EPN-V2

MECH4301 Computational Fluid Dynamics Course description

Course name in Norwegian
Numerisk strømningsberegning
Study programme
Master’s Programme in Mechanical Engineering
Weight
10.0 ECTS
Year of study
2025/2026
Curriculum
FALL 2025
Schedule
Course history

Introduction

This course covers some fundamental concepts of Computational Fluid Dynamics and their practical use in computer simulations. Students learn about different challenges associated with compressible and incompressible flows, different grid structures and the numerical modelling of turbulence. The theoretical understanding is put to practical use through programming exercises with computer tools such as OpenFoam and python.

Recommended preliminary courses

The course is an introduction to how theories on evolution and behaviour analysis can explain the behaviour of animals and humans. The course covers key topics in modern evolutionary biology, behavioural ecology and selection in relation to consequences. This includes an introduction to topics from biology, evolutionary psychology, anthropology and behaviour analysis views on selection and culture

Learning outcomes

Knowledge:

The candidate

  • can derive the governing equations and explain the standard mathematical classifications of fluid flow
  • can formulate fluid flow problems in terms of Partial Differential Equations
  • can explain the difference between Direct Numerical Simulation and averaged turbulence models in CFD
  • can explain the difference between staggered and collocated grids for a CFD meshing structure.

Skills:

The candidate

  • can solve standard fluid flow problems by applying CFD tools, such as OpenFOAM
  • can implement Finite Volume solution algorithms in Python
  • can apply von Neumann and TVD analysis to derive precise stability bounds on numerical methods for partial differential equations
  • can design suitable mesh structures for CFD analysis tailored to a given fluid flow problem.

General competence:

The candidate

  • can design and perform CFD simulations for common industrial problems
  • is capable of critically evaluating the results of CFD analyses and identifying potential sources of errors and inaccuracies
  • can communicate their work and can master language and terminology of the CFD field.

Teaching and learning methods

  • Lectures
  • Problem solving sessions
  • Computer laboratory sessions in
    • Applied CFD using high-level tools such as OpenFOAM.
    • Scientific programming in python.

Course requirements

None

Assessment

Individual oral examination, 30 minutes per student.

Permitted exam materials and equipment

None

Grading scale

Grade scale A-F.

Examiners

Two internal examiners. External examiner is used periodically.

Course contact person

Tore Flåtten