Unit information: Advanced Techniques in Multi-Disciplinary Design in 2010/11

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.

Teaching Information

Lectures.

Assessment Information

20% in class test, 30% reflective account of case studies, and 50% major project