Dataset: Benchmarking of Massively Parallel Phase-Field Codes for Directional Solidification




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1,430
433.52 GB
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0
in dataset
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0
documented
Views
262
total views
Downloads
5
total downloads
Published
Feb 17, 2026
6 months ago

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1430 files across 6 type(s)

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Published
Published: 6 months ago
Views
262
Downloads
5
Total Size
433.52 GB
Description

This dataset contains the data and metadata for the benchmark simulations described in Sections 4.1 - 4.4 of the manuscript titled "Benchmarking of Massively Parallel Phase-Field Codes for Directional Solidification" by Jiefu Tian, David Montiel, Kaihua Ji, Trevor Lyons, Jason Landini, Katsuyo Thornton, and Alain Karma. Submitted to Computational Materials Science.

For information about how the data is organized, please refer to the README.md file.

More details, including the DOI of the article will be added upon publication.

Binary 5 Image 304 Text 573 Unknown 525 Video 4 Zipfile 19
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Funding
This research was supported by the National Aeronautics and Space Administration (NASA) under award number 80NSSC24K0466, as part of the project titled Computational Modeling of Columnar-Equiaxed Alloy Solidification MicroStructures (COMPASS). The project is administered through the Science Mission Directorate: Biological and Physical Sciences, with funding to Northeastern University. Additional support was provided by the U.S. Department of Energy Office of Basic Energy Sciences Division of Materials Science and Engineering under Award DE-SC0008637 as part of the Center for PRedictive Integrated Structural Materials Science (PRISMS). Computational resources were provided by the Research Computing team at Northeastern University, including access to the Discovery cluster hosting NVIDIA V100-SXM2 GPUs and our dedicated private compute node. In addition, this work used the Anvil supercomputer at the Rosen Center for Advanced Computing in Purdue University through allocation MSS160003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.