Numerical simulation of semi-solid aluminum alloy preparation process by electromagnetic stirring based on dynamic thermophysical parameters

The electromagnetic stirring method has become one of the important methods for producing semi-solid slurries. Researchers typically use numerical models based on constant thermophysical parameters to predict the desired metrics. However, due to the fact that thermophysical parameters vary with temperature, this model leads to deviations in simulation results. Consequently, in this study, a three-dimensional model based on dynamic thermophysical parameters was constructed to simulate the electromagnetic field-velocity field-temperature field during the electromagnetic stirring of semi-solid aluminum alloy melt. Furthermore, the results were compared with those a constant-parameter model. The simulation model was verified through experiments such as magnetic induction strength measurement, temperature measurement, and metallurgical morphology analysis. The results reveal that the fluctuation trend of velocity at monitoring points obtained from the dynamic thermophysical parameters model exhibits better consistency with the actual situation during three-phase alternating-current electromagnetic stirring. Meanwhile, the temperature distribution trend predicted by the dynamic parameter model aligns more closely with the measured data. In addition, at the monitoring points, the simulated curve based on the dynamic thermophysical parameter model demonstrates superior agreement with the experimental measured values.

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Publication Details

Journal
China Foundry
Published
2026-09-05
DOI
https://doi.org/10.1007/s41230-026-5035-3
Primary Topic
Aluminum Alloy Microstructure Properties
Type
article
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Numerical simulation of semi-solid aluminum alloy preparation process by electromagnetic stirring based on dynamic thermophysical parameters

Bo Jiang, 刘娇月 LIU Jiao-yue, Yu Guo, Ye Wang et al.
China Foundry
Aluminum Alloy Microstructure Properties
article

Numerical simulation of semi-solid aluminum alloy preparation process by electromagnetic stirring based on dynamic thermophysical parameters

Bo Jiang, 刘娇月 LIU Jiao-yue, Yu Guo, Ye Wang, Hao Luo, Xun Zhang
article en

Abstract

The electromagnetic stirring method has become one of the important methods for producing semi-solid slurries. Researchers typically use numerical models based on constant thermophysical parameters to predict the desired metrics. However, due to the fact that thermophysical parameters vary with temperature, this model leads to deviations in simulation results. Consequently, in this study, a three-dimensional model based on dynamic thermophysical parameters was constructed to simulate the electromagnetic field-velocity field-temperature field during the electromagnetic stirring of semi-solid aluminum alloy melt. Furthermore, the results were compared with those a constant-parameter model. The simulation model was verified through experiments such as magnetic induction strength measurement, temperature measurement, and metallurgical morphology analysis. The results reveal that the fluctuation trend of velocity at monitoring points obtained from the dynamic thermophysical parameters model exhibits better consistency with the actual situation during three-phase alternating-current electromagnetic stirring. Meanwhile, the temperature distribution trend predicted by the dynamic parameter model aligns more closely with the measured data. In addition, at the monitoring points, the simulated curve based on the dynamic thermophysical parameter model demonstrates superior agreement with the experimental measured values.

China Foundry
Harbin University of Science and Technology (CN), Harbin University (CN), Huzhou Normal University (CN), Harbin Electric Corporation (China) (CN)
Openalex Percentile: Top 7%
Aluminum Alloy Microstructure Properties
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Numerical simulation of semi-solid aluminum alloy preparation process by electromagnetic stirring based on dynamic thermophysical parameters — Bo Jiang, 刘娇月 LIU Jiao-yue, et al. · China Foundry (2026) | TGRS Research Map | TGRS