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

J C S Kadupitiya,Vikram Jadhao

doi:10.1007/s11249-021-01457-3

Abstract

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 (Pge 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.

Highlights

Many liquids when sheared exhibit a decrease in viscosity with the rise in shear strain rate [1, 2]
We explore the use of Machine learning (ML) tools such as dimension reduction methods to analyze and visualize the data generated in typical non-equilibrium molecular dynamics simulations (NEMD) simulations
We utilize the information encoded in all 435 pairs as well as all the six non-trivial components of the pair orientation tensors to make a complete assessment of the correlation between strain rate, pressure, and molecular alignment in squalane via dimension reduction methods

Summary

Introduction

Many liquids when sheared exhibit a decrease in viscosity with the rise in shear strain rate [1, 2]. This shear thinning behavior depends on many factors such as thermodynamic conditions, strain rates, and the molecular structure of the liquid. Shear thinning and the scaling of the Newtonian viscosity with temperature and pressure are critical to the performance of liquids in many practical applications. In elastohydrodynamic lubrication (EHL), high sliding velocities of solid machine components produce strain rates > 105 s−1 and substantial shear thinning of lubricant films, which lowers the frictional stress [5,6,7]. The high normal loads compress the lubricant in EHL contacts to pressures in excess of 500 MPa, which increases the Newtonian viscosity of the lubricant by orders of magnitude, preventing squeeze out and limiting wear

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Conclusion