الفهرس 
Nuclear ReactorsOnly 14 pages are availabe for public view
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Abstract
In spite of the various classifications of nuclear reactors either according to their
purpose, structure or the energy distribution of neutrons, they all possess the same
bases of calculational tools. The design, construction and operation follow up for any
nuclear reactor require specific criteria to maintain its safety. These criteria require the
knowledge of the parameters affecting the reactor operation like power and flux
distribution, control rod worth, effective multiplication factor and fuel burnup. All
these parameters must be determined throughout the reactor cycle.
Special computer codes have been developed to determine the values of all the
reactor parameters. They vary according to the applied model, computational methods
and the calculation procedure. These computer codes are called neutronic calculation
codes. These codes are validated and maintained for application in a variety of nuclear
energy research programs.
Researchers have investigated the potential applications of artificial neural
network in enhancing the safety and efficiency of nuclear reactors. The areas under
investigation are: diagnosis of specific abnormal conditions, detection of the change of
mode of operation, signal validation, monitoring of check valves, modeling of the
plant thermodynamics, monitoring of plant parameters, and analysis of plant
vibrations.
The power level of a reactor depends on the macroscopic fission cross section
and the neutron flux. Over a short time interval, the cross section remains essentially
constant, although it may not have the same value at all locations in the core. Hence,
the power level at any instant can be considered proportional to the neutron flux. In
most situations a reactor is controlled by varying the neutron flux. Among the general methods available for changing the neutron flux in a reactor is the insertion and
withdrawal of the control rods.
The present study proposes an Artificial Neural Network (ANN) modeling
technique that predicts the control rods positions in a nuclear research reactor. The
neutron flux in the core of the reactor is used as the training data for the neural
network model. The data used to train and validate the network are obtained by
modeling the reactor core with the validated neutronic calculation code; CITVAP. The
type of the network used in this study is the feedforward multilayer neural network
with the backpropagation algorithm.
The results achieved show the potential effectiveness of neural network in
determining the control rods positions knowing the neutron flux at each fuel element
for a material and test reactor; MTR. The results also show the ability of the neural
network to find out functional relationship between the neutron flux (inputs) and the
control rods positions (outputs), where we don’t know how to describe the functional
relationship in advance, but we do know examples of correct mapping. This is the
power of the neural network to discover its own algorithms.
The method proposed in the study can be used to predict critical control rods
positions to be used for reactor operation in the process of replacing burned fuel by
fresh fuel.