Many hours are spent by systemand control engineers deriving reduced order dynamicmodels
portraying the dominant systemdynamics of thermohydraulic systems. A need therefore exists
to develop a method that will automate the model derivation process. The model format
preferred for control system design and analysis during preliminary system design is the state
space format. The aim of this study is therefore to develop an automated and generic state
space model extraction method that can be applied to thermohydraulic systems.
Well developed system identification methods exist for obtaining state space models from
input-output data, but these models are not transparent, meaning the parameters do not
have any physical meaning. For example one cannot identify system parameters such
as heat or mass transfer coefficients. Another approach is needed to derive state space
models automatically. Many commercial thermohydraulic simulation codes follow a network
approach towards the representation of thermohydraulic systems. This approach is probably
one of the most advanced approaches in terms of technical development. It would therefore be
useful to develop a state space extraction algorithm that would be able to derive reduced order
state space models from network representations of thermohydraulic systems. In this regard a
network approach is followed in the development of the state space extraction algorithm. The
advantage of using a network-based extraction method is that the extracted state space model
is transparent and the algorithm can be embedded in existing simulation software that follow
a network approach.
In this study an existing state space extraction algorithm, used for electrical network analysis, is
modified and applied in a new way to extract state space models of thermohydraulic systems.
A thermohydraulic system is partitioned into its respective physical domains which, unlike
electrical systems, have multiple variables. Network representations are derived for each
domain. The state space algorithm is applied to these network representations to extract
symbolic state spacemodels. The symbolic parametersmay then be substitutedwith numerical
values. The state space extraction algorithm is applied to small scale thermohydraulic systems
such as a U-tube and a heat exchanger, but also to a larger, more complex system such
as the Pebble Bed Modular Reactor Power Conversion Unit (PBMR PCU). It is also shown
that the algorithm can extract linear, nonlinear, time-varying and time-invariant state space
models. The extracted state space models are validated by solving the state space models
and comparing the solutions with Flownex results. Flownex is an advanced and extensively
validated thermo-fluid simulation code. The state space models compared well with Flownex
results.
The usefulness of the state space model extraction algorithm in model-based control system
design is illustrated by extracting a linear time-invariant state space model of the PBMR PCU.
This model is embedded in an optimal model-based control scheme called Model-Predictive
Control (MPC). The controller is compared with standard optimised control schemes such as
PID and Fuzzy PID control. The MPC controller shows superior performance compared to
these control schemes.
This study succeeded in developing an automated state space model extraction method that
can be applied to thermohydraulic networks. Hours spent on writing down equations from
first principles to derive reduced order models for control purposes can now be replaced
with a click of a button. The need for an automated state space model extraction method for
thermohydraulic systems has therefore been resolved / Thesis (Ph.D. (Computer and Electronical Engineering)--North-West University, Potchefstroom Campus, 2009.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/3838 |
| Date | January 2009 |
| Creators | Uren, Kenneth Richard |
| Publisher | North-West University |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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