基于图形神经网络的颗粒雪崩中的动态量预测
Ling Zhang1, Jianfeng Chen1, Hang Zhang1
1School of Automation, Central South University, Changsha 410083, China.
The Journal of chemical physics
|December 1, 2023
概括
我们使用在初始粒子结构上训练的图形神经网络准确预测3D颗粒雪崩动态. 这种方法揭示了颗粒流中的强烈相关性,进步了我们对复杂颗粒介质的理解.
科学领域:
- 颗粒状材料的物理学 颗粒状材料的物理学
- 计算物理学的计算物理.
- 复杂系统的动态 复杂系统的动态
背景情况:
- 旋转桶中的颗粒雪崩对于理解颗粒介质,工业过程和地质危险至关重要.
- 尽管进行了广泛的研究,但由于当前技术的局限性,准确预测雪崩动态仍然具有挑战性.
- 了解颗粒流动行为在科学和工业领域是必不可少的.
研究的目的:
- 准确预测三维 (3D) 颗粒雪崩的动态特征.
- 使用图形神经网络 (GNN) 仅从初始静态微观结构来预测雪崩动态.
- 证明GNN在揭示颗粒雪崩的基本物理学的稳定性和预测能力.
主要方法:
- 使用图形神经网络 (GNN) 来分析3D颗粒材料的初始静态微结构.
- 训练GNN来预测雪崩的动态特征,而无需直接模拟粒子运动.
- 验证GNN对相互作用潜力的变化,大小多分散性和粒子坐标噪声的稳定性.
主要成果:
- 基于初始微观结构的GNN使用3D颗粒雪崩动态特征的准确预测.
- 在各种模型参数中证明了GNN方法的稳定性.
- 在3D颗粒雪崩系统中确定了全球速度和全球速度波动之间的强烈相关性.
结论:
- 图形神经网络提供了一个强大的工具,可以从静态配置中预测颗粒性的雪崩动态.
- 该GNN方法提供了对控制颗粒流的基本物理学的见解.
- 该方法在理解无形系统和改进涉及颗粒材料的工业流程方面具有潜在的应用.
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