Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation

被引：0

作者：

Dhulipala, Somayajulu L.N. ^{[1
]}

Shields, Michael D. ^{[2
]}

Chakroborty, Promit ^{[2
]}

Jiang, Wen ^{[1
]}

Spencer, Benjamin W. ^{[1
]}

Hales, Jason D. ^{[1
]}

Labouré, Vincent M. ^{[3
]}

Prince, Zachary M. ^{[3
]}

Bolisetti, Chandrakanth ^{[4
]}

Che, Yifeng ^{[1
]}

机构：

[1] Computational Mechanics and Materials, Idaho National Laboratory, Idaho Falls,ID,83402, United States

[2] Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore,MD,21218, United States

[3] Reactor Physics Methods and Analysis, Idaho National Laboratory, Idaho Falls,ID,83402, United States

[4] Advanced Reactor Technology and Design, Idaho National Laboratory, Idaho Falls,ID,83402, United States

来源：

Reliability Engineering and System Safety | 2022年 / 226卷

关键词：

This research is supported through the INL Laboratory Directed Research & Development (LDRD); USA Program under DOE Idaho Operations Office Contract DE-AC07-05ID14517. This research made use of the resources of the High Performance Computing Center at INL; which is supported by the Office of Nuclear Energy of the U.S. DOE and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517.We thank Peter German at Idaho National Laboratory for his valuable feedback on a draft of this paper. This manuscript has been authored by Battelle Energy Alliance; LLC under Contract No. DE-AC07-05ID14517 with the US Department of Energy. The United States Government retains and the publisher; by accepting the article for publication; acknowledges that the United States 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 United States Government purposes. This research is supported through the INL Laboratory Directed Research & Development (LDRD); which is supported by the Office of Nuclear Energy of the U.S. DOE and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517.This manuscript has been authored by Battelle Energy Alliance; for United States Government purposes;

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共 28 条

[1] Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation
Dhulipala, Somayajulu L. N.
Shields, Michael D.
Chakroborty, Promit
Jiang, Wen
Spencer, Benjamin W.
Hales, Jason D.
Laboure, Vincent M.
Prince, Zachary M.
Bolisetti, Chandrakanth
Che, Yifeng
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 226
[2] Active learning with multifidelity modeling for efficient rare event simulation
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Laboure, Vincent M.
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JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 468
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