球床反应堆高保真CFD模拟的超分辨率初始化
Super-Resolution Initialization of High-Fidelity CFD Simulations for Pebble-Bed Reactors
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中文总结 AI 辅助
本研究提出用超分辨率图神经网络（SR-GNN）改进球床反应堆高阶NekRS CFD模拟的初始化，可降低计算成本，在不同雷诺数及更大球床几何上均展现良好适用性。
中文摘要 AI 辅助
高阶计算流体动力学（CFD）模拟可详细解析球床反应堆内非均匀的间隙流动，但其计算成本高昂，尤其是在达到统计稳态所需的初始流动发展阶段。本研究探讨使用超分辨率图神经网络（SR-GNN）改进高阶NekRS模拟的初始化：以低阶P=2速度场为输入，重构高阶表示，将其作为P=7重启模拟的初始条件。该方法在含146个球的球床中，针对雷诺数Re=1000、Re=2500、Re=5000三种工况进行评估，以压降收敛为主要性能指标。SR-GNN模型通过配对的低阶、高阶快照训练，首先通过定性推理对比进行评估。高阶重启模拟结果显示，对于Re=1000和Re=2500，SR-GNN初始化的案例产生的压降历史与从真实P=2场直接重启的结果相似；而对于Re=5000，超分辨率场重启比直接P=2重启及从均匀速度场初始化的参考P=7模拟，更快接近统计稳态的P=7压降范围。此外，将训练好的Re=5000模型应用于更大的含1568个球的球床，验证了该工作流程在显著更大的填充床几何结构上的定性适用性。这些结果表明，基于SR-GNN的初始化是降低高阶流动发展成本的有前景策略，同时也推动了在更宽雷诺数范围及几何泛化方面的进一步研究。
英文摘要
High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the initial flow-development period required to reach statistically stationary conditions. This work investigates the use of a Super-Resolution Graph Neural Network (SR-GNN) to improve the initialization of high-order NekRS simulations. Lower-order P = 2 velocity fields are used as inputs to reconstruct higher-order representations, which are then used as initial conditions for P = 7 restart simulations. The approach is evaluated using a 146-pebble bed at Re = 1000, Re = 2500, and Re = 5000, with pressure-drop convergence used as the main figure of merit. The SR-GNN models were trained using paired low- and high-order snapshots and were first evaluated through qualitative inference comparisons. High-order restart simulations showed that, for Re = 1000 and Re = 2500, the SR-GNN initialized cases produced pressure-drop histories similar to direct restarts from true P = 2 fields. For Re = 5000, however, the super-resolved field restart approached the statistically stationary P = 7 pressure-drop range faster than both the direct P = 2 restart and the reference P = 7 simulation initialized from a uniform velocity field. The trained Re = 5000 model was also applied to a larger 1568-pebble bed, demonstrating qualitative applicability of the workflow to a significantly larger packed-bed geometry. These results indicate that SR-GNN-based initialization is a promising strategy for reducing high-order flow-development cost, while also motivating further work on broader Reynolds-number and geometry generalization.
发表机构
- The Pennsylvania State University（宾夕法尼亚州立大学）
- Argonne National Laboratory（阿贡国家实验室）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。
