This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, 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 non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage 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.

A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels / De Stefano, Marco; Grivet-Talocia, Stefano; Wendt, Torben; Yang, Cheng; Schuster, Christian. - In: IEEE TRANSACTIONS ON COMPONENTS, PACKAGING, AND MANUFACTURING TECHNOLOGY. - ISSN 2156-3950. - STAMPA. - 11:3(2021), pp. 471-484. [10.1109/TCPMT.2021.3056746]

A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels

De Stefano, Marco;Grivet-Talocia, Stefano;
2021

Abstract

This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, 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 non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2869792