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Accelerated Method for Simulating the Solidification Microstructure of Continuous Casting Billets on GPUs.

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Title: Accelerated Method for Simulating the Solidification Microstructure of Continuous Casting Billets on GPUs.
Authors: Wang, Jingjing1 (AUTHOR), Liu, Xiaoyu2 (AUTHOR) liuxiaoyu@ise.neu.edu.cn, Li, Yuxin1 (AUTHOR), Mao, Ruina1,2 (AUTHOR)
Source: Materials (1996-1944). May2025, Vol. 18 Issue 9, p1955. 17p.
Subjects: Continuous casting, Dendritic crystals, Parallel algorithms, Cellular automata, Solidification
Abstract: Microstructure simulations of continuous casting billets are vital for understanding solidification mechanisms and optimizing process parameters. However, the commonly used CA (Cellular Automaton) model is limited by grid anisotropy, which affects the accuracy of dendrite morphology simulations. While the DCSA (Decentered Square Algorithm) reduces anisotropy, its high computational cost due to the use of fine grids and dynamic liquid/solid interface tracking hinders large-scale applications. To address this, we propose a high-performance CA-DCSA method on GPUs (Graphic Processing Units). The CA-DCSA algorithm is first refactored and implemented on a CPU–GPU heterogeneous architecture for efficient acceleration. Subsequently, key optimizations, including memory access management and warp divergence reduction, are proposed to enhance GPU utilization. Finally, simulated results are validated through industrial experiments, with relative errors of 2.5% (equiaxed crystal ratio) and 2.3% (average secondary dendrite arm spacing) in 65# steel, and 2.1% and 0.7% in 60# steel. The maximum temperature difference in 65# steel is 1.8 °C. Compared to the serial implementation, the GPU-accelerated method achieves a 1430× higher speed using two GPUs. This work has provided a powerful tool for detailed microstructure observation and process parameter optimization in continuous casting billets. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Accelerated Method for Simulating the Solidification Microstructure of Continuous Casting Billets on GPUs.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Jingjing%22">Wang, Jingjing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaoyu%22">Liu, Xiaoyu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> liuxiaoyu@ise.neu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Yuxin%22">Li, Yuxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mao%2C+Ruina%22">Mao, Ruina</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. May2025, Vol. 18 Issue 9, p1955. 17p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Continuous+casting%22">Continuous casting</searchLink><br /><searchLink fieldCode="DE" term="%22Dendritic+crystals%22">Dendritic crystals</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+automata%22">Cellular automata</searchLink><br /><searchLink fieldCode="DE" term="%22Solidification%22">Solidification</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Microstructure simulations of continuous casting billets are vital for understanding solidification mechanisms and optimizing process parameters. However, the commonly used CA (Cellular Automaton) model is limited by grid anisotropy, which affects the accuracy of dendrite morphology simulations. While the DCSA (Decentered Square Algorithm) reduces anisotropy, its high computational cost due to the use of fine grids and dynamic liquid/solid interface tracking hinders large-scale applications. To address this, we propose a high-performance CA-DCSA method on GPUs (Graphic Processing Units). The CA-DCSA algorithm is first refactored and implemented on a CPU–GPU heterogeneous architecture for efficient acceleration. Subsequently, key optimizations, including memory access management and warp divergence reduction, are proposed to enhance GPU utilization. Finally, simulated results are validated through industrial experiments, with relative errors of 2.5% (equiaxed crystal ratio) and 2.3% (average secondary dendrite arm spacing) in 65# steel, and 2.1% and 0.7% in 60# steel. The maximum temperature difference in 65# steel is 1.8 °C. Compared to the serial implementation, the GPU-accelerated method achieves a 1430× higher speed using two GPUs. This work has provided a powerful tool for detailed microstructure observation and process parameter optimization in continuous casting billets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/ma18091955
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 1955
    Subjects:
      – SubjectFull: Continuous casting
        Type: general
      – SubjectFull: Dendritic crystals
        Type: general
      – SubjectFull: Parallel algorithms
        Type: general
      – SubjectFull: Cellular automata
        Type: general
      – SubjectFull: Solidification
        Type: general
    Titles:
      – TitleFull: Accelerated Method for Simulating the Solidification Microstructure of Continuous Casting Billets on GPUs.
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            NameFull: Wang, Jingjing
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            NameFull: Liu, Xiaoyu
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            NameFull: Li, Yuxin
      – PersonEntity:
          Name:
            NameFull: Mao, Ruina
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          Dates:
            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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              Value: 18
            – Type: issue
              Value: 9
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            – TitleFull: Materials (1996-1944)
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