Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models

For machining processes, such as drilling, grinding, and cutting, fluids play a crucial role for lubrication and cooling. For adequately describing such processes, robust models for the thermophysical properties of the fluids are a prerequisite. In the contact zone, extreme conditions prevail, e.g. regarding temperature and pressure. As thermophysical property data at such conditions are presently often not available, predictive and physical models are required. Molecular-based equations of state (EOS) are attractive candidates as they provide a favorable trade-off between computational speed and predictive capabilities. Yet, without experimental data, it is not trivial to assess the physical reliability of a given EOS model. In this work, Brown’s characteristic curves are used to assess molecular-based fluid models. Brown’s characteristic curves provide general limits that are to be satisfied such that a given model is thermodynamically consistent. Moreover, a novel approach was developed, which uses pseudo-experimental data obtained from molecular simulations using high-accurate force fields. The method is generalized in a way that it can be applied to different force field types, e.g. model potentials and complex real substances. The method was validated based on the (scarcely) available data in the literature. Based on this pseudo-experimental data, different thermodynamic EOS models were assessed. Only the SAFT-VR Mie EOS is found to yield thermodynamically consistent results in all cases. Thereby, robust EOS models were identified that can be used for reliably modeling cutting fluids at extreme conditions, e.g. in machining processes.