Many application-relevant fluids exhibit shear thinning, where viscosity decreases with shear rate above some critical shear rate. For hydraulic fluids formulated with polymeric additives, the critical shear rate is a function of the molecular weight and concentration of the polymers. Here we present a model for predicting the critical shear rate and Newtonian viscosity of fluids, with the goal of identifying a fluid that shear thins in a specific range relevant to hydraulic pumps. The model is applied to predict the properties of fluids comprising polyisobutene polymer and polyalphaolefin base oil. The theoretical predictions are validated by comparison to viscosities obtained from experimental measurements and molecular dynamics simulations across many decades of shear rates. Results demonstrate that the molecular weight of the polymer plays a key role in determining the critical shear rate, whereas the concentration of polymer primarily affects the Newtonian viscosity. The simulations are further used to show the molecular origins of shear thinning and critical shear rate. The atomistic simulations and simple model developed in this work can ultimately be used to formulate polymer-enhanced fluids with ideal shear thinning profiles that maximize the efficiency of hydraulic systems.

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.Graphical Abstract

The ASTM D7721 standard practice for determining the effect of fluid selection on hydraulic system performance was applied to the study of two excavators and a hydraulic dynamometer. Six fully formulated fluids spanning two viscosity grades and three viscosity index (VI) levels were evaluated using the dynamometer. Four of these fluids were also evaluated in the excavators. A common monograde ISO VG 46 hydraulic oil served as the reference fluid in both studies. In dynamometer testing, high-VI, low-traction fluids reduced hydraulic motor torque losses by 9 to 35 N·m. In excavator trials, multigrade hydraulic fluids shortened the average bucket filling time by 5.1% and increased the average bucket payload by 2.8%. Based on the physical properties of the test fluids and fundamental principles of fluid mechanics, the observed performance gains are believed to be the result of reduced viscous shear, boundary friction and traction losses. The results presented explore correlations between fluid properties and hydraulic system performance, enabling formulation strategies that can help reduce parasitic losses and improve machine productivity.