Optimal Design in Multidisciplinary Systems

In This Section

Synopsis:

When you are designing or evaluating a complicated engineering system such as an aircraft or a launch vehicle, can you effectively reconcile the multitude of conflicting requirements, interactions, and objectives? This course discusses the underlying challenges in such an environment, and introduces you to methods and tools that have been developed over the years.


You will be presented with a review of the state-of-the-art methods for disciplinary optimization that exploit the modern computer technology for applications with large numbers of variables, design limitations, and many objectives. You will learn how to evaluate sensitivity of the design to variables, initial requirements, and constraints, and how to select the best approach from many currently available.


From that disciplinary level foundation, the course will take you to system level applications where the primary problem is in harmonizing the local disciplinary requirements and design goals to attain the objectives required of the entire system, and where performance depends on the interactions and synergy of all its parts. In addition to imparting skills immediately applicable, the course will give you a perspective on emerging methods and development trends.

Key Topics:

  • Multidisciplinary design-components, challenges, and opportunities
  • Optimization methods and Sensitivity analysis
  • Decomposition in multidisciplinary design
  • Surrogate Modeling in Design
  • Soft computing methods in optimal design
  • Uncertainty in Multidisciplinary Design

Who Should Attend:

Design engineers and technical managers involved with preliminary or detailed design of aerospace, mechanical, and other multidisciplinary engineering systems will find this material applicable in their work environment. Advanced research students and research scholars in academia and in research laboratories will also benefit from the topics covered in this course. They would use this material as an entry point into possible areas of further research.

Course Information:

Type of Course: Instructor-Led Short Course
Course Level: Fundamentals/Intermediate


Course scheduling available in the following formats:


  • Course at Conference
  • On-site Course
  • Stand-alone/Public Course

Course Length: 2 days
AIAA CEU's available: yes

Outline

Course Outline:


I. Overview of MDO Methods
A. Analysis
B. Optimization
C. Strategies

II. Review of Numerical Methods of Optimization
A. Gradient and Non-Gradient Based Methods
B. Methods of Sensitivity Analysis

III. MDO Solutions Through Decomposition
A. Hierarchical & Non Hierarchical Methods
B. Coordination Strategies
C. Surrogate Modeling
D. Complex Systems

IV. Soft Computing Methods
A. Genetic Algorithms and Other Heuristic Methods for Optimization
B. Neural Networks/SVM for Function Approximation

V. Reliability Based Design
A. Problems Formulations in Presence of Uncertainty
B. Efficient Solution Strategies for Reliability Based Design

 

Materials

Course Materials:


Since course notes will not be distributed onsite, AIAA and your course instructor are highly recommending that you bring your computer with the course notes already downloaded to the course.

Once you have registered for the course, these course notes are available about two weeks prior to the course event, and are available to you in perpetuity.

 

Instructors

Course Instructors:


Joaquim R. R. A. Martins is an Associate Professor at the University of Michigan, where he heads the Multidisciplinary Design Optimization Laboratory (MDOlab) in the Department of Aerospace Engineering. His research involves the development and application of MDO methodologies to the design of aircraft configurations, with a focus on high-fidelity simulations that take advantage of high-performance parallel computing. Before joining the University of Michigan faculty in September 2009, he was an Associate Professor at the University of Toronto Institute for Aerospace Studies, where from 2002 he held a Tier II Canada Research Chair in Multidisciplinary Optimization. Prof. Martins received his undergraduate degree in Aeronautical Engineering from Imperial College, London, with a British Aerospace Award. He obtained both his M.Sc. and Ph.D. degrees from Stanford University, where he was awarded the Ballhaus prize for best thesis in the Department of Aeronautics and Astronautics. He was a keynote speaker at the International Forum on Aeroelasticity and Structural Dynamics in 2007 and the Aircraft Structural Design Conference in 2010. He has received the Best Paper Award in the AIAA Multidisciplinary Analysis and Optimization Conference thrice (2002, 2006, and 2012). He is a member of the AIAA MDO Technical Committee and was the technical co-chair for the 2008 AIAA Multidisciplinary Analysis and Optimization Conference. He is also an Associate Editor for Optimization and Engineering, AIAA Journal, and Structural and Multidisciplinary Optimization.

Jaroslaw Sobieski, Ph.D., holds three degrees: a B.S., M.S., and Ph.D. in Aeronautical Engineering from the Technical University of Warsaw. Before joining the staff of NASA Langley Research Center in 1979, he taught at the Technical University of Warsaw, St. Louis University, and George Washington University. Dr. Sobieski is currently a Senior Researcher at NASA Langley Research Center. He also teaches George Washington University students at the NASA site. Dr. Sobieski received the AIAA MDO Award in 1996, and a NASA Medal for Exceptional Engineering Achievement in 1988.