Cellular automaton simulation of austenite microstructure evolution in offshore steel during the heating process
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摘要: 为探究海工钢加热过程中奥氏体组织演变规律，基于热激活能、曲率驱动及晶界能量耗散机制，构建了海工钢保温过程中奥氏体晶粒长大的元胞自动机模型，分析了加热温度和保温时间对晶粒长大的影响。结果表明：提高加热温度为晶界迁移提供了更大的驱动力，促进晶界迁移和晶粒间的吞并，奥氏体晶粒尺寸增加；随着加热时间的延长，晶粒不断长大，晶界趋于平滑；建立了海工钢的奥氏体晶粒长大预测模型，得到其晶粒生长指数为0.45，确定了真实时间和模拟时间之间的对应关系。研究结果将对海工钢组织的精准调控提供理论支撑。Abstract: To investigate the evolution law of austenite microstructure in offshore steel during the heating process, this study established a cellular automaton (CA) model for austenitic grain growth in offshore steel during heating based on the mechanisms of thermal activation energy, curvature-driven growth, and grain boundary energy dissipation. The effects of heating temperature and holding time on austenitic grain growth were analyzed. The results indicate that increasing the heating temperature provides a greater driving force for grain boundary migration, promoting grain boundary migration and grain coalescence, leading to an increase in austenite grain size. As heating time increases, the grains continue to grow, and the grain boundaries tend to become smoother. A prediction model for austenite grain growth of offshore steel was established, yielding a grain growth index of 0.45, and the correspondence between real time and simulation time was determined. The findings will provide theoretical support for the precise control of microstructure in offshore steel.
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Key words:
- cellular automaton /
- offshore steel /
- austenitic grain /
- heating temperature /
- holding time
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图 1 模型边界条件与元胞邻居类型
(a)周期性边界条件；(b)Moore型邻居关系
Figure 1. Model boundary conditions and cellular neighbor types
图 2 晶界迁移曲率驱动示意
(a)元胞转变规则2；(b)元胞转变规则3；(c)元胞转变规则4
Figure 2. Schematic diagram of curvature-driven grain boundary migration
图 5 不同加热温度下模拟的海工钢奥氏体晶粒组织演变
Figure 5. Simulation of austenitic grain evolution in offshore steel under different heating temperatures
(a) 1 000 ℃；(b) 1 100 ℃；(c) 1 200 ℃
图 6 晶粒长大过程晶粒数量与模拟步数的关系
Figure 6. Relationship between grain quantity and simulation steps during grain growth process
图 7 不同模拟步数下的奥氏体晶粒尺寸分布
Figure 7. Austenitic grain size distribution under different simulation steps
(a) 2000 CAS；(b) 4000 CAS；(c) 6000 CAS；(d) 8000 CAS
图 8 不同条件下模拟的奥氏体晶粒边数分布
Figure 8. Austenitic grain boundary distribution under different conditions
图 9 奥氏体晶粒尺寸与模拟步数的关系
(a) Dm-S的关系；(b) lnDm-lnS的关系
Figure 9. The relationship between austenite grain sizes and simulation steps
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