将矩张量势与有限元建模相结合用于基于氟锂铍的熔盐系统中的传热预测
Integrating moment tensor potentials with finite-element modeling for heat transfer prediction in FLiBe-based molten salt systems
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中文总结 AI 辅助
研究基于氟锂铍的熔盐系统传热预测，提出集成多尺度框架，结合机器学习驱动的原子模拟与有限元建模。通过矩张量势获取输运性质并输入有限元模型，验证了该方法，还预测了三元系统传热效率变化，为MSR冷却剂配方筛选提供途径。
中文摘要 AI 辅助
熔盐是先进熔盐反应堆（MSR）中很有前景的传热介质，可靠的热物理性质测定对部件设计和安全至关重要。我们提出了一个集成多尺度框架，将机器学习驱动的原子模拟与有限元（FE）建模相结合，以预测基于氟锂铍的盐在直线换热器中的传热性能。在原子尺度上，针对纯氟锂铍（66 - 34和74 - 26 LiF - BeF2摩尔%）、氟锂铍 - 氟化镧和氟锂铍 - 四氟化铀，基于从头算数据主动训练矩张量势（MTP）。这些势用于分子动力学模拟以获得温度和成分相关的输运性质，映射为实验热回路三维有限元模型的输入。含文献输运性质的有限元模型在层流状态下将纯氟锂铍的实验传热行为再现到10%以内，在过渡和湍流状态下再现到18%以内。含MTP - MD衍生输运性质的同一模型将传热系数系统高估25 - 28%。应用于0 - 5摩尔%的三元系统氟锂铍 - 氟化镧和氟锂铍 - 四氟化铀，MTP - MD驱动的有限元模型预测相对于纯氟锂铍传热效率平均降低8 - 11%，四氟化铀影响最强。该框架是高温实验的补充，为快速筛选MSR冷却剂配方提供了基于物理的途径。
英文摘要
Molten fluoride salts are promising heat-transfer media for advanced molten salt reactors (MSRs), where reliable thermophysical property determination is critical for component design and safety. We present an integrated multiscale framework that couples machine-learning-driven atomistic simulations with finite-element (FE) modeling to predict the heat-transfer performance of FLiBe-based salts in a linear heat exchanger. At the atomistic scale, Moment Tensor Potentials (MTPs), actively trained on ab initio data, are developed for pure FLiBe (66-34 and 74-26 LiF-BeF2 mol%), FLiBe-LaF3, and FLiBe-UF4. These potentials are used in molecular dynamics simulations to obtain temperature- and composition-dependent transport properties (density, viscosity, thermal conductivity, and isobaric heat capacity), which are mapped as inputs to a three-dimensional FE model of the experimental thermal loop. The FE model with literature transport properties reproduces the experimental heat-transfer behavior of pure FLiBe to within 10% in the laminar regime and 18% in the transitional and turbulent regimes, validating the end-to-end pipeline for this composition. The same model with MTP-MD-derived transport properties systematically overestimates the heat-transfer coefficient by 25-28%, an offset consistent with the MTP-MD biases on thermal conductivity and viscosity. Applied to the ternary systems FLiBe-LaF3 and FLiBe-UF4 over 0-5 mol%, the MTP-MD-driven FE model predicts a mean reduction in heat-transfer efficiency of 8-11% relative to pure FLiBe, with UF4 exhibiting the strongest effect. The qualitative ordering of the three systems is the more robust result; the absolute value of the 8-11% figure is contingent on the MTP accuracy. The framework is complementary to high-temperature experiments and provides a physics-based pathway for the rapid screening of MSR coolant formulations.
