IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipbs0360544222020527.html
   My bibliography  Save this article

Influence of operational parameters on the performance of Tesla turbines: Experimental investigation of a small-scale turbine

Author

Listed:
  • Thomazoni, André Luis Ribeiro
  • Ermel, Conrado
  • Schneider, Paulo Smith
  • Vieira, Lara Werncke
  • Hunt, Julian David
  • Ferreira, Sandro Barros
  • Rech, Charles
  • Gouvêa, Vinicius Santorum

Abstract

Tesla turbines can be employed as small-scale turbines to recover waste energy in several industrial applications. However, there is no consensus on the turbine efficiency as experimental studies show significantly lower values than those obtained by analytical and CFD approaches. The present work addresses that question by performing a systematic literature review (SLR) on Tesla turbines, comparing the efficiency values reported by experimental and simulation works. To validate the SLR findings an experimental small-scale air driven Tesla turbine was built. The Design of Experiments (DoE) methodology was applied to understand the effects of selected independent variables on the turbine output power and mechanical efficiency. Inlet air pressure, temperature, and rotational speed were chosen as controllable factors of a Central Composite Design applied to the prototype of < 1 kW output power. The results indicate a 5% efficiency increase when inlet pressure increases 1 bar, on average. In the SLR, the average efficiency of 40%–60% was reported by simulation works, while experimental articles reported maximum efficiencies of 20%, on average. The experimental turbine analyzed in this paper presented a maximum efficiency of 14.2% ± 0.4% at 3 barg and 4,000 rpm, agreeing with other experimental studies.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222020527
    DOI: 10.1016/j.energy.2022.125159
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222020527
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.125159?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. 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. Ngo, Thanh-An & Kim, Jinsoo & Kim, Seung-Soo, 2014. "Characteristics of palm bark pyrolysis experiment oriented by central composite rotatable design," Energy, Elsevier, vol. 66(C), pages 7-12.
    3. Kim, Chul Kyu & Yoon, Joon Yong, 2016. "Performance analysis of bladeless jet propulsion micro-steam turbine for micro-CHP (combined heat and power) systems utilizing low-grade heat sources," Energy, Elsevier, vol. 101(C), pages 411-420.
    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. Savic, Ivana M. & Savic, Ivan M. & Stojiljkovic, Stanisa T. & Gajic, Dragoljub G., 2014. "Modeling and optimization of energy-efficient procedures for removing lead(II) and zinc(II) ions from aqueous solutions using the central composite design," Energy, Elsevier, vol. 77(C), pages 66-72.
    6. Zhao, Dan & Ji, Chenzhen & Teo, C. & Li, Shihuai, 2014. "Performance of small-scale bladeless electromagnetic energy harvesters driven by water or air," Energy, Elsevier, vol. 74(C), pages 99-108.
    7. Yabin Liu & Lei Tan & Binbin Wang, 2018. "A Review of Tip Clearance in Propeller, Pump and Turbine," Energies, MDPI, vol. 11(9), pages 1-30, August.
    8. Yahya Sheikhnejad & João Simões & Nelson Martins, 2020. "Energy Harvesting by a Novel Substitution for Expansion Valves: Special Focus on City Gate Stations of High-Pressure Natural Gas Pipelines," Energies, MDPI, vol. 13(4), pages 1-18, February.
    9. Wenjiao Qi & Qinghua Deng & Yu Jiang & Qi Yuan & Zhenping Feng, 2018. "Disc Thickness and Spacing Distance Impacts on Flow Characteristics of Multichannel Tesla Turbines," Energies, MDPI, vol. 12(1), pages 1-25, December.
    10. Nunes, Matheus M. & Brasil Junior, Antonio C.P. & Oliveira, Taygoara F., 2020. "Systematic review of diffuser-augmented horizontal-axis turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    11. Manfrida, G. & Pacini, L. & Talluri, L., 2018. "An upgraded Tesla turbine concept for ORC applications," Energy, Elsevier, vol. 158(C), pages 33-40.
    12. 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).
    13. 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.
    14. Bonjean Stanton, Muriel C. & Dessai, Suraje & Paavola, Jouni, 2016. "A systematic review of the impacts of climate variability and change on electricity systems in Europe," Energy, Elsevier, vol. 109(C), pages 1148-1159.
    15. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    16. 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.
    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. Cheng, Hongzhi & Li, Ziliang & Duan, Penghao & Lu, Xingen & Zhao, Shengfeng & Zhang, Yanfeng, 2023. "Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles," Applied Energy, Elsevier, vol. 352(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. 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).
    3. 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).
    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. Rusin, K. & Wróblewski, W. & Rulik, S., 2021. "Efficiency based optimization of a Tesla turbine," Energy, Elsevier, vol. 236(C).
    6. Lisheng Pan & Huaixin Wang, 2019. "Experimental Investigation on Performance of an Organic Rankine Cycle System Integrated with a Radial Flow Turbine," Energies, MDPI, vol. 12(4), pages 1-20, February.
    7. Sun, Hongjun & Yang, Zhen & Li, Jinxia & Ding, Hongbing & Lv, Pengfei, 2024. "Performance evaluation and optimal design for passive turbulence control-based hydrokinetic energy harvester using EWM-based TOPSIS," Energy, Elsevier, vol. 298(C).
    8. Huang, Zhenwei & Huang, Zhenyou & Fan, Honggang, 2020. "Influence of C groove on energy performance and noise source of a NACA0009 hydrofoil with tip clearance," Renewable Energy, Elsevier, vol. 159(C), pages 726-735.
    9. Burillo, Daniel & Chester, Mikhail V. & Pincetl, Stephanie & Fournier, Eric, 2019. "Electricity infrastructure vulnerabilities due to long-term growth and extreme heat from climate change in Los Angeles County," Energy Policy, Elsevier, vol. 128(C), pages 943-953.
    10. Shi, Guangtai & Liu, Zongku & Xiao, Yexiang & Li, Helin & Liu, Xiaobing, 2020. "Tip leakage vortex trajectory and dynamics in a multiphase pump at off-design condition," Renewable Energy, Elsevier, vol. 150(C), pages 703-711.
    11. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    12. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    13. Zhang, Zhiqing & Dong, Rui & Tan, Dongli & Duan, Lin & Jiang, Feng & Yao, Xiaoxue & Yang, Dixin & Hu, Jingyi & Zhang, Jian & Zhong, Weihuang & Zhao, Ziheng, 2023. "Effect of structural parameters on diesel particulate filter trapping performance of heavy-duty diesel engines based on grey correlation analysis," Energy, Elsevier, vol. 271(C).
    14. Wenjiao Qi & Qinghua Deng & Yu Jiang & Qi Yuan & Zhenping Feng, 2018. "Disc Thickness and Spacing Distance Impacts on Flow Characteristics of Multichannel Tesla Turbines," Energies, MDPI, vol. 12(1), pages 1-25, December.
    15. Zhou, Zhiyong & Qin, Weiyang & Zhu, Pei, 2017. "Harvesting acoustic energy by coherence resonance of a bi-stable piezoelectric harvester," Energy, Elsevier, vol. 126(C), pages 527-534.
    16. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    17. Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    18. 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.
    19. Xin Li & Fengrong Bi & Lipeng Zhang & Xiao Yang & Guichang Zhang, 2022. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer," Energies, MDPI, vol. 15(3), pages 1-17, February.
    20. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.

    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:261:y:2022:i:pb:s0360544222020527. 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.