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Turbocharger turbine rotor tip leakage loss and mass flow model valid up to extreme off-design conditions with high blade to jet speed ratio

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  • Serrano, José Ramón
  • Navarro, Roberto
  • García-Cuevas, Luis Miguel
  • Inhestern, Lukas Benjamin

Abstract

Due to the power consumption restriction of the turbocharger compressor, common turbine maps are rather narrow. To extrapolate them, reliable physical submodels are needed that are valid for broad ranges. Plenty of research has been done referring to tip leakage losses in axial and traditional radial turbomachinery. However, less effort has been put into the tip leakage analysis of radial turbocharger turbines, whose characteristics including high rotational speed and geometry are rather different. Commonly developed tip leakage loss models in radial turbines are mainly based on correlations with the rotational speed, while in axial turbomachinery they are mainly based on blade loading assumptions. Wide range computational fluid dynamics (CFD) data of a medium sized automotive turbine have been used to analyze tip leakage mass flow under extremely diverse running conditions. To be able to fit a model in a broad range of the map, blade loading and rotational speed have to be considered. A novel tip clearance model has been derived from the Navier Stokes Equations. The model owns a dependency on the rotational speed and the blade loading. With this approach CFD data have been fitted in a very good quality to model the tip leakage mass flow rate and tip leakage losses.

Suggested Citation

  • Serrano, José Ramón & Navarro, Roberto & García-Cuevas, Luis Miguel & Inhestern, Lukas Benjamin, 2018. "Turbocharger turbine rotor tip leakage loss and mass flow model valid up to extreme off-design conditions with high blade to jet speed ratio," Energy, Elsevier, vol. 147(C), pages 1299-1310.
  • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:1299-1310
    DOI: 10.1016/j.energy.2018.01.083
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    References listed on IDEAS

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    1. Serrano, José Ramón & Tiseira, Andrés & García-Cuevas, Luis Miguel & Inhestern, Lukas Benjamin & Tartoussi, Hadi, 2017. "Radial turbine performance measurement under extreme off-design conditions," Energy, Elsevier, vol. 125(C), pages 72-84.
    2. Galindo, J. & Fajardo, P. & Navarro, R. & García-Cuevas, L.M., 2013. "Characterization of a radial turbocharger turbine in pulsating flow by means of CFD and its application to engine modeling," Applied Energy, Elsevier, vol. 103(C), pages 116-127.
    3. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng, 2015. "Modeling and extrapolating mass flow characteristics of a radial turbocharger turbine," Energy, Elsevier, vol. 87(C), pages 628-637.
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    Cited by:

    1. Wei, Jiangshan & Xue, Yingxian & Yang, Mingyang & Deng, Kangyao & Wang, Cuicui & Wu, Xintao, 2021. "A reduced-order model of twin-entry nozzleless radial turbine based on flow characteristics," Energy, Elsevier, vol. 214(C).
    2. Serrano, José Ramón & Piqueras, Pedro & De la Morena, Joaquín & Gómez-Vilanova, Alejandro & Guilain, Stéphane, 2021. "Methodological analysis of variable geometry turbine technology impact on the performance of highly downsized spark-ignition engines," Energy, Elsevier, vol. 215(PB).
    3. José Galindo & Andrés Tiseira & Roberto Navarro & Lukas Benjamin Inhestern & Juan David Echavarría, 2022. "Numerical Analysis of the Effects of Different Rotor Tip Gaps in a Radial Turbine Operating at High Pressure Ratios Reaching Choked Flow," Energies, MDPI, vol. 15(24), pages 1-30, December.
    4. Tüchler, Stefan & Chen, Zhihang & Copeland, Colin D., 2018. "Multipoint shape optimisation of an automotive radial compressor using a coupled computational fluid dynamics and genetic algorithm approach," Energy, Elsevier, vol. 165(PA), pages 543-561.
    5. Xue, Yingxian & Yang, Mingyang & Martinez-Botas, Ricardo F. & Romagnoli, Alessandro & Deng, Kangyao, 2019. "Loss analysis of a mix-flow turbine with nozzled twin-entry volute at different admissions," Energy, Elsevier, vol. 166(C), pages 775-788.
    6. Rong Huang & Jimin Ni & Houchuan Fan & Xiuyong Shi & Qiwei Wang, 2023. "Investigating a New Method-Based Internal Joint Operation Law for Optimizing the Performance of a Turbocharger Compressor," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    7. Wang, Zhiqi & Xie, Baoqi & Xia, Xiaoxia & Yang, Huya & Zuo, Qingsong & Liu, Zhipeng, 2022. "Energy loss of radial inflow turbine for organic Rankine cycle using mixture based on entropy production method," Energy, Elsevier, vol. 245(C).
    8. Serrano, José Ramón & Arnau, Francisco José & García-Cuevas, Luis Miguel & Inhestern, Lukas Benjamin, 2019. "An innovative losses model for efficiency map fitting of vaneless and variable vaned radial turbines extrapolating towards extreme off-design conditions," Energy, Elsevier, vol. 180(C), pages 626-639.

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