The driving mode of turbulence in disc galaxy simulations with adaptive mesh refinement
Abstract
Turbulence is a key ingredient in controlling the structure of the interstellar medium (ISM) and star formation, yet we still lack a detailed understanding of its drivers in galaxies. Previous idealised simulations of turbulence employ a stochastic forcing field to drive turbulence. The geometry of this forcing field - whether it is predominantly solenoidal or compressive - is a key parameter governing how turbulence shapes the ISM density distribution and regulates the star formation rate. The turbulence driving parameter (b) quantifies the relative contribution of compressive versus solenoidal driving.. Therefore, accurate knowledge of the driving parameter is essential for understanding and predicting star formation, and for sub-grid modelling of ISM physics and the star formation rate. In this work, we introduce an algorithm to measure the turbulence driving parameter in adaptive mesh refinement (AMR) simulations of galaxies. We focus our analysis on a synthetic Large Magellanic Cloud (LMC), present-day analogue with a total mass M ≈ 1011 M⊙. We find that turbulence is driven primarily solenoidally (b<0.4) within the inner ∼4 kpc of the galaxy, and becomes increasingly compressive with b>0.4 towards the outskirts, R≳4.5 kpc. The volume-weighted median across the disc, b≃0.4, is consistent with the natural mixture of driving modes. We further find that b is weakly correlated with the strength of shear, in that solenoidal driving tends to be associated with regions of higher shear, as expected for the more central parts of galaxies. These trends persist over ∼ 2 Gyr of the galaxy's evolution.
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BibTeX
@misc{watt2026driving,
title = {{The driving mode of turbulence in disc galaxy simulations with adaptive mesh refinement}},
author = {James Watt and Christoph Federrath and Thorsten Tepper-Garcia and Oscar Agertz and Joss Bland-Hawthorn},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15041},
eprint = {2609.15041},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15041},
}
RIS
TY - UNPB
TI - The driving mode of turbulence in disc galaxy simulations with adaptive mesh refinement
AU - James Watt
AU - Christoph Federrath
AU - Thorsten Tepper-Garcia
AU - Oscar Agertz
AU - Joss Bland-Hawthorn
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.15041
UR - https://arxiv.org/abs/2609.15041
AB - Turbulence is a key ingredient in controlling the structure of the interstellar medium (ISM) and star formation, yet we still lack a detailed understanding of its drivers in galaxies. Previous idealised simulations of turbulence employ a stochastic forcing field to drive turbulence. The geometry of this forcing field - whether it is predominantly solenoidal or compressive - is a key parameter governing how turbulence shapes the ISM density distribution and regulates the star formation rate. The turbulence driving parameter (b) quantifies the relative contribution of compressive versus solenoidal driving.. Therefore, accurate knowledge of the driving parameter is essential for understanding and predicting star formation, and for sub-grid modelling of ISM physics and the star formation rate. In this work, we introduce an algorithm to measure the turbulence driving parameter in adaptive mesh refinement (AMR) simulations of galaxies. We focus our analysis on a synthetic Large Magellanic Cloud (LMC), present-day analogue with a total mass M ≈ 10^11 M_⊙. We find that turbulence is driven primarily solenoidally (b<0.4) within the inner ∼4 kpc of the galaxy, and becomes increasingly compressive with b>0.4 towards the outskirts, R≳4.5 kpc. The volume-weighted median across the disc, b≃0.4, is consistent with the natural mixture of driving modes. We further find that b is weakly correlated with the strength of shear, in that solenoidal driving tends to be associated with regions of higher shear, as expected for the more central parts of galaxies. These trends persist over ∼ 2 Gyr of the galaxy's evolution.
ER -