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Title: Mechanical properties of materials for fusion power plants
Author: Forsik, Stéphane Alexis Jacques
ISNI:       0000 0004 2711 7348
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2009
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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 aims of research in the field of controlled fusion. In the past decades, many experiments have been carried out to understand 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 experimental 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 fission 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 suppressing some random aspects of the training is presented. Models of the elongation, yield strength and ductile-to-brittle transition temperature trained following this procedure were created and compared to classical models.
Supervisor: Bhadeshia, H. K. D. H. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral