IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v236y2021ics0360544221016960.html
   My bibliography  Save this article

Efficiency based optimization of a Tesla turbine

Author

Listed:
  • Rusin, K.
  • Wróblewski, W.
  • Rulik, S.

Abstract

Tesla turbine is a bladeless, radial turbine which might be suitable for the applications in various energy systems, e.g. in Organic Rankine Cycle or combined heat and power systems if its efficiency improves. The investigations presented in the paper concern efficiency based numerical optimization of a Tesla turbine. Parameters subjected to the optimization process are inlet nozzle height, inter-disc gap, nozzle angle, pressure and rotational velocity. The optimization was conducted using response surface methodology. The first stage of the optimization excluded pressure ratio as neutral to efficiency. The second stage of the optimization allowed determination of the most important parameters: tangential velocity ratio, partial admission coefficient, reaction degree and aspect ratio required for maximization of the turbine's efficiency. Efficiency was improved almost twice with the respect to the nominal model (from 9% to 17%) and validated experimentally. Performance characteristics of the optimized turbine model were calculated using computational fluid dynamics software.

Suggested Citation

  • Rusin, K. & Wróblewski, W. & Rulik, S., 2021. "Efficiency based optimization of a Tesla turbine," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016960
    DOI: 10.1016/j.energy.2021.121448
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221016960
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121448?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Wenjiao Qi & Qinghua Deng & Zhinan Chi & Lehao Hu & Qi Yuan & Zhenping Feng, 2019. "Influence of Disc Tip Geometry on the Aerodynamic Performance and Flow Characteristics of Multichannel Tesla Turbines," Energies, MDPI, vol. 12(3), pages 1-23, February.
    3. P. Sebastiani & H. P. Wynn, 2000. "Maximum entropy sampling and optimal Bayesian experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 145-157.
    4. Moradi, Ramin & Habib, Emanuele & Bocci, Enrico & Cioccolanti, Luca, 2020. "Investigation on the use of a novel regenerative flow turbine in a micro-scale Organic Rankine Cycle unit," Energy, Elsevier, vol. 210(C).
    5. Fiaschi, Daniele & Manfrida, Giampaolo & Maraschiello, Francesco, 2015. "Design and performance prediction of radial ORC turboexpanders," Applied Energy, Elsevier, vol. 138(C), pages 517-532.
    6. Weiß, Andreas P. & Novotný, Václav & Popp, Tobias & Streit, Philipp & Špale, Jan & Zinn, Gerd & Kolovratník, Michal, 2020. "Customized ORC micro turbo-expanders - From 1D design to modular construction kit and prospects of additive manufacturing," Energy, Elsevier, vol. 209(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Singer, Gerald & Köll, Rebekka & Aichhorn, Lukas & Pertl, Patrick & Trattner, Alexander, 2023. "Utilizing hydrogen pressure energy by expansion machines – PEM fuel cells in mobile and other potential applications," Applied Energy, Elsevier, vol. 343(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Enhua Wang & Ningjian Peng, 2023. "A Review on the Preliminary Design of Axial and Radial Turbines for Small-Scale Organic Rankine Cycle," Energies, MDPI, vol. 16(8), pages 1-20, April.
    3. 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).
    4. 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).
    5. Hessa Al-Thani & Jon Lee, 2020. "An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data," SN Operations Research Forum, Springer, vol. 1(3), pages 1-21, September.
    6. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    7. Gaetano Morgese & Francesco Fornarelli & Paolo Oresta & Tommaso Capurso & Michele Stefanizzi & Sergio M. Camporeale & Marco Torresi, 2020. "Fast Design Procedure for Turboexpanders in Pressure Energy Recovery Applications," Energies, MDPI, vol. 13(14), pages 1-26, July.
    8. Al Jubori, Ayad M. & Al-Dadah, Raya & Mahmoud, Saad, 2017. "Performance enhancement of a small-scale organic Rankine cycle radial-inflow turbine through multi-objective optimization algorithm," Energy, Elsevier, vol. 131(C), pages 297-311.
    9. Braimakis, Konstantinos & Karellas, Sotirios, 2017. "Integrated thermoeconomic optimization of standard and regenerative ORC for different heat source types and capacities," Energy, Elsevier, vol. 121(C), pages 570-598.
    10. Abbas Aghagoli & Mikhail Sorin & Mohammed Khennich, 2022. "Exergy Efficiency and COP Improvement of a CO 2 Transcritical Heat Pump System by Replacing an Expansion Valve with a Tesla Turbine," Energies, MDPI, vol. 15(14), pages 1-16, July.
    11. Witanowski, Ł. & Klonowicz, P. & Lampart, P. & Suchocki, T. & Jędrzejewski, Ł. & Zaniewski, D. & Klimaszewski, P., 2020. "Optimization of an axial turbine for a small scale ORC waste heat recovery system," Energy, Elsevier, vol. 205(C).
    12. Weiß, Andreas P. & Novotný, Václav & Popp, Tobias & Streit, Philipp & Špale, Jan & Zinn, Gerd & Kolovratník, Michal, 2020. "Customized ORC micro turbo-expanders - From 1D design to modular construction kit and prospects of additive manufacturing," Energy, Elsevier, vol. 209(C).
    13. Davide Di Battista & Roberto Cipollone, 2023. "Waste Energy Recovery and Valorization in Internal Combustion Engines for Transportation," Energies, MDPI, vol. 16(8), pages 1-28, April.
    14. Rodriguez, Sergio & Ludkovski, Michael, 2020. "Probabilistic bisection with spatial metamodels," European Journal of Operational Research, Elsevier, vol. 286(2), pages 588-603.
    15. Ssebabi, B. & Dobson, R.T. & Sebitosi, A.B., 2015. "Characterising a turbine for application in an organic Rankine cycle," Energy, Elsevier, vol. 93(P2), pages 1617-1632.
    16. Witanowski, Łukasz & Ziółkowski, Paweł & Klonowicz, Piotr & Lampart, Piotr, 2023. "A hybrid approach to optimization of radial inflow turbine with principal component analysis," Energy, Elsevier, vol. 272(C).
    17. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2017. "A mean-line model to predict the design efficiency of radial inflow turbines in organic Rankine cycle (ORC) systems," Applied Energy, Elsevier, vol. 205(C), pages 187-209.
    18. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
    19. Andrea Meroni & Angelo La Seta & Jesper Graa Andreasen & Leonardo Pierobon & Giacomo Persico & Fredrik Haglind, 2016. "Combined Turbine and Cycle Optimization for Organic Rankine Cycle Power Systems—Part A: Turbine Model," Energies, MDPI, vol. 9(5), pages 1-15, April.
    20. Kim, Taejin & Lee, Gueseok & Youn, Byeng D., 2019. "PHM experimental design for effective state separation using Jensen–Shannon divergence," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.