A Multistage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels
A Multistage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels
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DOI：
10.1109/tcpmt.2021.3056746
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
M. De Stefano;S. Grivet-Talocia;T. Wendt;Cheng Yang;C. Schuster
中科院分区：
文献类型：
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作者：
M. De Stefano;S. Grivet-Talocia;T. Wendt;Cheng Yang;C. Schuster
本文提出了一种分层自适应采样方案，用于大规模线性集总宏模型的无源表征。在本文中，大规模是指动态顺序，特别是输入输出端口的数量。由于计算成本过高，基于相关哈密顿矩阵谱特性的标准无源表征方法要么效率低下，要么不适用于大规模模型。本文基于现有的自适应采样方法，提出了一种混合多级算法，能够利用有限的计算资源检测被动性违规。广泛测试的结果表明，相对于竞争方法，计算要求显着降低。
This article proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. In this article, large scale is intended both in terms of dynamic order and especially number of input–output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or nonapplicable for large-scale models, due to an excessive computational cost. This article builds on existing adaptive sampling methods and proposes a hybrid multistage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches.