Fusion power is the production of electricity from a hot plasma of deuterium
and tritium, reacting to produce particles and 14 MeV neutrons, which are
collected by a cooling system. Their kinetic energy is transformed into heat
and electricity via steam turbines. The constant
ux of neutrons on the rst
wall of the reactor produces atomic displacement damage through collisions
with nuclei, and gas bubbles as a result of transmutation reactions. This
leads eventually to hardening and embrittlement. Designing a material able
to withstand such intensity of damage is one of the main aim of research in
the eld of controlled fusion.
In the past decades, many experiments have been carried out to under-
stand the formation of radiation{induced damage and quantify the changes
in mechanical properties of irradiated steels, but the lack of facilities prevents
us from testing candidate materials in a fusion{like environment. Modelling
techniques are utilised here to extract information and principles which can
help estimate changes in steels due to damage.
The elongation and yield strength of various low{activation ferritic/
martensitic steels were modelled by neural networks and Gaussian processes.
These models were used to make predictions which were compared to exper-
imental values. Combined with other techniques and thermodynamic tools,
it was possible to understand the evolution of the mechanical properties of
irradiated steel, with a particular focus on the role of chromium and the
roles of irradiation temperature and irradiation dose. They were also used
to extrapolate data related to ssion and attempt to make predictions in
fusion conditions.
A set of general recommendations concerning the database used to train
the neural networks were made and the usage of such a modelling technique
in materials science is discussed.
An attempt to optimise the performance of neural networks by suppress-
ing some random aspects of the training is presented. Models of the elon-
gation, yield strength and ductile-to-brittle transition temperature trained
following this procedure were created and compared to classical models
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