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The importance of plasma-wall interaction processes for the edge plasma is well known: creation of impurities by different sputtering mechanisms or recycling properties of the walls are examples of processes determining the divertor characteristics and the edge plasma profiles. To be able to have a better understanding of the plasma-wall interaction process itself, a multi-scale procedure is followed: molecular dynamics calculations resolve the microscopic length scale and deliver quite precise input data for kinetic Monte Carlo calculations (jump frequencies, migration energies, jump step-sizes) used for meso-scale up to the macroscopic system length. To cover the whole length scale involved -from microscopic up to macroscopic -several subsequent levels of kinetic Monte Carlo are needed, each providing the necessary input data for the next level. With this procedure the corresponding time scales spanning from picoseconds atomic interaction times to wall equilibration times of at least milliseconds will be spanned. Inclusion of a realistic structure model is also important, like for porous graphite where the void structure and orientation of the microcrystallites have to be included. First results of such a multi-scale calculation are presented studying the diffusion of hydrogen isotopes in porous graphite and are compared with experimental results from the literature.

Numerical and analytical study of a detached divertor equilibrium is presented. The model uses one-dimensional equations for continuity, momentum and energy balance with radiation, ionization, charge-exchange, and recombination processes. A reasonably simple neutral model is also employed. Analytical calculation, using a simple five-region model for a case with negligible convective heat flux and constant sources/sinks, captures the essence of detailed numerical calculation for the same case. More general cases are handled numerically. The detachment is studied as a function of the ratio Q⊥/S⊥ [the ratio of power and particle volume source, coming from the core to the scrape-off layer (SOL) region]. For low values of Q⊥/S⊥ (detached state), at the midplane and at the target, the ion temperature (Ti) is almost equal to the electron temperature (Te). As this ratio increases (attached state), Ti is larger than Te at the midplane. However at the target, Te is found to be slightly larger than Ti. It is also observed that as Q⊥/S⊥ increases, the region of most intense radiation shifts progressively from closer to the X-point towards the target plate.

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A multi-scale model for particle diffusion in porous structures is used to study the effect of the porous internal structure of graphite on atomic hydrogen transport and inventory in graphite. The diffusion of trace amounts of atomic hydrogen are modeled as a trapping-detrapping mechanism within the porous network typical in graphites. Activation energies for the different traps are taken from experiments and from molecular dynamics simulations. It is seen that by varying the size of the voids between granules which constitute the graphite, the trace diffusion coefficients of hydrogen vary by several orders of magnitude as reported in experiments ([1] and references therein) and it scales as L 2 void , where L void is the average size of voids in the graphite. Different diffusion mechanisms dominate at different graphite temperatures. Depending on the detrapping mechanism and on the internal structure of graphite, the jump lengths after each detrapping event can vary over a few orders of magnitude. This gives rise to the possibility of anomalous diffusion in graphite. The effect of closing the pores of graphite is also presented.

We report on molecular Dynamics (MD) simulations carried out in fcc (Cu) and bcc metals (W) using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code to study (i) the statistical variations in the number of Frenkel Pairs produced by energetic primary knock-on atoms (PKA) (0.1-5 keV) directed in random directions and (ii) the in-cascade cluster size distributions. It is seen that more than 100 random directions have to be explored for the variance in the number of Frenkel pairs produced to become steady in the case of fcc Cu, whereas for bcc W at least 60 random directions need to be explored. It is also seen that most of the interstitials are single in all cases. The number of Frenkel pairs produced in the MD simulations are compared with that from the Binary Collision Approximation Monte Carlo (BCA-MC) code SDTRIM-SP and the results from the NRT model. It is seen that a proper choice of the damage energy, i.e. the energy required to create a stable interstitial is essential for the BCA-MC results to match the MD results. On the computational front it is seen that in-situ processing saves the need to input / output (I/O) atomic position data of several tera-bytes when exploring 1000 random directions and there is no difference in run-time because the extra 1 run-time in processing data is offset by the time saved in I/O.

The structure of defect clusters formed in a displacement cascade plays a significant role in the microstructural evolution during irradiation. Molecular dynamics simulations have been widely used to study collision cascades and subsequent clustering of defects. We present a novel method to pattern match and classify defect clusters. A cluster is characterized by the geometrical and topological histograms of its angles and distances which can then be used as similarity metrics. The technique is demonstrated by matching similar clusters for different cluster shapes like ring, crowdions etc. in a database of cascade damage configurations in Fe and W at different energies. We further use graph based dimensionality reduction techniques and unsupervised machine learning on the features of all the clusters present in the database to find classes of clusters. The classification successfully separates out many already known categories of clusters such as crowdions, planar crowdion pairs, rings and perpendicular crowdions. The dimensionality and size of different classes provides a broad categorization of classes. The distribution of different classes of shapes among cascades of different elements and energies shows the exclusivity of shapes to elements and energies. We discuss the key points and computational efficiency of the algorithms along with the various prominent results of their application. We discuss the motivation for using machine learning and statistics for the problems and compare different techniques. The algorithms along with the supporting analysis and visualizations give an unsupervised approach for classification and study of defect clusters in cascades. The distribution of cluster shapes and structures along with the shape properties like diffusivity, stability, etc. can be used as input to higher scale models in a multi-scale radiation damage study.The defects formed during the displacement cascades due to irradiation are the primary source of radiation damage [1,2,3,4,5,6,7]. The defects in metals with body-centered cubic structure are produced in the form of single point defects (interstitials and vacancies) or clusters of such defects. The point defects and glissile clusters diffuse after the cascade to either annihilate or form bigger defect clusters. The structural details of primary point defect clusters (formed as a direct consequence of the cascade) define the diffusion, recombination, thermal stability and their other characteristics [2,8,9,10] which in the long term determine the micro-structural changes in the material [11,12,13,14,15]. These properties have an affect on the results of higher scale models like Monte Carlo methods, rate theories etc. [13,14,7,16]. The glissile clusters can move and interact with other defects and grain boundaries whereas the sessile clusters can be nucleation centers for defect-growth. The interaction of these clusters with other defects will decide the micro-structural changes due to irradiation. Classification and taxonomy of all possible clusters in diff...