2023/24 Taught Postgraduate Module Catalogue

MECH5790M Design Optimisation

15 creditsClass Size: 100

Module manager: Dr Zinedine Khatir
Email: Z.Khatir@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2023/24

Pre-requisite qualifications

Must have studied Structural Analysis module in undergraduate degree.

This module is not approved as an Elective

Module summary

This module will introduce students to formal design optimisation methods that can be used to improve engineering design subject to practical constraints. Students will learn how to formulate optimisation problems rigorously and be able to classify problems into unconstrained and constrained and linear and nonlinear problems with continuous and/or integer design variables. Students will become familiar with a range of optimisation methods for single and multi-variable optimisation methods and be able to implement these algorithmically, using appropriate software tools, to solve practical optimisation problems. Students will learn the differences between deterministic and stochastic optimisation methods and be able to choose the most appropriate method to specific optimisation problems.

Objectives

On completion of this module students should acquire a comprehensive understanding of the scientific principles of design optimisation and ability to arrive at an improved design for an engineering system that satisfies given requirements.

Having completed the module students should be able to: formulate a design optimisation problem that is treated as a systematic design improvement; select, compare, contrast, understand limitations and apply appropriate methods and computer software for solving such problems; critically interpret the obtained results. The emphasis is made on the application of modern optimisation techniques linked to the numerical methods of analysis of engineering systems.

Learning outcomes
1. A comprehensive knowledge and understanding of the scientific principles and methodology necessary to underpin their education in their engineering discipline, and an understanding and know-how of the scientific principles of related disciplines, to enable appreciation of the scientific and engineering context, and to support their understanding of relevant historical, current and future developments and technologies (SM1m)
2. Knowledge and understanding of mathematical and statistical methods necessary to underpin their education in their engineering discipline and to enable them to apply a range of mathematical and statistical methods, tools and notations proficiently and critically in the analysis and solution of engineering problems (SM2m)
3. A comprehensive knowledge and understanding of mathematical and computational models relevant to the engineering discipline, and an appreciation of their limitations (SM5m)
4. Ability to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques (EA2)

Skills outcomes
- General learning outcomes (UK-SPEC): the ability to apply new concepts and methods (in the context of design optimisation as systematic design improvement);

- Specific learning outcomes (UK-SPEC): Underpinning Science & Mathematics (Knowledge and understanding of mathematical methods as applied to design optimisation; an appreciation of the limits of such methods);

- Engineering Analysis (the ability to apply mathematical methods to solve problems in design optimisation; the ability to assess the advantages and limitations of particular cases);

- Design (broader knowledge and understanding of design improvement aims and techniques for a chosen engineering system);

- Engineering Practice (a thorough understanding of how simulation-based optimisation techniques influence design methods and engineering practice).

Syllabus

1. Introduction to the course, motivation for the systematic design improvement. Criteria of design quality. Formulation of an optimisation problem as a nonlinear mathematical programming problem. Choice of design variables and the objective function. Formulation of typical constraints on the system's behaviour.
2. Classification of design optimisation problems. Constrained and unconstrained problems. Global and local optima. Kuhn-Tucker optimality conditions. Multi-objective problems. Pareto optimum solutions. Basic approaches to the formulation of a combined criterion.
3. Numerical optimisation techniques. Local and global one-dimensional optimisation. Unconstrained multi-parameter optimisation techniques. Penalty methods. Linear programming. General constrained optimisation techniques. Random search, genetic algorithms.
4. Approximation techniques. Local, mid-range and global approximations, used in conjunction with a high fidelity numerical analysis. Design of Experiments (DoE) techniques for sampling in approximation building. Case studies and applications to practical problems.
5. Design sensitivity analysis based on the finite element modelling of structural behaviour. Analytical, semi-analytical, and finite difference techniques.
6. The relationships between fully-stressed and minimum weight structures. Topology, shape and sizing optimisation. Case studies and applications to practical problems.
7. Other applications of optimisation techniques in engineering. Structural identification problems: finite element model identification, material parameter identification, structural damage recognition.
8. Effect of stochastic inputs on an engineering system, stochastic analysis optimisation, robust design, reliability optimisation.
9. Real-life examples of design optimisation. Review of availability of commercial software.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Seminar	11	1.00	11.00
Private study hours			117.00
Total Contact hours			33.00
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Progress will be monitored in the tutorial periods.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Design Problems	30.00
Total percentage (Assessment Coursework)		30.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc) (S2)	2 hr	70.00
Total percentage (Assessment Exams)		70.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 18/01/2024

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