Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Kowal, Peter J.; Blake, Camden E.; Dominesey, Kurt A.; Lefebvre, Robert Alexander; Brown, Forrest B.; Ji, Wei

doi:10.1080/00295639.2022.2153617

Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Journal Article · Wed Feb 08 00:00:00 EST 2023 · Nuclear Science and Engineering

DOI:https://doi.org/10.1080/00295639.2022.2153617· OSTI ID:1976016

Kowal, Peter J. ^[1]; Blake, Camden E. ^[1]; Dominesey, Kurt A. ^[1]; ^[2]; Brown, Forrest B. ^[3]; ^[1]

Rensselaer Polytechnic Inst., Troy, NY (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of New Mexico, Albuquerque, NM (United States)

Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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

Grant/Contract Number:: AC05-00OR22725; NE0008707

OSTI ID:: 1976016

Journal Information:: Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering Journal Issue: 8 Vol. 197; ISSN 0029-5639

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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