Issue |
A&A
Volume 691, November 2024
|
|
---|---|---|
Article Number | A71 | |
Number of page(s) | 13 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202348614 | |
Published online | 30 October 2024 |
VENICE: A multi-scale operator-splitting algorithm for multi-physics simulations
Leiden Observatory, University of Leiden,
Niels Bohrweg 2,
2333
CA
Leiden,
The Netherlands
e-mail: wilhelm@strw.leidenuniv.nl
★ Corresponding author; spz@strw.leidenuniv.nl
Received:
15
November
2023
Accepted:
31
July
2024
Context. We present VENICE, an operator-splitting algorithm to integrate a numerical model on a hierarchy of timescales.
Aims. VENICE allows a wide variety of different physical processes operating on different scales to be coupled on individual and adaptive time-steps. It therewith mediates the development of complex multi-scale and multi-physics simulation environments with a wide variety of independent components.
Methods. The coupling between various physical models and scales is dynamic, and realised through (Strang) operators splitting using adaptive time-steps.
Results. We demonstrate the functionality and performance of this algorithm using astrophysical models of a stellar cluster, first coupling gravitational dynamics and stellar evolution, then coupling internal gravitational dynamics with dynamics within a galactic background potential, and finally combining these models while also introducing dwarf galaxy-like perturbers. These tests show numerical convergence for decreasing coupling timescales, demonstrate how VENICE can improve the performance of a simulation by shortening coupling timescales when appropriate, and provide a case study of how VENICE can be used to gradually build up and tune a complex multi-physics model. Although the examples provided here couple dedicated numerical models, VENICE can also be used to efficiently solve systems of stiff differential equations.
Key words: methods: numerical
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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