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http://hdl.handle.net/20.500.12386/34847
Title: | Exploring the Formation of Resistive Pseudodisks with the GPU Code Astaroth | Authors: | Väisälä, Miikka S. Shang, Hsien GALLI, Daniele Lizano, Susana Krasnopolsky, Ruben |
Issue Date: | 2023 | Journal: | THE ASTROPHYSICAL JOURNAL | Number: | 959 | Issue: | 1 | First Page: | 32 | Abstract: | Pseudodisks are dense structures formed perpendicular to the direction of the magnetic field during the gravitational collapse of a molecular cloud core. Numerical simulations of the formation of pseudodisks are usually computationally expensive with conventional CPU codes. To demonstrate the proof of concept of a fast computing method for this numerically costly problem, we explore the GPU-powered MHD code Astaroth, a sixth-order finite difference method with low adjustable finite resistivity implemented with sink particles. The formation of pseudodisks is physically and numerically robust and can be achieved with a simple and clean setup for this newly adopted numerical approach for science verification. The method’s potential is illustrated by evidencing the dependence on the initial magnetic field strength of specific physical features accompanying the formation of pseudodisks, e.g., the occurrence of infall shocks and the variable behavior of the mass and magnetic flux accreted on the central object. As a performance test, we measure both weak and strong scaling of our implementation to find the most efficient way to use the code on a multi-GPU system. Once suitable physics and problem-specific implementations are realized, the GPU-accelerated code is an efficient option for 3D magnetized collapse problems. | URI: | http://hdl.handle.net/20.500.12386/34847 | URL: | https://iopscience.iop.org/article/10.3847/1538-4357/acfb00 | ISSN: | 0004-637X | DOI: | 10.3847/1538-4357/acfb00 | Bibcode ADS: | 2023ApJ...959...32V | Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista |
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File | Description | Size | Format | |
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Väisälä_2023_ApJ_959_32.pdf | Pdf editoriale | 6.06 MB | Adobe PDF | View/Open |
Väisälä_2023_preprint.pdf | preprint | 6.69 MB | Adobe PDF | View/Open |
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