PyRC

Resistance-capacity (RC) networks bridge the gap between simple, imprecise table calculations and complex, detailed computational fluid dynamics (CFD). PyRC adds the possibility to create and simulate such RC networks using Python, focusing on automation and solving dynamic systems. The Python package will soon be published as an open-source project (pyrc.de). It has been tested on thermal networks with variable boundary conditions (using e.g. Deutscher Wetterdienst (DWD) weather data), as well as on networks involving radiation and constant and variable mass flows between nodes. However, it should also be possible to simulate electrical networks, provided that the class handling induction is added first.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-02
DOI
https://doi.org/10.5281/zenodo.18832971
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

PyRC

J. Kimmich, Tim Christopher Pascal Jourdan
Zenodo (CERN European Organization for Nuclear Research)
Integrated Energy Systems Optimization
article

PyRC

J. Kimmich, Tim Christopher Pascal Jourdan
article en

Abstract

Resistance-capacity (RC) networks bridge the gap between simple, imprecise table calculations and complex, detailed computational fluid dynamics (CFD). PyRC adds the possibility to create and simulate such RC networks using Python, focusing on automation and solving dynamic systems. The Python package will soon be published as an open-source project (pyrc.de). It has been tested on thermal networks with variable boundary conditions (using e.g. Deutscher Wetterdienst (DWD) weather data), as well as on networks involving radiation and constant and variable mass flows between nodes. However, it should also be possible to simulate electrical networks, provided that the class handling induction is added first.

Zenodo (CERN European Organization for Nuclear Research)
University of Stuttgart (DE)
Openalex Percentile: Top 40%
Integrated Energy Systems Optimization
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