2024/25 Taught Postgraduate Module Catalogue

MECH5790M Design Optimisation

15 creditsClass Size: 120

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

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

Pre-requisite qualifications

Undergraduate degree in Physical or Engineering Sciences

Programming such as MATLAB

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 different types of optimisation problem. They will be able to use a variety of optimisation methods for simple engineering optimisation problems.

Objectives

On completion of this module students will 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 will 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
On successful completion of the module students will have demonstrated the following learning outcomes :

1. Have a good overview of different optimisation methods used for a variety of engineering design problems.
2. Know the importance characteristics of each optimisation methods and in particular its limitations
3. Be able to translate simple optimisation algorithms into computer code (e.g. matlab, python)
4. Have learned how to investigate performance of an optimisation algorithm by solving practical examples.

Upon successful completion of this module the following Engineering Council Accreditation of Higher Education Programmes (AHEP) learning outcome descriptors (fourth edition) are satisfied:
5. Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems. Much of the knowledge will be at the forefront of the particular subject of study and informed by a critical awareness of new developments and the wider context of engineering. (M1)
6. Formulate and analyse complex problems to reach substantiated conclusions. This will involve evaluating available data using first principles of mathematics, statistics, natural science and engineering principles, and using engineering judgment to work with information that may be uncertain or incomplete, discussing the limitations of the techniques employed. (M2)
7. Select and apply appropriate computational and analytical techniques to model complex problems, discussing the limitations of the techniques employed. (M3)


Skills Learning Outcomes

On successful completion of the module students will have demonstrated the following skills:

problem solving and analytical, Programming

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. Real-life examples of design optimisation.

Methods of Assessment

We are currently refreshing our modules to make sure students have the best possible experience. Full assessment details for this module are not available before the start of the academic year, at which time details of the assessment(s) will be provided.

Assessment for this module will consist of:

1 x Assignment
1 x Exam

Teaching methods

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

Opportunities for Formative Feedback

Students will attend 11 1-hour computer practical sessions where formative feedback on progress and problems in understanding can be provided.

Reading list

The reading list is available from the Library website

Last updated: 04/09/2024 09:47:43

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