ArticleThe journal of physical chemistry. B2023
Computing Viscosities of Mixtures of Ester-Based Lubricants at Different Temperatures.
Article in The journal of physical chemistry. B, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Benchmarking machine learning and molecular dynamics methods for the prediction of the thermophysical properties of organic liquids.Digital discovery · 2026Article
- Using multiple linear regression to predict engine oil life.Scientific reports · 2025Article
- Calculating shear viscosity with confined non-equilibrium molecular dynamics: a case study on hematite - PAO-2 lubricant.RSC advances · 2023Article
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Authors and funding
10 authors.
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Abstract
Synthetic esters are used as lubricants for applications at high temperatures, but their development can be a trial and error process. In this context, molecular dynamics simulations could be used as a tool to investigate the properties of new lubricants, in particular viscosity. We employ nonequilibrium molecular dynamics (NEMD) simulations to predict bulk Newtonian viscosities of a set of mixtures of two esters, di(2-ethylhexyl) sebacate (DEHS) and di(2-ethylhexyl) adipate (DEHA) at 293 and 343 K as well as equilibrium molecular dynamics (EMD) and NEMD at 393 K and compare these to experimental measurements. The simulations predict mixture densities within 5% of the experimental values, and we are able to retrieve between 99% and 75% of the experimental viscosities for all ranges of temperature. Experimental viscosities show a linear trend which we are able to capture using NEMD at low temperature and EMD at high temperature. Our work shows that, using EMD and NEMD simulations, and the workflows we developed, we can obtain reliable estimates of the viscosities of mixtures of industrially relevant ester-based lubricants at different temperatures.
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