MCNP6® advanced course

This advanced course is designed for people who have experience in running MCNP Monte Carlo calculations, but who would like to advance their skills and develop greater depth of understanding of theory and use techniques. Provided examples will be assembled, executed, and examined. Time will be available throughout the week to discuss individual questions and problems with MCNP experts. If time permits, details on more advanced features in the code can be discussed at the attendee’s request.

Topics: New features in MCNP6.3, Brief refresher of basic concepts, Geometry: unstructured mesh (UM) and hybrid (CSG+UM) geometry, Source definitions: k-eigenvalue-to-fixed-source bootstrapping, Variance reduction: tally diagnostics and biasing, weight-window generation, Criticality: eigenvalue convergence acceleration & assessment; benchmarking, Python-based quantitative and qualitative post-processing techniques