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Impact of geometric, operational, and model uncertainties on the non-ideal flow through a supersonic ORC turbine cascade

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  • Razaaly, Nassim
  • Persico, Giacomo
  • Congedo, Pietro Marco

Abstract

Typical energy sources for Organic Rankine Cycle (ORC) power systems feature variable heat load and turbine inlet/outlet thermodynamic conditions. The use of organic compounds with heavy molecular weight introduces uncertainties in the fluid thermodynamic modeling. In addition, the peculiarities of organic fluids typically lead to supersonic turbine configurations featuring supersonic flows and shocks, which grow in relevance in the aforementioned off-design conditions; these features also depend strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. This study presents an Uncertainty Quantification (UQ) analysis on a typical supersonic nozzle cascade for ORC applications, by considering a two-dimensional high-fidelity turbulent Computational Fluid Dynamic (CFD) model. Kriging-based techniques are used in order to take into account at a low computational cost, the combined effect of uncertainties associated to operating conditions, fluid parameters, and geometric tolerances. The geometric variability is described by a finite Karhunen-Loeve expansion representing a non-stationary Gaussian random field, entirely defined by a null mean and its autocorrelation function. Several results are illustrated about the ANOVA decomposition of several quantities of interest for different operating conditions, showing the importance of geometric uncertainties on the turbine performances.

Suggested Citation

  • Razaaly, Nassim & Persico, Giacomo & Congedo, Pietro Marco, 2019. "Impact of geometric, operational, and model uncertainties on the non-ideal flow through a supersonic ORC turbine cascade," Energy, Elsevier, vol. 169(C), pages 213-227.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:213-227
    DOI: 10.1016/j.energy.2018.11.100
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    Citations

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    Cited by:

    1. Zou, Aihong & Chassaing, Jean-Camille & Persky, Rodney & Gu, YuanTong & Sauret, Emilie, 2019. "Uncertainty Quantification in high-density fluid radial-inflow turbines for renewable low-grade temperature cycles," Applied Energy, Elsevier, vol. 241(C), pages 313-330.
    2. McKeand, Austin M. & Gorguluarslan, Recep M. & Choi, Seung-Kyum, 2021. "Stochastic analysis and validation under aleatory and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Wang, Xiaojing & Zou, Zhengping, 2019. "Uncertainty analysis of impact of geometric variations on turbine blade performance," Energy, Elsevier, vol. 176(C), pages 67-80.
    4. Serafino, Aldo & Obert, Benoit & Cinnella, Paola, 2023. "Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery," Energy, Elsevier, vol. 269(C).
    5. Matar, Camille & Cinnella, Paola & Gloerfelt, Xavier & Reinker, Felix & aus der Wiesche, Stefan, 2023. "Investigation of non-ideal gas flows around a circular cylinder," Energy, Elsevier, vol. 268(C).
    6. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).

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