Attila4MC provides MCNP users with a graphical user interface (GUI) front end that can eliminate the most time-consuming bottlenecks in setting up, running, and visualizing MCNP calculations. Attila4MC provides robust CAD integration, allowing users to work directly with production CAD assemblies without extensive cleanup or simplification. Attila4MC supports automated CADIS and FW-CADIS variance reduction powered by the new Cottonwood Variance Reduction Module; providing rapid convergence for highly attenuating cases, even with complex models containing hundreds or thousands of parts.
Attila includes all the features of Attila4MC, with the added benefit of the Attila deterministic solver for independent calculations and global solution fields. Attila deterministically solves the Linear Boltzmann Transport Equation, incorporating unstructured mesh geometries allowing for rapid design iterations on complex models and providing the means to easily compare a complimentary Attila4MC calculation for verification.
The Attila suite has a GUI driven activation capability (Fornax) that tracks the population of isotopes created through neutron activation and decay processes, compatible with both SMP (attilasolver) and DMP (severian). Fornax provides users with the capability to use the Rigorous Two-Step (R2S) method with an Attila deterministic or Attila4MC (MCNP6) based workflow. With the Attila deterministic workflow, users have the ability to run neutron flux and activation calculations on the tetrahedral mesh and then produce gamma sources at desired decay time steps with an appropriate neutron flux strength. The gamma sources are produced on a mesh element wise basis, and the activation gamma calculations can then be run on the same tetrahedral mesh, making for an accurate, simple and fast workflow.
Attila4MC-Cottonwood was developed with the goal of providing an efficient, intelligent, and highly automatic CADIS and FW-CADIS workflow, regardless of geometric complexity. Whether your model contains several or several thousand parts, Attila4MC-Cottonwood can dramatically shorten and automate your MCNP analysis cycle for deep penetration calculations.
Attila4MC-Cottonwood directly reads the MCNP abaqus mesh, the MCNP input file, and several user specified parameters. From this information, Attila4MC-Cottonwood will automatically generate an arbitrary mesh refinement (AMR) computational grid for the deterministic weight window generation. Local refinement and coarsening are automatically performed based on calculated material half value layers, eliminating the need for users to manually define a Cartesian computational/weight window grid.