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| Unit name |
Advanced Techniques in Multi-Disciplinary Design |
| Unit code |
AENGM2005 |
| Credit points |
10 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Professor. Richards |
| Open unit status |
Not open |
| Pre-requisites |
AENG30001 / AENG30011/AENGM2003/AENGM2013 Aerospace Vehicle Design and Systems Integration
|
| Co-requisites |
None
|
| School/department |
Department of Aerospace Engineering |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
This Unit instructs students in numerical optimisation methods and architectures for executing automated multi-disciplinary sizing of aerospace vehicles. The unit is segmented into four areas of instruction: 1. The design process and requirements for numerical synthesis; 2. Design search and optimisation methods; 3. Advanced multi-disciplinary sub-space simulation and architectures; 4. Design space sensitivities and synthesised solution robustness. A series of practical examples in conjunction with well-documented case studies will complement the presented material. The coursework emphasises a hands-on approach comprising assignments and a group project.
This module aims to provide a comprehensive introduction to the use of numerical search and optimisation tools for purposes of conducting advanced technical decision-making in aerospace design. Focus is placed on the four themes of optimisation associated with contemporary aerospace engineering, namely, Size (cross-section), Shape (boundary), Topology (form) and Behaviour (group). Upon successful completion of this unit the student will:
- Have a fundamental understanding of various optimisation techniques; be able to state the definitions of important terms, the properties of common methods; and, be able to implement and apply simple optimisation methods;
- Have a fundamental understanding of constrained, multi-objective optimisation problems including the conditions for optimality;
- Appreciate the requisite array of principles and practises when attempting to couple in automated design within the product development process;
- Acquire an ability to judiciously declare an automated design suite architecture with suitable objective functions, design variables, parameters and constraints; and,
- Be equipped to perform critical evaluations of optimisation results by scrutinising sensitivity analyses, and exploring cost functions, figures of merit.
Intended Learning Outcomes
Upon successful completion of the Unit the student will:
- have a fundamental understanding of various optimisation techniques; be able to state the definitions of important terms, the properties of common methods; and, be able to implement and apply simple optimisation methods;
- have a fundamental understanding of constrained, multi-objective optimisation problems including the conditions for optimality;
- appreciate the requisite array of principles and practises when attempting to couple in automated design within the product development process;
- acquire an ability to judiciously declare an automated design suite architecture with suitable objective functions, design variables, parameters and constraints;
- be equipped to perform critical evaluations of optimisation results by scrutinising sensitivity analyses, and exploring cost functions, figures of merit.
Teaching Information
Lectures.
Assessment Information
20% in class test, 30% reflective account of case studies, and 50% major project
Reading and References
- Keane, A.J. Computational Approaches for Aerospace Design, Wiley, 9780470855409. 2005
- Papalambros, P.Y. Principles of Optimal Design, CUP, 9780521622158. 2000
- Vanderplaats, G.N. Numerical Optimization Techniques for Engineering Design, McGraw-Hill, 978007066964. 1984
