Module manager: Dr Amirul Khan
Email: a.khan@leeds.ac.uk
Taught: Semester 1 (Sep to Jan), Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
BEng degree with at least 2:1 honours and must have studied Finite Element Analysis module in undergraduate degree.
| CIVE5024M | Design Optimisation - MEng |
This module is not approved as an Elective
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.
1. 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 design optimisation and informed by an awareness of new developments and the wider context of engineering) (AHEP4 Learning Outcome M1).
2. 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 (as appropriate), and using engineering judgment to work with information that may be uncertain or incomplete, discussing the limitations of the techniques employed) (AHEP4 Learning Outcome M2).
3. Select and apply appropriate computational and analytical techniques to model complex problems in design optimisation, discussing the limitations of the techniques employed (AHEP4 Learning Outcome M3).
This module contributes to AHEP 4 Learning Outcomes M1, M2 and M3.
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).
Academic
a. The ability to plan time, prioritise tasks and organise academic and personal commitments effectively
b. An ability to extract and evaluate pertinent data and to apply engineering analysis techniques in the solution of optimisation problems.
Digital
c. The ability to find, evaluate, organise and share information across a variety of formats, ensuring the reliability and integrity both of the sources used.
d. The ability to use digital technology and techniques to create digital items and the willingness to engage with new practices and perspectives to solve problems, make decisions and answer questions.
Enterprise
e. The ability to search for, evaluate and use appropriate and relevant information sources to help strengthen the quality of academic work and independent research.
Sustainability Skills
f. Recognises and understands relationships; analyses complex systems; considers how systems are embedded within different domains and scales; deals with uncertainty; uses analytical thinking
g. Understands and evaluates multiple outcomes; their own visions for the future; applies the precautionary principle; assesses the consequences of actions; deals with risks and changes; uses scenario planning
Work ready
h. The ability to prioritise, work efficiently and productively and to manage your time well in order to meet deadlines.
i. The ability to take a logical approach to solving problems; resolving issues by tackling from different angles, using numerical skills. The ability to understand, interpret, analyse and manipulate numerical data.
j. The ability to take a logical approach to solving problems; resolving issues by tackling from different angles. The ability to understand, interpret, analyse and manipulate numerical data.
k. The ability to gather information from a range of sources, analyse, and interpret data to aid understanding and anticipate problems. To use reasoning and judgement to identify needs, make decisions, solve problems, and respond with actions
1. Introduction to the course, motivation for the systematic design improvement. Criteria of design quality. Formulation of an optimization 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 optimization 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 optimization techniques. Local and global one-dimensional optimization. Unconstrained multi-parameter optimization techniques. Penalty methods. Linear programming. General constrained optimization 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 optimization. Case studies and applications to practical problems.
7. Other applications of optimization 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 optimization, robust design, reliability optimization.
9. Real-life examples of design optimization. Review of availability of commercial software.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 24 |
| Tutorial | 11 | 1 | 9 |
| Private study hours | 117 | ||
| Total Contact hours | 33 | ||
| Total hours (100hr per 10 credits) | 150 | ||
22 hours revision for lectures, 22 hours (2 hours per tutorial) for preparation & revision for tutorials, 25 hours for preparation for assessment, 50 hours for preparation for examination.
Topics of directed independent study identified by lecturer to support learning. Such topics will include applications of design optimisation techniques to structural design, study of specialist technical reports and relevant journal papers (e.g. Structural and Multidisciplinary Optimization, ICE Proceedings, ASCE Journal of Structural Engineering, AIAA Journal).
Non-assessed tutorial questions covering structural topology and shape optimisation and approximation-based optimisation.
Progress will be monitored in the tutorial periods.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Design Problems | 30 |
| Assignment | Design Problems | 30 |
| Total percentage (Assessment Coursework) | 60 | |
The resit will be by online time-limited assessment only.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 3.0 Hrs 0 Mins | 70 |
| Total percentage (Assessment Exams) | 70 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 01/05/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team