Since the beginning of the utilisation of nuclear energy to generate electric power, the
evaluation of the nuclear plants behaviour under accidental scenarios is an important
line of research in the field of nuclear safety. But in most cases, as it is the case of other
engineering fields, the experimental assessment of nuclear power plants behaviour is
not feasible. This reason makes necessary to use simulation tools capable to reproduce
the plant behaviour.
In this way, the regulatory bodies allow the use of termalhydraulic simulation codes to
guarantee the safe operation of the installations, but only if the uncertainty associated
with the simulation is properly quantified. The uncertainties associated with a
termohydraulic simulation have different origins as: the limitations of the models used
to describe the physical process that take place inside the plant, the errors associated
with the numerical models used by the codes in the resolution of such models, and
unknown values of different variables necessaries to build the plant model.
In order to quantify and limit those uncertainties several methodologies have been
developed. But all of them require a certain amount of sensitivity analysis, which are
manually performed, and so they strongly depend on the analyst.
In this PhD thesis a new methodology to quantify and limit the termalhydraulic
simulations has been developed. This new methodology uses an optimisation technique
to minimise the user effect introduced in the thermalhydraulic simulations. In this way,
an automatic and systematic parameter search and optimisation method is proposed to
be used to assure that the error introduced in the simulation is the minimum possible.
This new methodology is applied to simple models as the convection equation and the
convection-diffusion equation using two different optimisation techniques: a genetic
algorithm and a direct search method. In particular, a steady state genetic algorithm and
the multidirectional search algorithm have been used to perform the analysis. From the
results obtained in the analysis it can be concluded that the direct search method is more
efficient to deal with this kind of problems.
As an application of the proposed methodology to a best estimated code, two cases
included in the RELAP5 assessment matrix have been studied. The purpose of these
applications has been to prove the capability of the methodology in the determination of
optimal values for parameters of the plant model, such as initial conditions and
boundary conditions which are frequently unknown in this kind of simulations.
Finally, the methodology has also proved to be useful in the construction of reduced
reactor models. These models present different effective parameters which can be
optimised to achieve a behaviour of the reduced model as similar as possible to the
behaviour of the plant that represents. In particular, a two asymmetric loops PWR
reactor model has been constructed, and some of its effective parameters have been
determined using as a reference the simulations of operational transients performed with
the best estimate code RELAP5. It is shown that for such transients, the reduced model
using the parameters obtained from the optimisation allows to forecast the behaviour of
the plant with good accuracy.