Dr. Achille Messac Multidisciplinary Design and Optimization Laboratory

1. Multidisciplinary Design Optimization as a Powerful Risk Mitigation Tool Dr. Achille Messac Multidisciplinary Design and Optimization Laboratory Mechanical, and AeronauticalEngineering Department Rensselaer Polytechnic Institute

2. Presentation Outline 1- Multidisciplinary Design Optimization What is it? A bit of history… 2- Aerospace and other MDO Applications 3- Multiobjective reality -- Physical Programming 4- Venturing into the nondeterministic environment of RISK – The role of MDO 5- Optimization in Conceptual Design 6- Concluding Remark

3. From Early Timid Optimization Applications to MDO From 50’s to 70’s, engineering optimization is - Primarily Uni-disciplinary Aerospace >> Structure – min. mass - Computationally Challenged - Of Timid Problem Scope - Heavily reliant on the defective MO method -- weighted sum

4. From Early Timid Optimization Applications to MDO -- Cont. In early to mid eighties - Computers become faster, and start decreasing in cost - OptimalControl grows in popularity - LargeSpaceStructures research grows - ConcurrentEngineering becomes a cherished word - Bi-disciplinary optimization: Control-Structure (Messac, Hale) - NASA, Air Force, and JPL initiate MDO, CSIprograms - Government financial resources relatively abundant (human, computer – however slow) - First Pre-MDOconference is held at Langley (Sobieski – 1984)

5. From Early Timid Optimization Applications to MDO -- Cont. Mid 80’s to Mid 90’s - Computers are much faster and cheaper - AIAAMDOTCisborn(Transition from Bi- to Multi-disciplinary) - MDO is still largelyDeterministic - MDO is still implemented inlaterstagesofdesignNot at Conceptual Design Stage - MDO is still largely addressingtraditionaldisciplinesAero, Structure, Control – NeglectingCost, Management, etc. While… - MDO’s popularity and use grows many-fold (e.g. GE heating element) - MDO research enjoys strong internationalgrowth - MA&O Conference grows more and more successful

6. From Early Timid Optimization Applications to MDO -- Cont. • Mid 90’s to Present • MDO is still implemented inlater stages of designNot at ConceptualDesign Stage -- New Conceptual Design optimization method emerged (s-Pareto based Conceptual Design – Mattson-Messac) • Optimization is still often used using the defective weighted sum paradigm • - Computer cost and speedare dramatically reduced • - MDO grows within government AND industry • MDO begins to addressnon-traditional disciplines Cost, Management, Manufacturability, Profit, Safety, Risk, etc. • - MDO becomes more appropriately treated as Multiobjective • - MA&O Conference continues to prosper: 2002 Atlanta; Sept. 2004 Near Saratoga Springs NY

7. Multidisciplinary Design Optimization (MDO) Performance Conventional Feasible Trades Design Space Suboptimal Design MDO Search Discipline 2 Optimum Multidisciplinary Aero Loads Optimal Design Structures CFD Deformations Discipline 1 Str. Weight Aero Loads Optimum Mach Number TOGW Design space discipline 1 Performance Design space discipline 2 Design Variables • Effective Integration of Individual Disciplines/Subsystems to Capture the Interactions • Novel Solution Procedures to Enable Improved System Solutions: • - Account for Interdisciplinary Couplings & Integrated Product Team (IPT) settings. • MDO = { Design Optimization, Design Exploration, Cross-Attribute Optimization, • InterDisciplinary Optimization, System Synthesis }

8. Ford-SGI - Vehicle System MDO Project HPC/MDO for NVH & Safety on the Origin 3800 Minimize:Vehicle Weight Subject to: NVH design targets Frequency Bending Displacement Torsion Displacement Frontal crash design targets Dummy HIC Dummy Chest G Probability of severe injury Roof crush design targets Maximum resistant force 50% Frontal Offset crash design targets Intrusion Side Impact design targets Displacements Viscous Criterion NVH Everything influences everything else! Frontal Crash Roof Crush Safety Side Impact Offset Crash

9. In Vehicle Everything Couples to Structure Controls Aerodynamics Electrical system Fuel system AIRFRAME STRUCTURE Hydraulics Avionics • Structural optimization • is at MDO roots Propulsion Landing gear

10. Car Body Optimization for Noise, Vibration, Harshness, and Crash • Vehicle Roof Crush is a federally • mandated requirement to enhance • passenger safety during a rollover event. • NVH must be constrained for passengers comfort Courtesy: Ford Motor Co./R-J. Yang • Modified NVH Model with a Square Ram to Perform Roof • Crush as specified by regulations • Finite Element Model of 390,000+ dof, 6000+ boundary • conditions; • 20 design variables • Optimization executed using Response Surface approximations to crash analysis and sensitivity-based approximations to NVH • Implemented on a computer with 256 processors • Single processor computer would need 257 days to do this optimization • It was condensed to 1 day on the multiprocessor machine.

11. 12 Air Borne Laser System Design • System Level Design • Boeing • CDR 25-27 April • Beam Control System • Turret Assembly • Large Optics • Four Axis gimbals • Transfer optics • Beam Transfer Assembly • Sensor Suite • Active Mirrors • Illuminators • Electronics • Software/Processors • 747F Aircraft - • Boeing • CDR 29 Feb - 3 Mar • Chemical Oxygen Iodine • Laser (COIL) • TRW • 21-23 March • BMC4I • Boeing • 8-10 March

12. Supersonic Business Jet Test Case

13. Examples of applications that will need all that Configuration “B” Configuration “A” • MDO necessary to protect very small payload margin • Massively computational problem • Candidate MDO method : design data bases for subsystems and disciplines precomputed off-line using multiprocessor computing Courtesy: NASA LRC/Troutman

14. Prevailing Practice • Obtaining an effective objective function is difficult in practice. • Weighted-Sum (WS) method for optimization has its inherent significant drawbacks. It is incapable of capturing a large class of potentially desirable Pareto solutions. It is also the most popular! • Compromise Programming (CP) method can be used to generate a complete set of efficient solutions, but its reliance on meaningless weights is a serious problem. Pareto ... • Etc.

15. Optimal Solution UsingWeighted-Sum Obj. Fun. Line

16. Pareto Solutions Using Weighted-Sum

17. Weighted-Sum in Non-Convex Pareto Frontiers Obj. Fun. Line

18. Weighted Compromise Programming for Non-Convex Pareto Frontiers Obj. Fun. Line

19. Physical Programming (cont.) Quantify Preference for Each Design Metric Ex: Mass of Beam Highly Desirable < 250 (kg) Desirable 250 - 275 Tolerable 275 - 300 Undesirable 300 - 325 Highly Undesirable 325 - 350 Unacceptable > 350

20. Expressing Designer Preference During the Optimization Process

21. Behavior of the PP Class Functionfor Change in Preferences

22. Numerical Example 1 (of 2) Minimize Behaviors of different objective functions for a convex frontier Subject to Results WS CP PP

23. Example 2 Behaviors of different objective functions for a concave frontier Results WS CP PP

24. The Reality of Uncertainty and Risk 1- Non-Deterministic Forum -- SDM 2- Traditional Factor-of-Safety approach is rapidly becoming an anachronistic relic of the past 3- Development of probabilistic design optimization methods 4- New MDO methods emerge to model risk and uncertainty 5- Profit as a design metric gains acceptance 6- Growing recognition that optimization in Conceptual Design is a necessity 7- TC has moves to Engineering & Technology Management Group to explore expanded collaboration

25. INJECTING MDO INTO INDUSTRIAL PRACTICE: A Challenge? • • Not Primarily a Technical Problem • Critical Issues are: EDUCATION, MANAGEMENT, • AND ORGANIZATION “CULTURE”

26. Concluding Remarks • The picture is somewhat muddied! Much of good news; yet much to be desired. • MDO’s power is evident. • Yet its strongest influence can be felt in this decade if -- its application in a holistic setting comes to pass, and -- key methodological developments take place in collaboration with non-traditional participants • MDO is a unifying indispensable glue, if we are to effectively design the ultra-safe, profitable, and effective design of the future • For more information: www.rpi.edu/~messac