TORT solutions to the NEA suite of benchmarks for 3D transport methods and codes over a range in parameter space

We present the TORT solutions to the 3D transport codes’ suite of benchmarks exercise. An overview of benchmark configurations is provided, followed by a description of the TORT computational model we developed to solve the cases comprising the benchmark suite. In the numerical experiments reported in this paper, we chose to refine the spatial and angular discretizations simultaneously, from the coarsest model (40 × 40 × 40, 200 angles) to the finest model (160 × 160 × 160, 800 angles). The MCNP reference solution is used for evaluating the effect of model-refinement on the accuracy of the TORT solutions. The presented results show that the majority of benchmark quantities are computed with good accuracy by TORT, and that the accuracy improves with model refinement. However, this deliberately severe test has exposed some deficiencies in both deterministic and stochastic solution approaches. Specifically, TORT fails to converge the inner iterations in some benchmark configurations while MCNP produces zero tallies, or drastically poor statistics for some benchmark quantities. We conjecture that TORT’s failure to converge is driven by ray effects in configurations with low scattering ratio and/or highly skewed computational cells, i.e. aspect ratio far from unity. The failure of MCNP occurs in quantities tallied over a very small area or volume in physical space, or quantities tallied many (∼25) mean free paths away from the source. Hence automated, robust, and reliable variance reduction techniques are essential for obtaining high quality reference values of the benchmark quantities. Preliminary results of the benchmark exercise indicate that the occasionally poor performance of TORT is shared with other deterministic codes. Armed with this information, method developers can now direct their attention to regions in parameter space where such failures occur and design alternative solution approaches for such instances.