In elastohydrodynamic lubrication (EHL), the lubricant experiences pressures in excess of 500 MPa and strain rates larger than \(10^5\) \(\text{s}^{-1}\). The high pressures lead to a dramatic rise in the Newtonian viscosity and the high rates lead to large shear stresses and pronounced shear thinning. The extraction of accurate rheological properties using non-equilibrium molecular dynamics simulations (NEMD) has played a key role in improving our understanding of lubricant flow in EHL conditions. However, the high dimensionality of the output data generated by NEMD simulations often makes a deeper interrogation of the link between molecular-scale features and rheological properties challenging. In this paper, we explore the use of machine learning to analyze and visualize the high-dimensional output data generated in typical NEMD simulations. We show that dimension reduction of NEMD simulation data describing the shear flow of squalane enables a clear visualization of the transition from Newtonian to non-Newtonian shear thinning with increasing shear rate and provides a reliable assessment of the correlation between shear thinning and the evolution in molecular alignment. The end-to-end atom pairs dominate the largest variations in pair orientation tensor components for low-pressure systems (0.1, 100 MPa) and provide the clearest separation of the orientation tensors with rate. On the other hand, the side atom pairs dominate the largest variation in the tensor components for the high-pressure systems (\(P\ge 400\) MPa) which exhibit an overall limited evolution in orientation tensors as a function of rate. Dimension reduction using all the six components of the orientation tensors of all 435 pairs associated with a squalane molecule shows that the decrease in viscosity with rate for low pressures is strongly correlated with changes in molecular alignment. However, for high pressures, shear thinning occurs at saturated orientational order.

在弹性流体动力润滑 (EHL) 中，润滑剂承受超过 500 MPa 的压力和大于\(10^5\)  \(\text{s}^{-1}\). 高压导致牛顿粘度急剧上升，而高速率导致大剪切应力和显着的剪切稀化。使用非平衡分子动力学模拟 (NEMD) 提取准确的流变特性在提高我们对 EHL 条件下润滑剂流动的理解方面发挥了关键作用。然而，由 NEMD 模拟生成的输出数据的高维度通常使得对分子尺度特征和流变特性之间的联系进行更深入的询问具有挑战性。在本文中，我们探索使用机器学习来分析和可视化典型 NEMD 模拟中生成的高维输出数据。我们表明，描述角鲨烷剪切流的 NEMD 模拟数据的降维可以清楚地显示随着剪切速率增加从牛顿剪切稀化到非牛顿剪切稀化的转变，并提供了对剪切稀化与分子演化之间相关性的可靠评估。结盟。端到端原子对在低压系统 (0.1, 100 MPa) 的对取向张量分量的最大变化中占主导地位，并提供最清晰的取向张量与速率分离。另一方面，侧原子对在高压系统的张量分量中占主导地位（端到端原子对在低压系统 (0.1, 100 MPa) 的对取向张量分量的最大变化中占主导地位，并提供最清晰的取向张量与速率分离。另一方面，侧原子对在高压系统的张量分量中占主导地位（端到端原子对在低压系统 (0.1, 100 MPa) 的对取向张量分量的最大变化中占主导地位，并提供最清晰的取向张量与速率分离。另一方面，对于高压系统，侧原子对占张量分量最大的变化（\(P\ge 400\) MPa) 表现出作为速率函数的取向张量的整体有限演变。使用与角鲨烷分子相关的所有 435 对取向张量的所有六个分量进行降维显示，粘度随低压速率的降低与分子排列的变化密切相关。然而，对于高压，剪切变稀发生在饱和取向顺序。