Module information on this degree can be found below, separated by year of study.

The module information below applies for the current academic year. The academic year runs from August to July; the 'current year' switches over at the end of July.

Students select optional courses subject to rules specified in the Mechanical Engineering Student Handbook,  for example at most three Design and Business courses. Please note that numbers are limited on some optional courses and selection criteria will apply.

Mathematics and Computing 2

Module aims

To continue the development of key mathematical and computational skills relevant to the wider mechanical engineering programme. Topics include the analytical solution of partial differential equations, basic numerical methods (interpolation, differentiation, integration), discrete methods for the solution of PDEs (finite difference, finite volume) and the iterative solution of systems of equations. For all of these topics, practical implementation through programming is studied. These skills and techniques are in support of ME2, ME3 and ME4 modules.

ECTS units: 10

Learning outcomes

To be able to solve some simple linear 2D partial differential equations using the method of separation of variables

To understand and employ interpolation in the representation of fields and to write python code to interpolate fields on triangulated 2D domains

To be able to develop finite difference schemes for differential forms of PDE

To be able to develop finite volume schemes for integral forms of PDE

To be able to write python code to solve some simple 2D PDEs using finite difference and finte volume methods

To understand the concepts of covergence and consistency in the context of numerical methods

To understand and be able to write python code for the direct and iterative solution of systems of linear equations

To understand fourier transforms and be able to apply discrete fourier transforms to data within python

Module syllabus

Root finding

Fourier transforms 

Analytical Solution of PDEs


Numerical differentiation

Numerical integration

Finite difference methods

Finite volume methods

Iterative solutions of systems of linear equations



Teaching methods

Allocation of study hours Hours
Lectures 44
Group teaching 22
Lab/ practical 40
Other scheduled  
Independent study 144
Total hours 250
ECTS ratio 25


Assessment type Assessment description Weighting Grading method Pass mark Must pass?
Examination 3 hour exam (6 questions) 60% Numeric 40% Y
Coursework Autumn term programming test 18% Numeric 40% N
Coursework Spring term programming test 18% Numeric 40% N
Examination Progress test 4% Numeric 40% N

Reading list

Module leaders

Dr Nicolas Cinosi
Dr Richard Jan van Arkel