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

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.

[1]  Giacomo Bruno Azzurro Persico,et al.  Assessment of Deterministic Shape Optimizations Within a Stochastic Framework for Supersonic Organic Rankine Cycle Nozzle Cascades , 2019, Journal of Engineering for Gas Turbines and Power.

[2]  Víctor E. Garzón,et al.  Probabilistic aerothermal design of compressor airfoils , 2003 .

[3]  Pradeep Dubey,et al.  Performance optimizations for scalable implicit RANS calculations with SU2 , 2016 .

[4]  Paola Cinnella,et al.  Robust optimization of dense gas flows under uncertain operating conditions , 2010 .

[5]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

[6]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[7]  Qiqi Wang,et al.  The Implications of Tolerance Optimization on Compressor Blade Design , 2014, 1411.0338.

[8]  F. Menter Improved two-equation k-omega turbulence models for aerodynamic flows , 1992 .

[9]  Thomas D. Economon,et al.  Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design , 2013 .

[10]  Giulio Gori,et al.  Experimental assessment of the open- source SU2 CFD suite for ORC applications , 2017 .

[11]  H. Bijl,et al.  Mesh deformation based on radial basis function interpolation , 2007 .

[12]  Pietro Marco Congedo,et al.  Quantification of Thermodynamic Uncertainties in Real Gas Flows , 2009 .

[13]  Stefano Rebay,et al.  Real-gas effects in Organic Rankine Cycle turbine nozzles , 2008 .

[14]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[15]  Rémi Abgrall,et al.  TSI metamodels-based multi-objective robust optimization , 2013 .

[16]  Gianluca Bontempi,et al.  New Routes from Minimal Approximation Error to Principal Components , 2008, Neural Processing Letters.

[17]  Juan J. Alonso,et al.  Extension of the SU2 open source CFD code to the simulation of turbulent flows of fuids modelled with complex thermophysical laws , 2015 .

[18]  Matthias Voigt,et al.  Principal component analysis on 3D scanned compressor blades for probabilistic CFD simulation , 2012 .

[19]  I. Papaioannou,et al.  Numerical methods for the discretization of random fields by means of the Karhunen–Loève expansion , 2014 .

[20]  Paola Cinnella,et al.  Efficient Uncertainty Quantification of Turbulent Flows through Supersonic ORC Nozzle Blades , 2015 .

[21]  Michael B. Giles,et al.  Nonreflecting boundary conditions for Euler equation calculations , 1990 .

[22]  Pietro Marco Congedo,et al.  Sensitivity analysis of dense gas flow simulations to thermodynamic uncertainties , 2011 .

[23]  F. Menter ZONAL TWO EQUATION k-w TURBULENCE MODELS FOR AERODYNAMIC FLOWS , 1993 .

[24]  Qiqi Wang,et al.  Optimal Design and Tolerancing of Compressor Blades Subject to Manufacturing Variability , 2014 .

[25]  Rémi Abgrall,et al.  High-order statistics in global sensitivity analysis: Decomposition and model reduction , 2016 .

[26]  Giacomo Bruno Azzurro Persico,et al.  Adjoint Method for Shape Optimization in Real-Gas Flow Applications , 2015 .

[27]  Pietro Marco Congedo,et al.  Numerical investigation of dense-gas effects in turbomachinery , 2011 .

[28]  Marcel Vinokur,et al.  Generalized Flux-Vector splitting and Roe average for an equilibrium real gas , 1990 .

[29]  V. Dubourg Adaptive surrogate models for reliability analysis and reliability-based design optimization , 2011 .

[30]  Pietro Marco Congedo,et al.  Shape optimization of an airfoil in a BZT flow with multiple-source uncertainties , 2011 .

[31]  P. Roe Approximate Riemann Solvers, Parameter Vectors, and Difference Schemes , 1997 .

[32]  E. Nyström Über Die Praktische Auflösung von Integralgleichungen mit Anwendungen auf Randwertaufgaben , 1930 .

[33]  Alberto Guardone,et al.  Roe linearization for the van der Waals gas , 2002 .

[34]  Pietro Marco Congedo,et al.  Backward uncertainty propagation method in flow problems: Application to the prediction of rarefaction shock waves , 2012 .