الفهرس 
Background Nuclear Research Reactors
Reactor Core KineticsOnly 14 pages are availabe for public view
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Abstract
Different power levels of a nuclear reactor have different behavior
due to nonlinearity and time variance parameters. So, the need for a
controller to handle these constraints and work at different power levels is
essential.
The work in this thesis provides a design of two controllers and apply these
controllers over a multi-point reactor core model. This model has sufficient
accuracy to represent the reactor and has the main neutron reaction and the
six precursors delayed neutron. The effect of temperature changes and the
nonlinearity of Xenon poisoning have been taken into consideration in
designing this model. This model also takes into consideration the effect of
fuel consumption during the operation.
In this thesis, two controllers have been designed. These controllers are
Model Predictive control and Neural Network Predictive control. These
controllers are tested for tracking and regulating scenarios at different
power levels. These scenarios are evaluated for enough time to test the
nonlinearity behavior and time variance of the system and their results are
compared to the currently installed Proportional Derivative controller.
Although filters are applied over the sensor system for both neutron noise
and signal noise, the controllers are tested for nonfiltered signals.
The model predictive controller has shown its performance superiority
over the other controllers. It handles system constraints and dynamics. It
has the least variation over the steady state, no overshoot, no steady state error and with the fastest action.