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

Author

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  • Mohammadsadegh Pahlavanzadeh

    (Department of Power Engineering and Turbomachinery, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Krzysztof Rusin

    (Department of Power Engineering and Turbomachinery, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Włodzimierz Wróblewski

    (Department of Power Engineering and Turbomachinery, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

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.

Suggested Citation

  • Mohammadsadegh Pahlavanzadeh & Krzysztof Rusin & Włodzimierz Wróblewski, 2025. "Roughness Modeling Using a Porous Medium Layer in a Tesla Turbine Operating with ORC Fluids," Energies, MDPI, vol. 18(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4990-:d:1753551
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    References listed on IDEAS

    as
    1. Pacini, Leonardo & Ciappi, Lorenzo & Talluri, Lorenzo & Fiaschi, Daniele & Manfrida, Giampaolo & Smolka, Jacek, 2020. "Computational investigation of partial admission effects on the flow field of a tesla turbine for ORC applications," Energy, Elsevier, vol. 212(C).
    2. Rusin, K. & Wróblewski, W. & Rulik, S., 2021. "Efficiency based optimization of a Tesla turbine," Energy, Elsevier, vol. 236(C).
    3. Mohammadsadegh Pahlavanzadeh & Włodzimierz Wróblewski & Krzysztof Rusin, 2024. "On the Flow in the Gap between Corotating Disks of Tesla Turbine with Different Supply Configurations: A Numerical Study," Energies, MDPI, vol. 17(17), pages 1-19, September.
    4. Ciappi, L. & Fiaschi, D. & Niknam, P.H. & Talluri, L., 2019. "Computational investigation of the flow inside a Tesla turbine rotor," Energy, Elsevier, vol. 173(C), pages 207-217.
    5. Thomazoni, André Luis Ribeiro & Ermel, Conrado & Schneider, Paulo Smith & Vieira, Lara Werncke & Hunt, Julian David & Ferreira, Sandro Barros & Rech, Charles & Gouvêa, Vinicius Santorum, 2022. "Influence of operational parameters on the performance of Tesla turbines: Experimental investigation of a small-scale turbine," Energy, Elsevier, vol. 261(PB).
    6. Manfrida, G. & Pacini, L. & Talluri, L., 2018. "An upgraded Tesla turbine concept for ORC applications," Energy, Elsevier, vol. 158(C), pages 33-40.
    7. Talluri, Lorenzo & Dumont, Olivier & Manfrida, Giampaolo & Lemort, Vincent & Fiaschi, Daniele, 2020. "Geometry definition and performance assessment of Tesla turbines for ORC," Energy, Elsevier, vol. 211(C).
    8. Talluri, L. & Fiaschi, D. & Neri, G. & Ciappi, L., 2018. "Design and optimization of a Tesla turbine for ORC applications," Applied Energy, Elsevier, vol. 226(C), pages 300-319.
    9. Krzysztof Rusin & Włodzimierz Wróblewski & Sebastian Rulik & Mirosław Majkut & Michał Strozik, 2021. "Performance Study of a Bladeless Microturbine," Energies, MDPI, vol. 14(13), pages 1-18, June.
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