Roughness Modeling Using a Porous Medium Layer in a Tesla Turbine Operating with ORC Fluids

The transfer of momentum and kinetic energy is a key factor in turbomachinery performance, particularly influencing the efficiency of the bladeless Tesla turbine, which holds significant potential for applications such as Organic Rankine Cycle (ORC) systems and energy recovery processes. In this study, a comprehensive numerical analysis was carried out to simulate the effects of surface roughness on the flow between the co-rotating disks of a Tesla turbine, using R1234yf and n-hexane as working fluids. To capture roughness effects, a porous medium layer (PML) approach was employed, with porous material parameters adjusted to replicate real roughness behavior. The model was first validated against experimental data for water flow in a minichannel by tuning the PML parameters to match measured pressure drops. In contrast to previous studies, this work applies the PML model to a Tesla turbine operating with organic Rankine cycle (ORC) fluids, where the working medium is changed from air to low-boiling gases. Compared to the air-based cases, the gap between the co-rotating disks is rescaled to smaller dimensions, which introduces additional challenges. Under these conditions, the effective roughness thickness must also be rescaled, and this study investigates how these rescaled roughness effects influence turbine performance using the k-ω shear stress transport (SST) turbulence model combined with the proposed roughness model. Results showed that incorporating the PML roughness model enhances momentum transfer and significantly influences flow characteristics, thereby providing an effective means of simulating Tesla turbine performance under varying roughness conditions.