相关实验视频
Updated: Jul 6, 2025

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The Diffusion of Passive Tracers in Laminar Shear Flow
Published on: May 1, 2018
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模拟和数据驱动模拟米流体的运输特性
Gustavo Chaparro1, Erich A Müller1
1Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
The journal of physical chemistry. B
|January 5, 2024
概括
这项研究使用分子动力学来模拟 Mie 流体的自扩散性和粘度等运输特性. 人工神经网络提供了准确的相关性,有助于力场参数回归.
科学领域:
- 热力学和统计力学的热力学.
- 计算化学和物理计算化学和物理
背景情况:
- 运输特性 (自扩散性,剪切粘度,导热性) 对流体行为至关重要.
- 米流体模型是研究这些特性的一种多功能工具.
- 准确的建模需要广泛的模拟数据跨越各种条件.
研究的目的:
- 计算和建模 Mie 流体的自扩散率,剪切粘度和导热率.
- 使用人工神经网络 (ANN) 开发这些传输特性的准确相关性.
- 调查缩与实证ANN对属性预测的有效性.
主要方法:
- 对米流体进行了平衡分子动力学模拟.
- 在广泛的密度和温度范围内生成了大量的运输特性数据库.
- 人工神经网络使用半经验 (缩量) 和经验 (密度,温度) 两种方法进行训练.
主要成果:
- 创建并验证了每个运输财产的17,000多个数据点的综合数据库.
- 经验ANN,数据规范化 (例如, ρ*D*, ln η*, ln κ*) 和逆温度,表现出优异的性能.
- 单独的透缩放不足以在所有密度上准确建模.
结论:
- 经验ANN在广泛的条件下有效地模拟米流体的运输特性.
- 开发的ANN可以与力场参数回归的状态方程集成.
- 这项工作为预测流体运输特性提供了一个强大的计算框架.
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