Applied Computational Fluid Dynamics for Industrial Processes (MSK600)

This course will teach you the practical application of computational fluid dynamics to solve industrial problems. That is, problems involving turbulent flow and complex geometries.

Computational fluid dynamics (CFD) lets us solve the governing equations for fluid dynamics for complex engineering problems. CFD is today used in a wide range of industries, some examples are:

• air resistance for airplanes and cars
• wind and wave loads on buildings and marine structures
• heat- and mass transfer in chemical processing plants
• consequence modelling of fires and explosions in the oil- and gas industry

These challenges require handling complex geometries and turbulent flows. For a robust numerical solution, one needs to understand how to create an unstructured grid and how turbulence can be modeled. The course is divided into the following modules:

• Turbulence modeling
• Generation of unstructured grids
• Aerodynamics
• Heat transfer
• Multiphase flow
• Advanced turbulence modeling
• Development of numerical solvers

The first two modules are mandatory, and in addition, two modules are chosen based on your interests.

Knowledge

The students shall

• know the most common models for turbulent flow
• know the basic criteria for mesh quality and how they affect simulation accuracy
• know relevant mathematical models within some of the following fields: Aero/hydrodynamics, heat transfer, multiphase flow

Skills

The students shall be able to

• perform simulations in the CFD software OpenFOAM; create simulation mesh, select initial- and boundary conditions, discretization schemes and solution methods and visualize the results
• compare simulations against analytical and experimental results
• implement mathematical models in OpenFOAM using C++ (if module on solver development is chosen)

General qualifications

The students shall be able to

• simplify practical problems to make them amenable for analysis with appropriate scientific methods
• visualize and present data from simulations in a scientific manner
• interpret results from simulations and evaluate accuracy and uncertainty
• collaborate in groups to carry out work

The teaching holds place in the last half of the semester

2 hours lectures per week

4 hours computer lab per week

8-12 hours self study