Code Verification and Solution Verification framework in pin-resolved neutron transport code MPACT

  • Jipu Wang
  • , William R. Martin
  • , Thomas J. Downar
  • , Brendan Kochunas
  • , Nathan C. Andrews
  • , Lindsay Gilkey
  • , Erik D. Walker
  • , Benjamin S. Collins
  • , Martin Pilch

    Research output: Contribution to journalArticlepeer-review

    5 Scopus citations

    Abstract

    Program verification in scientific computing encompasses the application of formal and mathematical techniques to a scientific computing code for its credibility, accuracy, and validity. Code Verification identifies bugs and performance issues in the software development stage. Solution Verification assesses the applicability of the code and the accuracy of the solution to problems of interest. Both activities utilize application cases and quantify the error against prescribed acceptance criteria. However, simply executing more application cases does not guarantee stronger or more comprehensive credibility. In this work, we establish a verification framework that involves Code Verification and Solution Verification, both of which work together such that the overarching goal of “converge to the correct answer for the intended application” can be reasonably inferred. The application of such a verification framework is demonstrated using the pin-resolved neutron transport code MPACT, where standard unit tests and regression tests are covered, and where the Method of Exact Solutions and the Method of Manufactured Solutions are successfully used. Additionally, the applicability of Method of Manufactured Solutions is extended to the OECD/NEA C5G7 benchmark problems of practical material and geometric configurations. Solution Verification activities are demonstrated on a practical hierarchy of application models of increasing complexity ranging from 2D pin cell problems to 3D assembly problems. The convergence behavior and rate of convergence with respect to each individual variable are studied and provided. This framework can be adapted broadly to other fields involving scientific computing codes.

    Original languageEnglish
    Article number109365
    JournalAnnals of Nuclear Energy
    Volume178
    DOIs
    StatePublished - Dec 1 2022

    Funding

    This research was supported by the Consortium for Advanced Simulation of Light Water Reactors ( https://casl.gov/ ), an Energy Innovation Hub ( http://www.energy.gov/hubs ) for Modeling and Simulation of Nuclear Reactors, under U.S. Department of Energy Contract No. DE-AC05-00OR22725. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy.gov/downloads/doe-public-access-plan).

    Keywords

    • Code Verification
    • Convergence
    • Method of Manufactured Solutions
    • Solution Verification
    • Verification

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