Integration of thermodynamic library data into OpenFOAM for the simulation of compressible multiphase flows with phase change

In many industries, steam is used to clean free surfaces and sterilize equipment and products. Steam is an environmentally friendly alternative to cleaning with chemicals. Through a combination of high temperature and high pressure, it effectively loosens dirt and contaminants from surfaces by breaking bonds. Because it has smaller molecules than water, it can penetrate surface pores even better. During application, the steam comes into contact with the normally much colder environment and eventually condenses. The condensate contains the dissolved dirt and inactivated organisms, which are then removed from the solid surfaces with water or by wiping. This research project focuses on improved modeling and simulation of steam applications as a representative example of compressible multicom- ponent flows with temperature-driven phase change. To account for the dynamic changes in material properties due to pressure and temperature variations during application, thermodynamic library data will be integrated into a computational fluid dynamics (CFD) code. By using real thermodynamic library data in combination with machine learning, an equation of state is no longer required to close the sys- tem of transport equations, and the temperature can be iteratively determined from the solution of the conservation equations at each time step in a straightforward manner. In addition, optical visualization experiments will be performed with a customized superheated steam injection system to investigate for the first time the full velocity and temperature fields in condensing steam flows and to verify the predictability of the developed solver. We intend to make the developed solver available to the CFD community as an open-source tool.