Fluids are central to many industrial products and processes, with applications ranging from upstream oil and gas exploration and production to fuels, additives, and lubricants, as well as pharmaceuticals and drug delivery. Improvements in industrial processes currently rely on accurate modeling of heat and fluid flow, which require reliable data as numerical input. Due to the difficulties of obtaining experimental data of sufficient accuracy over the required ranges of temperature and pressure, fluid performance modelers are increasingly relying on powerful atomistic and multi-scale simulation techniques for the physical and chemical property values they need. The term “multi-scale modeling” comprises varying methodologies and levels of theory, starting at the molecular level and reaching “up” to continuum methods and equations of state.

The specific fluid properties or performance characteristics one needs to understand determine the time and length scales at which simulations must be performed. In turn, the relevant length and time scales dictate the level of theory which must be applied, whether first principles, atomistic, and/or statistical mechanics approaches. By this means, simpler constituent parts of complex systems may be analyzed individually, and the knowledge gained may then be reintegrated to provide insights into the combined system or process.

Applications to organic fluids of industrial relevance abound for properties such as vapor-liquid equilibria (VLE), solubility, surface tension, viscosity, thermal conductivity, and heat capacity. Using efficient simulation workflows, we illustrate the added synergistic value of performing simulations at multiple scales for determining diverse fluid properties. We present efficient protocols for deriving simple, easy-to-use correlations for selected properties based on large simulation datasets. Specific applications are used to illustrate the accuracy, application range, and methodological limits of different approaches. We then offer perspectives and assess development trends.