TMT4210 - Material and Process Modelling


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Semester test 30/100 A
work 70/100

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 processes 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

After the course is finished the students should be able to:
- Identify and describe key elements of mathematical modelling of processes and reactions in materials science and engineering, and shortly be able to account for why and in which connectins mathematical/numerical modelling is useful.
- Apply adequate algorithms for smoothing, numerical derivation and integration in work sheets (Excel), in order to process and analyse experimental data, including graphical representation of results.
- Account for the principles of model fitting/regression and be able to use ”trendline” and Solver in Excel to perform calculations to determine relevant model parameters.
- Analyse and reformulate mathematical equations and simple models into a form which is suitable for numerical solutions in a computer.
- Make use of basic principles and algorithms to make effective and userfriendly computer codes for numerical calculations and simulations, including use of repetitive constrol structures (loops), conditional control structures (if, while) and functions, as well as simple/useful methods for input (menues)/output.
- Implement and perform relevant calculations which involve algorithms for numerical derivation and integration, iterative techniques for solving equations (root finding), numerical solutions to ordinary and partial differential equations (incl. Eulers method, Runge-Kutta methods and Finite Difference methods).
- Make use of random numbers and Monte Carlo methods to solve deterministic problems and specifically to perform evaluations of definite integrals.
- Analyse and discuss accuracy in relevant numerical calculations, and be able to choose relevant model parameters to achieve required accuracy.

Learning methods and activities

The course and the teaching will be centered around 11-12 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 Python/Matlab.
Total amont of work load for the Whole semester (incl. independent home work) is ~200 hrs.

Further on evaluation

Final grade in the course is based on portfolio assessment. The portfolio includes the obligatory computer problems (70%) and an obligatory term test (30%). The evaluation of the different parts is given in %points while the final grade for the whole folder is given by a letter grade.
If a student has to take the course over again, all evaluations in the course has to be repeated.

Required previous knowledge

About 2/3 of the course (mandatory computer exercises) is based on programming in Python/Matlab. The course therefore requires basic knowledge of Python/Matlab and some experience with programming, or equivalent previous knowledge to be evaluated by the course responsible to be satisfactory

Course materials

To be given at start of semester.

Credit reductions

Course code Reduction From To
SIK5019 7.5
More on the course



Version: 1
Credits:  7.5 SP
Study level: Third-year courses, level III


Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 2
Lab hours: 4
No.of specialization hours: 6

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Materials Science and Engineering
  • Technological subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Materials Science and Engineering



Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Spring ORD Semester test 30/100 A
Room Building Number of candidates
Spring ORD work 70/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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