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Abstract of PROMIS Project

During the operation of future fusion reactors it is expected that materials surrounding the fusion plasma will be exposed to high fluxes of energetic neutrons. The effects of neutrons on materials involve i) the displacement of atoms from their site in the lattice creating point defects and clusters, and ii) nuclear transmutation reactions leading to the formation of impurities such as He and H in the material. The subsequent evolution of these defects/impurities, in particular the agglomeration of He, H and vacancies (and other impurities), can severely degrade the properties of materials under irradiation. Therefore, predicting the evolution of these defects and impurities is essential to understand the microstructural change of materials and the degradation of their properties under fusion conditions. An efficient method to predict the evolution of defects in materials under irradiation is to use the rate equation framework, based on diffusion equations. The CPU time required to simulate a typical system with this method is usually not high (< 1 hour). However, the complexity of the systems has considerably increased during the last few years, often leading to systems composed of several thousands of ODE s and PDE s at each mesh node. In addition, simulating a system close to those reached experimentally requires using a relatively high number of spatial nodes with a fine mesh in regions where concentration gradients are expected to be steep, e.g. close to surfaces or in the case of implantation profiles. In such conditions, the CPU time increases significantly, ranging sometimes between 24 h and 10 days. It is therefore highly desirable to have a supercomputational tool in order to significantly decrease the CPU time required for simulations. In this project, our aim is to parallelize the Rate Theory code PROMIS using MPI and/or OpenMP. This code, which is based on finite differences, has been extensively used to simulate evolution of defects in different materials under irradiation.