On the Flow in the Gap between Corotating Disks of Tesla Turbine with Different Supply Configurations: A Numerical Study

Abstract

Momentum diffusion and kinetic energy transfer in turbomachinery have always been significant issues, with a considerable impact on the performance of the bladeless Tesla turbine. This radial turbine shows high potential for various energy applications, such as Organic Rankine Cycle or combined heat and power systems. Analyzing the flow inside the gap between the corotating disks of the Tesla turbine presents challenges due to several factors, including submillimeter length scales, variations in flow cross-section, interactions of body forces arising from rotation with turbulence, interactions between the turbine’s inlet nozzles and rotor, and moving walls. General design parameters, e.g., number of nozzles, also pose a challenge in order to achieve the full potential of this turbine. In this research, two different variants of the supply system are considered with six and forty nozzles. To minimize computational expenses, a portion of the entire domain is considered. The flow in each domain, consisting of one inlet nozzle and a segment of one gap between the disks, is examined to reveal the complexity of flow structures and their impact on the Tesla turbine performance. Large Eddy Simulation (LES) with the Smagorinsky subgrid-scale model is used to verify the results of the k-ω Shear-Stress Transport (SST) turbulence model in the first case study with six nozzles. Analyzing the results indicates that the k-ω SST model provides valuable insights with appropriate accuracy. The second case study, with forty nozzles, is simulated using the k-ω SST turbulence model. The research compares flow structure, flow parameters, and their impact on the system’s performance. From the comparison between the k-ω SST turbulence model and LES simulation, it was observed that although the k-ω SST model slightly overestimates the general parameters and damps fluctuations, it still provides valuable insights for assessing flow structures. Additionally, the mesh strategy is described, as the LES requirements make this simulation computationally expensive and time-consuming. The overall benefits of this method are discussed.