TRISO particle fuel performance and failure analysis with BISON

被引:54
作者
Jiang, Wen [1 ]
Hales, Jason D. [1 ]
Spencer, Benjamin W. [1 ]
Collin, Blaise P. [3 ]
Slaughter, Andrew E. [2 ]
Novascone, Stephen R. [1 ]
Toptan, Aysenur [1 ]
Gamble, Kyle A. [1 ]
Gardner, Russell [3 ]
机构
[1] Idaho Natl Lab, Computat Mech & Mat Dept, POB 1625, Idaho Falls, ID 83415 USA
[2] Idaho Natl Lab, Computat Frameworks, POB 1625, Idaho Falls, ID 83415 USA
[3] Kairos Power LLC, 707 Tower Ave, Alameda, CA 94501 USA
关键词
BISON; TRISO; UCO; Failure analysis; Fission product release;
D O I
10.1016/j.jnucmat.2021.152795
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Because of its widespread use in multiple advanced reactor concepts, the ability to accurately simulate tri-structural isotropic (TRISO) fuel performance is essential for ensuring the safe operation of these reactors. To that end, the BISON fuel performance code has undergone significant recent development to expand its TRISO particle fuel performance modeling capabilities. This includes the development of material models, such as elastic, creep, swelling, thermal expansion, thermal conductivity, and fission gas release models. The capability to perform statistical failure analysis on large sets of samples has also been developed, utilizing a Monte Carlo scheme to execute fast-running 1-D spherically symmetric models. Stress adjustments are made in those 1-D models to account for multi-dimensional failure phenomena. Stress correlation functions are extracted from multi-dimensional failure simulation results, such as from a particle with cracked inner pyrolytic carbon (IPyC) and an aspherical particle. This paper provides a detailed description of the models used by BISON for TRISO fuel, along with a set of problems that test these models by (favorably) comparing them both with another code and experimental data. These include simulations of the Advanced Gas Reactor (AGR)-2 and AGR-5/6/7 experiments, with predictions for fuel performance parameters, failure probability, and fission product transport. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:18
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