Computational Fluid Dynamics (CFD)

The problems and demands in process-simulation are far beyond a simple description of the flow- and temperature field. More often than not, the mass-transport, thermodynamics and reaction kinetics have to be taken into consideration, as demonstrated in the process of hot-dip galvanizing [Mataln2015].

In other fields, additional physical phenomena occur, which have to be modelled, like the electro-deposition process. In this case, not only the flow-field and the mass-diffusion of the species, but also the effects of the electric field and migration of ions have to be calculated.

The accurate simulation and description of multi-phase flows plays an important role in process simulation in order to enhance quality, efficiency and security of production processes. Depending on the inherent process characteristics, different phases (gaseous, fluid, solid) co-exist. Physical and chemical processes like mixing, solubility, heat- and mass-transfer can occur, and have to be modelled adequately.   

An example of a successful application of multi-phase simulation is the optimization of high-speed wiping nozzles in the continuous hot-dip galvanizing process. The height of the liquid zinc coating is reduced employing a transonic gas-jet. In order to predict the height, a detailed modelling with LES-VOF technique is required, which includes the interaction between the high-speed gas flow and the liquid zinc film. A detailed knowledge of this interaction offers the opportunity to optimize process parameters with respect to uniform coating thickness [Pfeiler2014,Pfeiler2015].

Another application example of multi-phase modelling are spray-processes, like cooling of components or spraying of etching or coating chemicals. The correct modelling of the surface interactions (e.g. wetting of a surface, phase-changes, heat-transfer, chemical reactions …) is of the utmost importance to get reliable results.

Current state-of-the-art X-ray computed tomography (CT) scans are capable of measuring detailed porous micro-structures. The data can be reconstructed into a computational grid, using adequate segmentation algorithms. This offers the opportunity to simulate physical phenomena on the micro-meter scale and to foster the understanding of the underlying process.

An example is the computation of the gas-diffusion through the porous metallic support of a metal-supported solid oxide fuel cell (SOFC). The effective mass-transport of hydrogen could be determined in the measured porous substrate [Reiss2015a]. In addition to that, a corrosion model is implemented which is capable of determining the growth of an oxide layer during operation (see Figure 5). The decreased pore size hinders the gas-diffusion and reduces the efficiency of the SOFC. With this knowledge a prediction of the mass-transfer related losses due to corrosion could be determined [Reiss2015b].