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

• Published in: Energy (Oxford) Vol. 169; pp. 213 - 227
• Main Authors: Razaaly, Nassim, Persico, Giacomo, Congedo, Pietro Marco
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2019; Elsevier BV; Elsevier
• Subjects: Autocorrelation functions; Computational fluid dynamics; Computer applications; Data processing; Energy sources; Fluid flow; Fluid mechanics; Fluids; Geometric variability; Heat recovery; Karhunen-Loeve expansion; Kriging interpolation; Mechanics; Molecular weight; Non-ideal compressible fluid dynamics; Nozzles; ORC turbine; Organic compounds; Organic loading; Parameter uncertainty; Physics; Rankine cycle; Statistics; SU2; Supersonic turbines; Thermodynamic models; Tolerances; Turbines; Two dimensional models; Uncertainty analysis; Uncertainty quantification; Variance analysis; Uncertainty quantification; SU2; ORC turbine; Geometric variability; Non-ideal compressible fluid dynamics; ORC Turbine; Uncertainty Quantification; Geometric Variability; Non-Ideal Compressible Fluid Dynamics
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.11.100

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.