|
|
| Topic | No of hrs | Instructor | References | |
|---|---|---|---|---|
| 1. | Introduction to Multidisciplinary Design Optimization (MDO), its need and importance, conceptual elements, examples. | 2 | KS | MDO-TC White Paper MDO-Intro |
| 2. | Coupled systems - analyzer vs evaluator, decomposition, system and discipline level design variables and constraints. | 2 | PMM | MDO-Algos-ppt MDO-Algos-pdf Simple-MDO-eg |
| 3. | Classification of MDO problem formulations - Single vs Bi-level | 1 | PMM | MDO-Algos-ppt MDO-Algos-pdf |
| 4. | Nested analysis and design vs simultaneous design and analysis | 1. | PMM | MDO-Algos-ppt MDO-Algos-pdf |
| 5. | System and discipline level functions and intercommunication. Single level MDO architechtures. | 1 | PMM | MDO-Algos.ppt MDO-Algos-pdf |
| 6. | Bi-level MDO architechtures. Collaborative optimization. | 3 | PMM | Bilevel-Algos-ppt MDO-Algos-pdf |
| 7. | Sensitivity Analysis Finite Difference Methods Analytical - direct and adjoint Complex variable approach Automatic Differentiation - ADIFOR/ADIC Global Sensitivity Equations |
6 | KS / AI / DG | sensitivity.ppt |
| 8. | Approximation Concepts and Surrogate Modelling | |||
| Introduction | 1 | KS / AI / DG | surrogate-prelim.ppt Uniform Random No Generation Normal Random No Generation |
|
| Least Square Estimation, Design Of Experiments (DOE) |
2 | KS / AI / DG | surrogate-lsq.ppt
readme for using demo programmes Demo of Least Square Estimation. While compiling on linux with g77 compiler use “–ffree-form” command line option. Requires input data from a file fit.inp. A sample fit.inp. Output will be in fit.out. Programme to generate data for the Least Square Estimation Programme to average the b estimates from large sets of experiments |
|
| Response Surface Method (RSM) | 3 | KS | surrogate-lsq.ppt | |
| Interpolating Vs Smoothening. Krigging | 1 | AI | ||
| Design and Analysis of Computer Experiments (DACE) | 2 | AI | dace.ppt sampling.ppt | |
| 9. | Bi-level Architectures (Contd.)
|
1 | PMM | Bilevel-Algos-ppt MDO-Algos-pdf |
| Multi-fidelity, Multi-point Approximations | 2 | PMM | ||
| 10. | Design under Uncertainties | 1 | DG | Robust Design-ppt |
| 11. | Software and IT issues in MDO MDO frameworks. |
3 | AI | |
| 12. | MDO@CASDE | 3 | PMM / KS / AI / DG | Everything you wanted to know about MDO@casde |