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An insight into the similarity approach to predict the maximum efficiency of organic Rankine cycle turbines

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  • Masi, Massimo
  • Da Lio, Luca
  • Lazzaretto, Andrea

Abstract

This work deals with the prediction of the maximum efficiency achievable by turbines for Organic Rankine Cycle (ORC) systems by means of similarity principles. At present, this is a key topic in the preliminary design optimization procedures of these systems. The dimensional analysis applied to the most general ORC turbines scenario helps obtain the functional relationship between maximum turbine efficiency and the most relevant design variables within the general framework of the flow similarity.

Suggested Citation

  • Masi, Massimo & Da Lio, Luca & Lazzaretto, Andrea, 2020. "An insight into the similarity approach to predict the maximum efficiency of organic Rankine cycle turbines," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220303856
    DOI: 10.1016/j.energy.2020.117278
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    References listed on IDEAS

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    1. Yu, Haoshui & Feng, Xiao & Wang, Yufei, 2015. "A new pinch based method for simultaneous selection of working fluid and operating conditions in an ORC (Organic Rankine Cycle) recovering waste heat," Energy, Elsevier, vol. 90(P1), pages 36-46.
    2. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2014. "New efficiency charts for the optimum design of axial flow turbines for organic Rankine cycles," Energy, Elsevier, vol. 77(C), pages 447-459.
    3. Manente, Giovanni & Da Lio, Luca & Lazzaretto, Andrea, 2016. "Influence of axial turbine efficiency maps on the performance of subcritical and supercritical Organic Rankine Cycle systems," Energy, Elsevier, vol. 107(C), pages 761-772.
    4. Maraver, Daniel & Royo, Javier & Lemort, Vincent & Quoilin, Sylvain, 2014. "Systematic optimization of subcritical and transcritical organic Rankine cycles (ORCs) constrained by technical parameters in multiple applications," Applied Energy, Elsevier, vol. 117(C), pages 11-29.
    5. Roberto Capata & Enrico Sciubba, 2015. "Experimental Fitting of the Re-Scaled Balje Maps for Low-Reynolds Radial Turbomachinery," Energies, MDPI, vol. 8(8), pages 1-15, July.
    6. 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.
    7. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2016. "Predicting the optimum design of single stage axial expanders in ORC systems: Is there a single efficiency map for different working fluids?," Applied Energy, Elsevier, vol. 167(C), pages 44-58.
    8. Vivian, Jacopo & Manente, Giovanni & Lazzaretto, Andrea, 2015. "A general framework to select working fluid and configuration of ORCs for low-to-medium temperature heat sources," Applied Energy, Elsevier, vol. 156(C), pages 727-746.
    9. Mounier, Violette & Olmedo, Luis Eric & Schiffmann, Jürg, 2018. "Small scale radial inflow turbine performance and pre-design maps for Organic Rankine Cycles," Energy, Elsevier, vol. 143(C), pages 1072-1084.
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    Cited by:

    1. Jankowski, Marcin & Klonowicz, Piotr & Borsukiewicz, Aleksandra, 2021. "Multi-objective optimization of an ORC power plant using one-dimensional design of a radial-inflow turbine with backswept rotor blades," Energy, Elsevier, vol. 237(C).

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