Data-driven predictive modeling of FeCrAl oxidation

IF 2.2 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Materials Letters: X Pub Date : 2023-03-01 DOI:10.1016/j.mlblux.2023.100183
Indranil Roy, Subhrajit Roychowdhury, Bojun Feng, Sandipp Krishnan Ravi, Sayan Ghosh, Rajnikant Umretiya, Raul B. Rebak, Daniel M. Ruscitto, Vipul Gupta, Andrew Hoffman
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引用次数: 3

Abstract

FeCrAl alloys are among the most promising candidates for accident-tolerant fuel cladding material in light water nuclear reactors. Despite their high-temperature oxidation resistance in corrosive environments coupled with their hydrothermal corrosion resistance, a key challenge remains in optimizing the composition of the alloy that can be achieved through statistical analysis. However, the current literature on FeCrAl alloy design lack studies for designing alloys based on oxidation resistance. This study addresses that gap by developing a predictive model for the oxidation of FeCrAl alloys based on an experimental dataset, which lays the groundwork for model-based optimization for alloy composition.

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数据驱动的FeCrAl氧化预测建模
铁铁合金是轻水反应堆耐事故燃料包壳材料中最有前途的候选材料之一。尽管它们在腐蚀环境中具有高温抗氧化性和水热腐蚀性,但一个关键的挑战仍然是通过统计分析来优化合金的成分。然而,目前关于铁铁合金设计的文献缺乏基于抗氧化设计合金的研究。本研究通过开发基于实验数据集的FeCrAl合金氧化预测模型来解决这一空白,这为基于模型的合金成分优化奠定了基础。
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来源期刊
CiteScore
3.10
自引率
0.00%
发文量
50
审稿时长
114 days
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