用于TRISO燃料性能预测的碳化硅中铯输运的多尺度、机制建模

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone
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引用次数: 0

摘要

了解铯在三结构各向同性(TRISO)粒子燃料中的输运对于预测高温反应堆裂变产物的释放至关重要。然而,目前的挑战包括扩散率数据的显著分散以及碳化硅层中无法解释的温度依赖扩散机制。本研究通过开发一个集成原子模拟和相场建模的多尺度、机制Cs输运模型来解决这些挑战。我们的模型量化了温度和晶粒尺寸对Cs扩散率的影响,将实验观察到的情况归因于高温下由体主导的扩散率向低温下由晶界主导的扩散率的转变。该模型通过扩散测量和先进气体反应堆(AGR)-1和AGR-2辐照后裂变产物释放数据进行验证,增强了BISON燃料性能代码的预测能力。该研究加深了我们对三氧化二碳颗粒Cs释放及其对温度和碳化硅晶粒尺寸的依赖的理解,对高温核反应堆的安全性和效率具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction

Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction

Understanding cesium (Cs) transport in TRistructural ISOtropic (TRISO) particle fuel is crucial for predicting fission product release in high-temperature reactors. However, current challenges include significant scatter in diffusivity data and unexplained temperature-dependent diffusion regimes in the silicon carbide layer. This study addresses these challenges by developing a multiscale, mechanistic Cs transport model integrating atomistic simulations and phase field modeling. Our model quantifies temperature and grain size effects on Cs diffusivity, attributing experimentally observed regimes to a transition from bulk-dominated diffusivity at high temperatures to grain boundary-dominated diffusivity at lower temperatures. The model, validated against diffusion measurements and advanced gas reactor (AGR)-1 and AGR-2 post-irradiation fission product release data, enhances the predictive capability of the BISON fuel performance code. This study advances our understanding of Cs release from TRISO particles and its dependence on temperature and silicon carbide grain size, with implications for the safety and efficiency of high-temperature nuclear reactors.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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