COMPARISON OF TWO ROUGHNESS MODELS FOR SIMULATING FLOW IN TESLA TURBINE: MODIFICATION OF THE k-ω SST TURBULENCE MODEL AND POROUS MEDIUM LAYER METHOD

The Tesla turbine is a bladeless turbomachinery whose performance is influenced by momentum diffusion and kinetic energy transfer between its rotating disks and the operating flow. This radial turbine shows significant potential for various energy applications. This research presents a systematic approach to compare two roughness models for simulating flow through the gap between co-rotating disks in a Tesla turbine. The first model involves modifying turbulence parameters based on the equivalent sand-grain height of the roughness. The second model adjusts the Porous Medium Layer (PML) to account for the effects of the actual roughness shape on flow. For the PML model, the thickness of the porous medium layer was assumed to correspond to the maximum roughness height. This study aims to evaluate the effectiveness of these approaches in capturing the impact of surface roughness on turbine performance. The investigation began with the validation of both models against experimental results from a minichannel setup. For the PML model, parameters were adjusted to match the experimentally observed pressure drop. For the turbulence model, parameters were modified based on the equivalent sand grain roughness, set equal to the maximum roughness height. Subsequently, both models were applied to the analysis of the Tesla turbine. The turbulence closure used was the k-ω shear stress transport (SST) model, which had been previously validated against Large Eddy Simulation (LES) results for a case with smooth rotor walls. The models were evaluated by comparing their predictions of roughness effects on turbine efficiency and flow parameters, providing insights into their agreement in simulating Tesla turbine performance. In the first stage, the friction factor predicted by both models at various Reynolds numbers was compared with experimental results. The comparison demonstrated the appropriate performance of both employed models in this context. The subsequent simulation of the Tesla turbine using both roughness models, along with a comparison of general parameters, showed acceptable agreement between the models. This suggests that both approaches are sufficient for simulating more complex test cases. From the observed results and comparative analysis, the PML model stands out for its ability to account for the real shape of surface roughness and its actual effect on flow. This feature enhances the model's reliability, as it avoids dependence on the calculation of equivalent sand grain roughness, reducing potential errors associated with this assumption.