Chu Circuit Models as Minimum Energy Representations of Wave Impedances

The recoverable energy of spherical wave impedances and their circuit realizations are studied. Results show that the ladder networks proposed by Chu are minimum energy representations, with recoverable energy exactly equal to the stored energy. This equivalence is demonstrated analytically and studied in detail for a single low-order mode, then generalized for higher order modes. Transient calculations using heuristic recovery sources for the lowest-order mode provide a ground-truth verification. The demonstrated equivalence closes the open problem as to whether Chu circuits overestimate the energy stored by spherical wave impedances.

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Publication Details

Journal
IEEE Transactions on Antennas and Propagation
Published
2025-02-26
DOI
https://doi.org/10.1109/tap.2025.3543041
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Chu Circuit Models as Minimum Energy Representations of Wave Impedances

Lukáš Jelínek, Kurt Schab
IEEE Transactions on Antennas and Propagation
Neural Networks and Applications
article

Chu Circuit Models as Minimum Energy Representations of Wave Impedances

Lukáš Jelínek, Kurt Schab
article en

Abstract

The recoverable energy of spherical wave impedances and their circuit realizations are studied. Results show that the ladder networks proposed by Chu are minimum energy representations, with recoverable energy exactly equal to the stored energy. This equivalence is demonstrated analytically and studied in detail for a single low-order mode, then generalized for higher order modes. Transient calculations using heuristic recovery sources for the lowest-order mode provide a ground-truth verification. The demonstrated equivalence closes the open problem as to whether Chu circuits overestimate the energy stored by spherical wave impedances.

IEEE Transactions on Antennas and PropagationVol. 73(6)
Santa Clara University (US), Czech Technical University in Prague (CZ)
Grantová Agentura České Republiky
Affordable and clean energy
Openalex Percentile: Top 99%
Neural Networks and Applications
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