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Unit information: Advanced Techniques in Multi-Disciplinary Design in 2018/19

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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 Dr. Poole
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department Department of Aerospace Engineering
Faculty Faculty of Engineering

Description

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 details

Lectures.

Assessment Details

30% Test

70% 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

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