基于多相多体耗散粒子动力学与PINNs数据驱动发现的CMAS液滴部分润湿特性表征
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
AI总结：
本研究针对高温应用中CMAS熔融砂液滴的润湿难题，结合多相mDPD模拟、PINN框架、符号回归与B-PINNs不确定性量化，揭示了高粘度CMAS液滴的铺展动力学规律。
摘要：
由氧化钙、氧化镁、氧化铝和硅酸盐组成的熔融砂混合物被称为CMAS，其特点是具有高粘度、高密度和高表面张力。CMAS的独特特性使其在高温应用中成为一种难以应对的材料，需要创新的解决方案和材料来防止其堆积并损坏关键设备。本文中，我们使用多相多体耗散粒子动力学（mDPD）模拟来研究高粘度熔融CMAS液滴的润湿动力学。模拟在三维空间中开展，设置了不同的初始液滴尺寸和平衡接触角。我们提出了一个粗粒度参数化常微分方程（ODE），该方程可刻画CMAS液滴的铺展半径变化行为。随后基于物理信息神经网络（Physics-Informed Neural Network，PINN）框架识别该ODE的参数。接着，利用符号回归给出了PINN发现的参数值关于初始半径和接触角的解析依赖关系。最后，我们采用贝叶斯PINNs（B-PINNs）来评估并量化与所发现参数相关的不确定性。简言之，本研究通过融合简单的参数化ODE建模与前沿机器学习技术，揭示了CMAS液滴的铺展动力学特性。
英文摘要：
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a coarse parametric ordinary differential equation (ODE) that captures the spreading radius behavior of the CMAS droplets. The ODE parameters are then identified based on the Physics-Informed Neural Network (PINN) framework. Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modeling and state-of-the-art machine learning techniques.
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