. Constrained Optimization, Gradient Based, Conjugate Gradient, Penalty Function, Stochastic Optimization, Genetic Algorithms, Synthetic Annealing,

25. Familiar Challenges Has anyone never… …been uncertain of inputs? …been uncertain of the analysis? Robust Design, Uncertainty & Error Propagation.

26. Familiar Challenges Has anyone never… …faced competing objectives? Decision Making. Pareto Frontier, Non-Dominated Solution, MADM, MODM, SAW, TOPSIS.

27. Natural Evolution of Design 1. Evolution of Complex Systems 2. MDO as the Solution to the Complexity of Systems 3. MDO as a Core Engineering Activity 4. MDO as a Toolbox for Familiar Challenges

28. Questions? Thanks, Rob McDonald