Modeling and Simulation of Dynamic Systems FM1006

Learning outcome

A candidate who has completed the course will have a learning outcome in the form of aquired knowledge, skills, and general competence, as described below.

The candidate will:

  • Understand the standard terminology within the topics of modelling and simulation
  • Understand the basic principles and ideas associated with modelling and simulation
  • Understand how to identify the crucial components of a system, needed for model design
  • Understand how to use relevant simplifying assumptions that give efficient models
  • Understand how to organize and write computer code for simulating dynamic models, and to make it readable and effective

The candidate will:

  • Be able to build dynamic models by use of modelling principles appropriate for the case
  • Be able to choose, and implement, the right computational methods for the given set of equations, writing structured and efficient computer code for the analysis
  • Be able to find, read and understand information in journals, books, internet, etc. relevant for the modeling/computational problem
  • Be able to draw the right conclusions from the analysis

The candidate will:

  • Be able to communicate/discuss with engineering colleagues problems related to modeling and simulation of dynamic systems, and to report work in writing

Course Description

  • Material, momentum and energy balances, stochiometric reactions and mass conservation, reaction kinetics, thermodynamic models, transport laws and coupled systems. The main principles of simplifying assumptions. Classification of model quantities. Analysis of developed models through linear approximation and solution of approximate model, time constants, and numerical solution/simulation using relevant tools. Applications of dynamic models. Although lumped and distributed modeling examples will be used to illustrate the topics, the core topic is systematic model development principles for process and energy systems.
  • Numerical techniques for computing integrals (Trapeze method, Simpsons method), solving non-linear algebraic equations (bisection method, fixed point iteration, Newton’s method, Secant method), solving ordinary and partial differential equations (Forward and Backward Euler methods, modified Euler, Crank-Nicolson’s method, Runge-Kutta 4th order, shooting method, finite difference methods), as well as basic programming skills required for the numerical analysis (in Matlab, Python, etc.)

Assessment Methods

All compulsory exercises and assignments must be graded as “passed” in order to participate in the final test at the end of the semester.

The grade of the course will be the grade received on the final test, i.e. the grade of the final test counts 100%. For this final test, it is necessary to pass in both of the topics Modeling and Simulation in order to get the single passing course grade (i.e., A – E). Those who fail in one or both topics will receive the single course grade F. Any re-take of the final test will include questions from the entire syllabus of the course.

Minor adjustments may occur during the academic year, subject to the decision of the Dean

Publisert av / forfatter Bernt Lie <>, last modified Unni Stamland Kaasin - 12/01/2013