Course syllabus

Simulation Tools

FMNN05, 7,5 credits, A (Second Cycle)

Valid for: 2013/14
Decided by: Education Board B
Date of Decision: 2013-04-10

General Information

Elective for: D4, F4, F4-bs, Pi4, Pi4-bs, Pi4-pv
Language of instruction: The course will be given in English on demand


Simulation techniques is a field which merges experience in modelling with knowledge in Scientific Computing and programming skills. The aim of the course is to give students in the last stage of their university studies the possibility to experience, in a working team, industrially relevant computational problems in connection with modelling of complex mechanical systems. The participants meet numerical methods on different levels in industrial simulation tools. In particular ordinary differential equations with and without algebraic constraints and methods for large systems of nonlinear equations will form the numerical backbone of the course.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

- be familiar with the software’s purpose.

-be familiar with commonly used numerical methods.

- be able to evaluate simulation results.

Competences and skills
For a passing grade the student must

- independently be able to apply and evaluate numerical methods within industrial software tools.

Judgement and approach
For a passing grade the student must

- be able to see structural parallels between various engineering problems.

- write an algorithmically well structured report in suitable terminology on the mathematical methods applied in industrial simulation tools.


Theoretical part: Numerical treatment of ordinary differential equations with discontinuities and/or algebraic constraints. Variants of different modelling techniques, variational integrators and other methods suitable for modelling. Introduction to a modelling language.

Practical part: numerical experiments with computational tools within commercial and industrial software packages, e.g. Dymola. Similar experiments with selfproduced code in Python/SciPy.

Examination details

Grading scale: UG
Assessment: A report in several parts.


Required prior knowledge: FMNN10 Numerical Methods for Differential Equations.
The number of participants is limited to: 25
Selection: Interview or special test.
The course overlaps following course/s: FMN145

Reading list

Contact and other information

Director of studies: Anders Holst,
Course coordinator: Claus Führer,
Course homepage: