Materials for fusion power plants Stéphane Forsik - Phase Transformations and Complex Properties Group FUSION POWER PLANT The production of electricity from the fusion of deuterium and tritium is an alternative source to classical fission nuclear power plants but, as a lot of technical issues still have to be resolved, the first commercial fusion power plant is not expected before 50 years. The plasma of deuterium and tritium will reach about 100 million K and neutrons with an energy of 14 MeV produced during the reaction will strongly irradiate the materials constituting the first wall of the reactor. Average irradiation doses are expected to be about 200 displacements-per- atom (dpa) over a lifetime in service of several decades. The behaviour of materials in such a hostile environment is not well-known and the choice of a candidate material with the best mechanical resistance to radiation-induced hardening and swelling is crucial. Large quantities of experimental data from irradiation experiments in fission reactors are available. A neural network model (a regression method able to fit non-linear functions) was trained on that database. It allowed us to catch complex relations between several input parameters (chemical composition, heat treatment, irradiation parameters, etc.) and the output (yield strength,  DBTT, etc.) and helped in the understanding of some aspects of the irradiation hardening. Such a model based on fission reaction data can also be extrapolated to higher values in order to predict the behaviour of materials in a fusion environment. ITER: prototype of a fusion reactor. CONCLUSIONS AND FURTHER WORK Two models were compared and predictions obtained with the neural network as well as with the Gaussian process are in agreement with experimental values but differ when extrapolated and no experimental data are available at high irradiation doses. Several aspects of the irradiation-induced hardening mechanism, such as the irradiation-induced dissolution of precipitates, need to be investigated and understood. However, Gaussian processes appear to be have the same accuracy at low doses and their training is less time-consuming. PREDICTION Neural-network predictions of the yield strength as a function of the irradiated dose and tensile test temperature for two different alloys are given and compared against experimental values: A model trained on a database containing 28 input parameters and about 1,600 lines was used to make predictions. Modified 9Cr-1Mo ferritic steel irradiated at dpa: predictions are in agreement with experimental values and uncertainties are small. The irradiation-induced hardening decreases at high tensile test temperatures (annihilation of radiation-induced defects). Low-activation ferritic- martensitic EUROFER’97 steel irradiated at 2.5, 7.5 and 9 dpa: predictions in agreement, the yield strength increases with the irradiation dose and saturates at ~10 dpa. Contrary to the previous example, the radiation- induced hardening does not disappear at high temperature, due the difference in irradiation temperature. EXTRAPOLATION The highest dose available in the database is 72 dpa whereas ~200 dpa will be reached in a fusion reactor. Two different regression tools were used to extrapolate at such high irradiation doses: a neural network and another method based on Gaussian processes. The two predictions differ violently. The neural network predicts 0 MPa just below 100 dpa, accompanied with large uncertainties whereas the Gaussian process predicts a stable value between 60 and 200 dpa with a relatively high confidence. This difference implies that hardening mechanisms at high doses should be more deeply understood.