Course
UndergraduateSemester
ElectivesSubject Code
AE496Subject Title
Multidisciplinary Design OptimizationSyllabus
Multidisciplinary Design Optimization (MDO): Need and importance – Coupled systems – Analyser vs. evaluator – Single vs. bi-level optimisation – Nested vs. simultaneous analysis/design – MDO architectures – Concurrent subspace, collaborative optimisation and BLISS – Sensitivity analysis – AD (forward and reverse mode) – Complex variable and hyperdual numbers – Gradient and Hessian – Uncertainty quantification – Moment methods – PDF and CDF – Uncertainty propagation – Monte Carlo methods – Surrogate modelling – Design of experiments – Robust, reliability based and multi-point optimisation formulations.
Text Books
Same as Reference
References
1. Keane, A. J. and Nair, P. B., Computational Approaches for Aerospace Design: The Pursuit of Excellence, Wiley (2005).
2. Khuri, A. I. and Cornell, J. A., Response Surfaces: Design and Analyses, 2nd ed., Marcel Dekker (1996).
3. Montgomery, D. C., Design and Analysis of Experiments, 8th ed., John Wiley (2012).
4. Griewank, A. and Walther, A., Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, 2nd ed., SIAM (2008).
5. Forrester, A., Sobester, A., and Keane, A., Engineering Design via Surrogate Modelling: A Practical Guide, Wiley (2008).
Course Outcomes (COs):
CO1: Convert complex design requirements to an optimisation problem statement.
CO2: Apply and analyse gradient and non-gradient optimisation algorithms for problem solution.
CO3: Create surrogate models to replace expensive analysis modules.
CO4: Solve optimisation problems under uncertainty.
CO5: Solve multi-objective optimisation problems.