{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-fast-and-robust-recipe-for-modeling-non","title":"A fast and robust recipe for modeling non-ideal MHD effects in star-formation simulations","arxiv_id":"2502.06933","date":"2025-02-10","proceeding":null,"authors":["E. Agianoglou","A. Tritsis","K. Tassis"],"abstract":"Non-ideal MHD effects are thought to be a crucial component of the star-formation process. Numerically, several complications render the study of non-ideal MHD effects in 3D simulations extremely challenging and hinder our efforts of exploring a large parameter space. We aim to overcome such challenges by proposing a novel, physically-motivated empirical approximation to model non-ideal MHD effects. We perform a number of 2D axisymmetric 3-fluid non-ideal MHD simulations of collapsing prestellar cores and clouds with non-equilibrium chemistry and leverage upon previously-published results. We utilize these simulations to develop a multivariate interpolating function to predict the ionization fraction in each region of the cloud depending on the local physical conditions. We subsequently use analytically-derived, simplified expressions to calculate the resistivities of the cloud in each grid cell. Therefore, in our new approach the resistivities are calculated without the use of a chemical network. We benchmark our method against additional 2D axisymmetric non-ideal MHD simulations with random initial conditions and a 3D non-ideal MHD simulation with non-equilibrium chemistry. We find excellent quantitative and qualitative agreement between our approach and the \"full\" non-ideal MHD simulations both in terms of the spatial structure of the simulated clouds and regarding their time evolution. We achieve a factor of 100-1000 increase in computational speed. Given that we ignore the contribution of grains, our approximation is valid up to number densities of 10^6 cm^(-3) and is therefore suitable for pc-scale simulations of molecular clouds. The tabulated data required for integrating our method in hydrodynamical codes, along with a fortran implementation of the interpolating function are publicly available at https://github.com/manosagian/Non-Ideal-MHD-Approximate-Code.","url_abs":"https://arxiv.org/abs/2502.06933v1","url_pdf":"https://arxiv.org/pdf/2502.06933v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-fast-and-robust-recipe-for-modeling-non","repo_url":"https://github.com/manosagian/non-ideal-mhd-approximate-code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}