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arXiv 1702.06871physics.plasm-ph
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等离子体诊断与验证的动态模式分解

Dynamic Mode Decomposition for Plasma Diagnostics and Validation

Roy Taylor, J. Nathan Kutz, Kyle Morgan, Brian Nelson

摘要:

我们展示了应用动态模式分解(DMD)的诊断分析的非线性动力学的磁化等离子体的电阻磁流体力学。DMD方法是一种理想的时空矩阵分解,其将计算或实验数据的空间特征相关联,同时将空间活动与周期性时间行为相关联。DMD可以产生低秩、降阶的代理模型,这些模型可以用于重建系统的状态并产生高保真的未来状态预测。这允许减少计算成本,并且,在同一时间,精确的近似的问题,即使数据是稀疏采样。我们演示了使用的方法的数值和实验数据,表明它是一个成功的数学架构,为特征的HIT-IS磁流体动力学。重要的是,DMD分解产生可解释的主导模式结构,包括spheromak模式和一对注入器驱动模式。组合起来,3模式DMD模型产生可用于未来状态预测和/或控制策略的优秀动态重建。

英文摘要:

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system and produce high-fidelity future state predictions. This allows for a reduction in the computational cost, and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the HIT-IS magnetohydrodynamics. Importantly, the DMD decomposition produces interpretable, dominant mode structures, including the spheromak mode and a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions that can be used for future state prediction and/or control strategies.

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