53 lines
3.5 KiB
TeX
53 lines
3.5 KiB
TeX
\section{Introduction}
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Advances in computing capabilities are further improving the feasibility of
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fast-running high-fidelity simulations of nuclear cores. Where current core
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simulations require a series of homogenization procedures to model reactors on a
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coarse-mesh in order to overcome memory and computer processing limitations
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\cite{Smith1986303}, modern techniques aspire to provide solutions using
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fully-detailed geometries with far fewer approximations. For instance, recent
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research efforts improving the scalability and efficiency of Monte Carlo neutron
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transport algorithms have resulted in very accurate solutions to the well-known
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Hoogenboom-Martin problem \cite{HM_bench} with the MC21 Monte Carlo code
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\cite{mc21_hoomartin2010} \cite{mc21_hoomartin2012}, with statistical
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uncertainties approaching the 1\% pin-power accuracy criterion proposed by Smith
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\cite{kordsmithchallenge} for full-core Monte Carlo analysis. Likewise, modern
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deterministic approaches are targeting similar accuracy on some of the world's
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largest supercomputers \cite{denovo_jaguar2012}. Indeed, the development of such
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high-fidelity full-core modelling capabilities for \acp{LWR} is the stated goal
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of several DOE projects such as \acs{CASL} (\acl{CASL}) \cite{casl_goals} and
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\acs{CESAR} (\acl{CESAR}) \cite{cesar_goals}. However, there is a lack of
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detailed and relevant benchmarks needed to validate these methods, and a more
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complete benchmark that includes measured reactor data is presented here.
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Nearly all non-proprietary benchmarks do not capture the detail of \acp{LWR}
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needed to validate high-fidelity methods being developed today. For instance,
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the OECD \ac{LWR} and \ac{PWR} reactor benchmark specifications
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\cite{oecd_bench} mostly refer to simple lattice experiments, limited physics
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testing configurations, and small test reactors. Whereas several full-core
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\acs{LMFR} models are available (\acs{FFTF}, JOYO, etc.), \acp{LWR} are markedly
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under represented. This is particularly true for full-core benchmarks of most
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interest to the methods development and regulatory community: production
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reactors similar to operating and planned commercial units. Some recent
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publications come close to satisfying this need, but they ultimately fall short
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either in scope or applicability. For instance, a 2011 \acs{EPRI} report
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\cite{epri2011bench} provides reactivity and depletion data with several
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benchmark specifications for \ac{PWR} assembly lattices. These benchmarks, while
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using full-core simulations and measured data, take the approach of reducing the
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benchmark to single-assembly calculations and do not provide detailed full-core
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tests or measured reactor data.
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A distinction should be noted between the kind of data-backed benchmark being
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pursued here and the code-comparison benchmarks often used to evaluate methods.
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For instance, several newer and widely-used \ac{LWR} benchmarking suites are not
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backed by measured data, such as the C5G7 \acs{MOX} benchmarks \cite{c5g7}, the
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Hoogenboom-Martin \ac{LWR} Monte Carlo benchmark \cite{mc21_hoomartin2010}, and
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the approximate \ac{PWR} specification by Douglas et al.
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\cite{Douglass20101384}. Instead, comparisons are based on results submitted by
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many different parties using a variety of codes. Measured reactor data is
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required for a credible validation.
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This document introduces a new benchmark that addresses many of the shortcomings
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of previous \ac{LWR} benchmarks by providing a highly-detailed \ac{PWR}
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specification with two cycles of measured operational data that can be used to
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validate high-fidelity core analysis methods.
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