Module code: ENGM259

Module provider

Mechanical Engineering Sciences

Module Leader

CIROVIC S Dr (Mech Eng Sci)

Number of Credits


ECT Credits



FHEQ Level 7

JACs code


Module cap (Maximum number of students)


Module Availability

Semester 2

Overall student workload

Workshop Hours: 4

Independent Study Hours: 105

Lecture Hours: 17

Laboratory Hours: 33

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 70%
Oral exam or presentation ORAL PRESENTATION 30%

Alternative Assessment

Not applicable.

Prerequisites / Co-requisites

Normal entry requirements for the Biomedical Engineering MSc degree programme.

Module overview

The module introduces the student to the application of modern computational methods in biomedical research. Within the context of real-life problems encountered in biomedical engineering practice, the student will be introduced to and provided with practical experience in using:

Finite element software (ANSYS, LS-DYNA).
Software for reconstruction of anatomically realistic geometries from clinical images (Simpleware).
High-end programming languages for a wide range of data analysis and mathematical simulation tasks (Matlab).

Module aims

A comprehensive understanding of  computer techniques  widely used in biomedical engineering research as well as the current state of the art of their application in the field.

A set of practical skills in using advanced software for design and analysis in biomedical engineering, which can also be applied to other engineering fields.

A broad knowledge base on which to independently build further expertise in using advanced engineering software.

Learning outcomes

Attributes Developed
Demonstrate an awareness of the main computer/analytical techniques currently in use in biomedical engineering and be able to critically evaluate their limitations on a case-to-case basis K
Practically implement advanced finite element and programming techniques to tackle a wide range of problems in bioengineering. CPT
Deal with complex software problems in bioengineering, making judgements to identify possible solutions. CPT
Analyse results and present them effectively in written and oral form. CT
Identify further modelling skills necessary to tackle novel and unfamiliar problems and be able to acquire these skills independently or with minimum guidance. CT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Reconstruction of anatomically accurate geometries

Introduction to the Simpleware software
Strategies for extracting geometries from stacks of MRI and CT scan images: manual painting, thresholding, flood-filling.
Filtering strategies for smoothing and noise removal.
Exporting of geometries in CAD file formats.

Finite element modelling

Introduction to the finite element method. Nodes, elements, types of elements, meshing strategies, level of geometric approximation: 3D solids, shells, beams/trusses.

Static analysis (ANSYS APDL)

Basic concepts: pre-processing, constraints, loads, material definition, post-processing of the results.
Advanced concepts: Geometry construction from CAD models, advanced material models, large deformations and geometric non-linearity.
Biomedical applications: Bone implants, soft-tissue modelling.

Explicit solvers for transient dynamic analysis (ANSYS Explicit, LS-DYNA)

Time step control and meshing strategies for explicit methods.
Initial and boundary conditions.
Modelling of contacts and constraints between objects.
Multi-body systems of rigid objects, springs, dampers, cables, seatbelts, force generators.
Post-processing for dynamic problems.
Biomedical applications: impact and acceleration trauma, pressure-wave therapy, modelling of active muscle tissue.

Programming in Matlab

Introduction to Matlab: basic commands, loops and logical statements; Mathematical functions, linear and vector algebra.
Introduction to statistics for healthcare; basic statistics, binomial and Poisson distributions, concepts of statistical significance and the student-T test
Statistical analysis of (experimental) data and its implementation in Matlab.
An introduction to linear and non-linear analysis of time series.
Numerical solution for systems of ordinary differential equations: lumped-parameter modeils of vascular and oher physiological systems.


Methods of Teaching / Learning

The learning and teaching strategy is designed to: Introduce the student to the use of advanced computer methods in various fields of biomedical engineering. The emphasis is on hands-on sessions with guidance from the lecturers, and on the individual learning through the individual project.

The module is delivered intensively over a two week period.: The learning and teaching methods include:

15 hours of lectures
30 hours of lab sessions/demonstrations


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate that they have developed a sound understanding of the application of engineering software for the solution of problems in biomedical engineering, as well as the practical skills needed to implement that software. The students will also have the opportunity to demonstrate their competence in analysing and presenting results.

Thus, the summative assessment for this module consists of:

·         Individual project [ Learning outcomes 1, 2,3,4,5 ]                             (80 hours)                  {70%}

·         Presentation     [ Learning outcomes 1,4 ]                                        (25 hours)                 {30%}

Formative assessment and feedback

Formative verbal feedback is given in computer lab sessions, and during individual project.
Written feedback is given for the individual project report.


Reading list


Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.