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The first step to designing polymers and base oils with improved performance is to understand the effect of their structure and chemistry on the bulk properties of lubricants. This research aimed to investigate the factors and mechanisms that influence the key bulk properties of lubricants at the molecular level using molecular dynamics (MD) simulations, machine learning (ML), and quantitative-structure-property-relationship (QSPR) modeling. First, the prospect of improving the mechanical efficiency of hydraulic systems by formulating fluids with viscosity modifiers was evaluated in a pump dynamometer. Second, a model for predicting the critical shear rate was developed with the goal of identifying a fluid that shear thins in the key shear rate range in a pump. The model was validated by comparison to viscosities obtained from experimental measurements and MD simulations across many decades of shear rates. Third, the molecular origins of differences in the viscosity index, thickening efficiency, and traction coefficient between fluids were investigated using simulations to quantify and correlating structural properties of the fluid molecules. Fourth, a python package, PyL3dMD, was developed to compute hundreds of dynamic molecular descriptors by post-processing MD simulation trajectories. Finally, the 3D conformations of 305 complex hydrocarbons were associated to their temperature-dependent densities and viscosities using ML-based QSPR models. Overall, this dissertation demonstrated the viability of various techniques in understanding molecular interactions and facilitating design of polymers and base
oils with improved performance.
Pawan Panwar is a Ph.D. candidate in the Department of Mechanical Engineering. He earned his M.S. in engineering and B.S. in mechanical engineering from Milwaukee School of Engineering. He joined Professor Ashlie Martini’s group in January of 2019. His research focuses on studying the lubricating properties of base oils and polymer-enhanced fluids using the molecular dynamics simulations and machine learning. He is particularly interested in the fields of tribology, rheology, fluid power, computational modeling, and machine learning.
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