火花等离子烧结UCo的热物理性质：与机器学习预测的比较
Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions
AI总结：
本研究通过实验验证了机器学习对UCo热导率预测的准确性，并展示了其在筛选高热导率铀化合物中的应用价值。
摘要：
氧化铀因其高铀密度和化学稳定性而被广泛用于商用轻水反应堆的核燃料。然而，其相对较低的热导率在燃料完整性和安全裕度方面并不理想，特别是在失水事故期间。尽管开发具有更高热导率的事故容忍燃料被强烈期望，但许多潜在的铀化合物由于处理放射性材料的限制而仍未被探索。为了高效筛选具有高热导率的铀化合物，以往的研究利用了机器学习模型来加速发现过程。在本研究中，我们通过制造UCo并测量其高温热物理性质来实验性地检验模型的预测。我们的结果表明，机器学习预测的UCo热导率与实验测量结果一致。尽管存在轻微的差异，额外的SHAP分析表明，模型的决策逻辑与已知的物理趋势一致。总体而言，本研究填补了报告的UCo热物理性质的空白，并为机器学习辅助筛选与先进燃料开发相关的铀化合物提供了实验支持。
英文摘要：
Uranium dioxide has been widely used as a nuclear fuel in commercial light water reactors due to its high uranium density and chemical stability. However, its relatively low thermal conductivity is not optimal from the viewpoints of fuel integrity and safety margins, particularly during loss-of-coolant accidents. Although the development of accident-tolerant fuels with higher thermal conductivity is strongly desired, many potential uranium compounds remain unexplored due to constraints associated with handling radioactive materials. To efficiently screen promising uranium compounds with high thermal conductivity, past studies have leveraged machine-learning models to accelerate the discovery process. In this study, we experimentally examine the model's predictions by fabricating UCo and measuring its high-temperature thermophysical properties. Our results show that the thermal conductivity of UCo predicted by machine learning is in good agreement with the experimental measurements. Despite slight discrepancies, additional SHAP analysis suggests that the model's decision logic is consistent with known physical trends. Overall, this study fills a gap in reported thermophysical properties of UCo and provides experimental support for machine-learning-assisted screening of uranium compounds relevant to advanced fuel development.
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