Catalog data:
Physical modelling of dynamic systems. Basic notions of modelling. Methodology, goals,
purpose of the model. System boundaries, subsystems, conservation laws. Causality, time
scales. Macroscopic versus microscopic models. Non-linear model behaviour. Spatially
distributed conservations laws, formulated in time and space variables.
Model approximation and reduction, based on time scales and time moments. Bilaterally
coupled physical subsystems. Internal structure of input-output models, described by
differential-algebraic equations. Reduction to state-space form, index problems.
Relationship with simulation tools. Model uncertainty description, sensitivity analysis.
Realization of input-output models, reduction via balancing and truncation. |
Courseyear:
3, 4
Semester: 0/4/0/0/0
Hours p/w: 2
Other hours: -
Assessment: Oral.
Assessm.period(s): By app.
(see academic calendar) |
Prerequisites:
wb2204, wb1220, wb1221. |
Follow
up: wb2305, wb2401, wb2403, wb2406, wb2407,
wb2408, wb2410, wb2411 |
Detailed
description of topics:
Physical modelling of dynamic systems. Basic
principles of process modelling. Formal steps in a modelling procedure, decision
structure. Methodology, goals, purpose and validation of the model. System boundaries,
subsystems, conservation laws. Causality, time scales. Macroscopic versus microscopic
models. Linearity, linearization, characterization of non-linear model behaviour.
Differential-algebraic model equations, explicit versus implicit simulation solutions.
Time scale characterization, modal approximation, time scale separation, singularly
perturbed models. Model approximation based on time moments, Pade approximation.
Compartmental models, bilaterally coupled physical subsystems. Spatially distributed
models, described in space and time in terms of partial differential equations. Causality,
boundary conditions.
Internal structure of input-output models, described by differential-algebraic equations. Rosenbrock's system
matrix, reduction to state-space form, index problems. Relationships with simulation
tools. Model uncertainty descriptions, sensitivity analysis. Realization of input-output
models. Model reduction via balancing and truncation.
|
Course
material:
O.H.Bosgra, Modelling of Dynamic Systems, Course
notes for wb 2406, Preliminary version, 164 pages, TU Delft/WbMT 1994. Revised version due
januari 1996.
|
References
from literature:
-
[1] ISBN: [0-13-221242-0], Friedly,J.C., Dynamic
behavior of processes, Prentice Hall, Inc., Englewood Cliffs, NJ, 1972.,
-
[2] ISBN: [3-540-50082-0], Kecman,V., State-Space
Models of Lumped and Distributed Systems., Lect.Notes Control Inf.Sci. vol 112., Springer
Verlag, Berlin, 1988.
-
[3] ISBN: [0-471-27535-2], Franks,R.G.E., Modeling and
Simulation in Chemical Engineering, John Wiley & Sons, Inc., New York, NY, 1972.,
-
[4] Himmelblau,D.M. Bischoff,K.B., Process Analysis and
Simulation. Deterministic Systems, John Wiley & Sons, Inc., New York, NY, 1968.
-
[5] ISBN: [3-527-28577-6], Ingham,J. Dunn,I.J.
Heinzle,E., Chemical Engineering Dynamics. Modelling with PC Simulation, VCH
Verlagsgesellschaft, Weinheim, W.Germany, 1994.
|
Remarks
(specific information about assesment, entry requirements, etc.): |
Goals:
The goal of the course is to provide an
introduction to the basic steps of physical system modelling, model simplification and
model simulation. The course discusses many example problems from the area of process
dynamics, chemical processes, and dynamical mechanical systems.
|
Computer
use:
The computer will be a key instrument in all steps
of model formulation, model simulation and model approximation. from the literature.
Simulation using Simulink (or similar) is required to investigate and assess the dynamic
properties and behaviour of the plant or system under study, as relevant for its
operation.
|
Laboratory
project(s):
The modelling and simulation exercise is an
individual exercise where the student has to formulate a dynamic model on the basis of a
description of the system in the form of a paper or article.
|
Design
content:
The goal of the course is modeling as part of a
process or control system design activity.
|
Percentage
of design: 0% |