Probing the Rheological Properties of Liquids Under Conditions of Elastohydrodynamic Lubrication Using Simulations and Machine Learning

Abstract

In elastohydrodynamic lubrication (EHL), the lubricant experiences pressures in excess of 500 MPa and strain rates larger than $$10^5$$ 10 5 $$\text{s}^{-1}$$ 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 ($$P\ge 400$$ P ≥ 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.

Prediction of transport property via machine learning molecular movements

“…Considering that the inverse of the positive and negative sign between the two correlation coefficients is due to the weight parameter for PC1, the viscosity is presumably determined just by PC1. While the recent study showed that stress tensors from NEMD simulations can show the viscosity, 41 our results suggests that the viscosity is presumed similarly from the molecular movements.…”

Machine learning surrogates for molecular dynamics simulations of soft materials

“…Recent years have seen a surge in the integration of machine learning (ML) methods with simulations to reduce their computational costs, enhance their predictive power, and expedite the analysis of high-dimensional output data [5]- [19]. ML has been used to develop efficient force fields [10]- [12], reduce high-dimensional simulation data to isolate molecularlevel mechanisms [7], [13], and develop surrogates to accurately predict simulation outcomes and expedite the exploration of the material design space [14]- [17]. The last set of applications, where surrogates are designed for learning the relationship between the input variables and simulation outputs, is the subject of this paper.…”

A review of advances in tribology in 2020–2021

“…In EHL, lubricants are subjected to pressures of more than 500 MPa and strain rates of more than 10 5 s -1 [873]. High pressure results in a sharp rise in Newtonian | https://mc03.manuscriptcentral.com/friction viscosity, while high velocity results in large shear stress and significant shear thinning, which bring challenges to the design of engineering moving parts working in this regime.…”

“…Kadupitiya and Jadhao [873] used non-equilibrium molecular dynamics (NEMD) simulations to extract accurate rheological properties of lubricants under EHL conditions, and innovatively used machine learning to analyze and visualize the high-dimensional results generated in output data of typical NEMD simulations (Fig. 58(a)).…”

“…Kajita et al [877] proposed an Fig. 58 Molecular dynamics and machine learning methods in EHL simulation [873,877]. Reproduced with permission from Ref.…”