AI 中文总结
本研究提出结合主成分分析的浅层循环解码器（SHRED），实现了MHD液态金属流的实时状态估计，在0.075-0.300 T磁场、5-30度倾斜角下误差约5%，可用于聚变反应堆包层监测。
AI 中文摘要
磁流体动力学（MHD）流的状态估计对托卡马克聚变反应堆中液态金属包层的实时监测至关重要。由于这些现象的多物理场特性，高保真模拟在实时应用中存在计算瓶颈。本研究探讨了一种数据驱动的降阶模型框架：浅层循环解码器（SHRED）结合主成分分析，用于将稀疏温度测量值映射到整个热液压系统的状态。本研究的主要贡献在于对代表DEMO增殖包层构型的全三维区域进行双参数分析。此处，流体受到方向和强度变化的外部磁场作用，并受到两个作为水冷系统的圆柱体阻碍，这些圆柱体在其表面施加温度边界条件。这种双参数磁场变化会引发流体动力学的非线性转变，从低磁场强度下的混沌行为过渡到高磁场强度下的层流状态，其特征是形成倾斜角为30度的非对称侧层。SHRED对温度、压力和速度场的重构保持了约5%的平均相对误差。该精度在弱磁场（0.075 T）到强磁场（0.300 T），以及倾斜角5度至30度（反映其主导的环形分量）范围内均得以维持。这些误差仅略大于低秩截断所决定的下限误差。研究结果证实，SHRED可作为复杂且实际工程应用中可靠的状态估计器，适用于完全未知的参数场景，并验证其为适用于实际设施在线监测与控制的精确实时状态估计技术。
英文摘要
State estimation in magnetohydrodynamic flows is critical for real-time monitoring of liquid metal blankets in tokamak fusion reactors. Due to the multiphysics nature of these phenomena, high-fidelity simulations are computationally prohibitive for real-time applications. This work investigates a data- driven Reduced Order Model framework: the Shallow Recurrent Decoder (SHRED) coupled with Principal Component Analysis, to map sparse temperature measurements to the full thermo-hydraulic system's state. The major contribution of this work lies in the two-parameter analysis of a fully three-dimensional domain representative of the DEMO breeding blanket configuration. Here, the flow is subjected to an external magnetic field varying in direction and intensity and is hindered by two cylinders acting as a water-cooling system, which impose a temperature boundary condition on their surfaces. This double-parametric magnetic variation induces nonlinear transitions in the flow dynamics, ranging from chaotic behavior at low magnetic field intensities to laminarized regimes at high intensities, characterized by the formation of asymmetric side layers at an inclination angle of 30 degrees. SHRED reconstruction maintains a mean relative error of approximately 5% for the temperature, pressure, and velocity fields. This accuracy is maintained across both weak and strong magnetic fields, ranging from 0.075 T to 0.300 T, and for inclination angles from 5 to 30 degrees, reflecting its dominant toroidal component. These errors are only slightly larger than the lower error bound dictated by low-rank truncation. The results establish SHRED as a reliable state estimator for complex and realistic engineering applications involving completely unseen parametric scenarios and validate it as an accurate real-time state estimation technique suitable for online monitoring and control of real facilities.
