Course content

The course includes a general introduction to modelling and computer simulation as tools in materials science and engineering, "advanced" use of spread sheets (Excel), and basic skills in programming and program development. Some important types of problems that will be treated are: Analysis and representation of experimental data, numerical integration and derivation, root finding and numerical methods to solve differential equations, random numbers and Monte-Carlo methods. The topics will be presented by means of relevant examples related to modelling and simulation of proceses and reactions in materials science and materials engineering. The examples are amongst others related to casting and solidification, heat conduction, recrystallization and grain growth, diffusion, melt treatment and thermomechanical treatment and transformation kinetics (C-curves).

Learning outcome

The intention of the course is to give skills and training in the use of computers, standard computer tools/software and programming to solve problems in materials science and engineering. The computer exercises should make the students able to to solve relevant problems by advanced use of spreadsheets (Excel) and programming in Matlab.
After completed the course the students should have competence and skills to perform smoothing and numerical differentiation by least squares procedures, numerical integration and model fitting (Solver) in a spread sheet (Excel), including graphical representation of the the results. Moreover, the students should have aquired competence and skills with respects to principles and algorithms to make effective and user-friendly computer codes to do numerical calculations and simulations in Matlab (or equivalent) for the topics listed above.
This includes repetition and loops, program structuring, algorithms for numerical derivation and integration, iterative techniques for solving equations numerically, numerical methods to solve ordinary and partial differential equations (incl. Euler's method, Runge-Kutta methods, finite-difference methods), random numbers and Monte Carlo Methods, different methods to input data and graphical representation of data and simulation results.

Learning methods and activities

The course and the teaching will be centered around 12-14 relevant problems/exercises. The problems/topic of the exercises and knowledge and skills required to solve the problems will be presented in the lectures. The exercises will take place in a computer laboratory (PC-lab), and will mainly be based on the use of spread sheet (Excel) and Matlab.

Compulsory assignments

• Computer exercises

Recommended previous knowledge

The course Information technology, Introduction or courses that give similar insight and knowledge about computers and use of basic computer tools. Basic knowledge and skills related to numerical methods.

Course materials

To be given at start of semester.

Credit reductions