UN中晶粒生长的模型：整合分子动力学、相场建模与不确定性量化
A model of grain growth in UN integrating molecular dynamics, phase-field modeling, and uncertainty quantification
- North Carolina State University（北卡罗来纳州立大学）
- UNSW Sydney（新南威尔士大学悉尼分校）
- Idaho National Laboratory（爱达荷国家实验室）
- Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究整合分子动力学、相场建模与不确定性量化，构建了UN晶粒生长的多尺度框架，提取了本征晶界迁移率参数，并识别出迁移率前因子和激活能是影响输出的关键不确定性来源。
AI中文摘要：
通过一个整合分子动力学（MD）、相场建模和代理辅助不确定性量化的多尺度框架，研究了氮化铀（UN）中的晶粒生长动力学和晶界（GB）性质。MD模拟得到了27个对称倾斜晶界在0–2000 K下的晶界能，这些结果与可用的DFT值一致。平均晶界能在1000 K以下几乎与温度无关，在更高温度下则增加。将一种机理性的孔隙拖拽模型应用于唯一可用的锕系氮化物晶粒生长数据集，得到迁移率降低因子$s \approx 0.93$–$0.99$，在统计上与1不可区分，证实了在实验条件下孔隙拖拽可忽略不计。因此，本征晶界迁移率直接从有效迁移率中提取，得到$M_0 = 2.05\times10^{-15}$ m$^4$/(J$\cdot$s)和$Q_M = 0.89$ eV。在1500–2000 K下进行的相场模拟证实了正常的曲率驱动晶粒生长，晶粒尺寸分布收敛于Hillert型形式。一种结合主成分分析、高斯过程回归和Sobol分解的代理辅助全局敏感性分析表明，迁移率前因子$M_0$在所有时间点主导输出方差，其次是激活能$Q_M$，而晶界能$\gamma$贡献最小。这些结果为UN建立了第一个定量晶粒生长框架，并将减少$M_0$和$Q_M$的不确定性确定为未来实验工作的最高优先目标。
英文摘要：
Grain growth kinetics and grain-boundary (GB) properties in uranium mononitride (UN) are investigated through an integrated multiscale framework combining molecular dynamics (MD), phase-field modeling, and surrogate-assisted uncertainty quantification. MD simulations yield GB energies for 27 symmetric tilt boundaries from 0--2000~K, which are consistent with available DFT values. The average GB energy is nearly temperature-independent below 1000~K and increases at higher temperatures. A mechanistic pore-drag model applied to the only available grain growth dataset for actinide nitrides yields a mobility reduction factor of $s \approx 0.93$--$0.99$, statistically indistinguishable from unity, confirming that pore drag is negligible under the experimental conditions. The intrinsic GB mobility is therefore extracted directly from the effective mobility, yielding $M_0 = 2.05\times10^{-15}$~m$^4$/(J$\cdot$s) and $Q_M = 0.89$~eV. Phase-field simulations conducted from 1500--2000~K confirm normal curvature-driven grain growth, with grain size distributions converging to the Hillert-like form. A surrogate-assisted global sensitivity analysis---combining principal component analysis, Gaussian process regression, and Sobol decomposition---reveals that the mobility prefactor $M_0$ dominates output variance at all times, followed by the activation energy $Q_M$, while the GB energy $γ$ contributes minimally. These results establish the first quantitative grain growth framework for UN and identify the reduction of uncertainty in $M_0$ and $Q_M$ as the highest-priority target for future experimental efforts.
