$χ$-$MeRA$: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

Stolte, Kristin Nichole; Tsvetkov, Pavel V.

doi:10.1016/j.anucene.2022.109617

$$χ$$-$MeRA$: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

Journal Article · Mon Dec 12 00:00:00 EST 2022 · Annals of Nuclear Energy

DOI:https://doi.org/10.1016/j.anucene.2022.109617· OSTI ID:3015152

^[1]; Tsvetkov, Pavel V. ^[2]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Texas A & M Univ., College Station, TX (United States)
Texas A & M Univ., College Station, TX (United States)

Here, the reactor physics community is always focused on reducing the computational time and memory required for simulations. $$χ$$-$MeRA$, which stands for flux-based-($$χ$$)-Mesh tally Refinement Adaptively, was built to reduce the computational time and memory required to solve the neutronics side of a multiphysics problem when compared to traditional methods for mesh based tallies in Monte Carlo (MC) simulations. $$χ$$-$MeRA$ couples a MC code with an adaptive mesh refinement (AMR) algorithm to take advantage of the accuracy of a MC code and the efficiency of an AMR algorithm. Also developed within $$χ$$-$MeRA$ was a set of metrics to assess the effects of the refinement on various parameters in the simulation space. For a plutonium sphere, $$χ$$-$MeRA$ shows a reduction in memory usage and computation time when compared to a fully refined mesh by a factor of 14.7 and 6.7, respectively. When compared to an unstructured mesh, improvement of 1.3 and 4.8 was achieved for memory usage and computation time. The development of $$χ$$-$MeRA$ helps solve the neutronics side of a multiphysics problem in a faster, more computationally efficient manner than traditional methods, and the final mesh created contains accurate results that can be passed onto the next physics code.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 3015152

Report Number(s):: LA-UR--22-29945; 10.1016/j.anucene.2022.109617

Journal Information:: Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Vol. 182; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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$$χ$$-$MeRA$: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

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