P. Colonna, J. Harinck, S. Rebay, and A. Guardone, Real-gas effects in organic rankine cycle turbine nozzles, Journal of Propulsion and Power, vol.24, issue.2, pp.282-294, 2008.

P. Cinnella, P. M. Congedo, V. Pediroda, and L. Parussini, Quantification of thermodynamic uncertainties in real gas flows, International Journal of Engineering Systems Modelling and Simulation, vol.2, issue.1, pp.12-24, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00549227

P. Congedo, P. Colonna, C. Corre, J. Witteveen, and G. Iaccarino, Backward uncertainty propagation method in flow problems: Application to the prediction of rarefaction shock waves, Computer Methods in Applied Mechanics and Engineering, vol.213, pp.314-326, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00655373

P. Cinnella, P. Congedo, V. Pediroda, and L. Parussini, Sensitivity analysis of dense gas flow simulations to thermodynamic uncertainties, Physics of Fluids, vol.23, p.116101, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00630032

P. M. Congedo, C. Corre, and P. Cinnella, Numerical investigation of dense-gas effects in turbomachinery, Computers & Fluids, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00601545

P. Congedo, C. Corre, and J. M. Martinez, Shape optimization of an airfoil in a BZT flow with multiple-source uncertainties, Computer Methods in Applied Mechanics and Engineering, vol.200, issue.1-4, pp.216-232, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00549220

O. Le-maitre and O. Knio,

, Stochastic Spectral Methods for Uncertainty Quantication with Applications to Computational Fluid Dynamics. Series on Scientific Computation, 2010.

C. Soize, Uncertainty Quantification: An Accelerated Course with Advanced Applications in Computational Engineering, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01498996

P. Congedo, G. Geraci, R. Abgrall, V. Pediroda, and L. Parussini, Tsi metamodels-based multi-objective robust optimization. Engineering Computations, vol.30, pp.1032-1053, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00807652

P. Cinnella and S. Hercus, Robust optimization of dense gas flows under uncertain operating conditions, Computers & Fluids, vol.39, issue.10, pp.1893-1908, 2010.

G. Geraci, P. Congedo, R. Abgrall, and G. Iaccarino, High-order statistics in global sensitivity analysis: Decomposition and model reduction, Computer Methods in Applied Mechanics and Engineering, vol.301, pp.80-115, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01247458

E. Bufi and P. Cinnella, Efficient uncertainty quantification of turbulent flows through supersonic orc nozzle blades, Energy Procedia, vol.82, p.2015, 2015.

F. Palacios, M. F. Colonno, A. C. Aranake, A. Campos, S. R. Copeland et al., University Unstructured (SU2): An open-source integrated computational environment for multi-physics simulation and design, 51st AIAA Aerospace Sciences Meeting and Exhibit, 2013.

T. D. Economon, D. Mudigere, G. Bansal, A. Heinecke, F. Palacios et al., Performance optimizations for scalable implicit {RANS} calculations with {SU2}, Computers & Fluids, vol.129, pp.146-158, 2016.

S. Vitale, Extension of the SU2 Open Source CFD code to the simulation of turpinibulent flows of fluids modelled with complex thermophysical laws, AIAA Aviation, 2015.

G. Gori, M. Zocca, G. Cammi, A. Spinelli, and A. Guardone, Experimental assessment of the open-source su2 cfd suite for orc applications, Energy Procedia, vol.129, pp.256-263, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01671013

M. B. Giles, Nonreflecting boundary conditions for euler equation calculations, AIAA journal, vol.28, issue.12, pp.2050-2058, 1990.

P. L. Roe, Approximate riemann solvers, parameter vectors, and difference schemes, J Comput Phys, vol.43, issue.2, pp.357-372, 1981.

M. Vinokur and J. L. Montagné, Generalized flux-vector splitting and roe average for an equilibrium real gas, J Comput Phys, vol.89, p.276, 1990.

A. Guardone and L. Vigevano, Roe linearization for the van der Waals gas, J Comput Phys, vol.175, pp.50-78, 2002.

F. Menter, Zonal two equation kw turbulence models for aerodynamic flows, 23rd fluid dynamics, plasmadynamics, and lasers conference, p.2906, 1993.
DOI : 10.2514/6.1993-2906

V. E. Garzon, Probabilistic aerothermal design of compressor airfoils

, Massachusetts Institute of Technology, 2003.

J. Häcker, Statistical analysis of manufacturing deviations and classification methods for probabilistic aerothermal design of turbine blades, 2000.

A. Lange, M. Voigt, K. Vogeler, and E. Johann, Principal component analysis on 3d scanned compressor blades for probabilistic cfd simulation, 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p.1762, 2012.
DOI : 10.2514/6.2012-1762

E. A. Dow and Q. Wang, The implications of tolerance optimization on compressor blade design, Journal of Turbomachinery, vol.137, issue.10, p.101008, 2015.

E. A. Dow and Q. Wang, Optimal design and tolerancing of compressor blades subject to manufacturing variability, 16th AIAA NonDeterministic Approaches Conference, p.1008, 2014.
DOI : 10.2514/6.2014-1008

URL : http://dspace.mit.edu/bitstream/handle/1721.1/97545/final.pdf%3Bjsessionid%3D26258F5473AB4B28597B971A183F0CA0?sequence%3D1

W. Betz, I. Papaioannou, and D. Straub, Numerical methods for the discretization of random fields by means of the karhunenlò eve expansion, Computer Methods in Applied Mechanics and Engineering, vol.271, pp.109-129, 2014.

K. Karhunen, ¨ Uber lineare Methoden in der Wahrscheinlichkeitsrechnung, vol.37, 1947.

M. Loeve, Functions aleatoires du second ordre, Processus stochastique et mouvement Brownien, pp.366-420, 1948.

E. J. Nyström, ¨ Uber die praktische auflösung von integralgleichungen mit anwendungen auf randwertaufgaben, Acta Mathematica, vol.54, issue.1, pp.185-204, 1930.

A. De-boer, M. Van-der-schoot, and H. Bijl, Mesh deformation based on radial basis function interpolation, Computers & structures, vol.85, pp.784-795, 2007.

M. Pini, G. Persico, D. Pasquale, and S. Rebay, Adjoint method for shape optimization in real-gas flow applications, ASME Journal of Engineering for Gas Turbines and Power, vol.137, issue.3, 2015.
DOI : 10.1115/1.4028495

C. E. Rasmussen and C. W. ,

, Gaussian Processes for Machine Learning, 2006.

V. Dubourg, Adaptive surrogate models for reliability analysis and reliability-based design optimization, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00697026

A. Saltelli, S. Tarantola, and F. Campolongo, Sensitivity Analysis in Practice, 2004.

I. M. Sobol, Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates, Mathematics and computers in simulation, vol.55, issue.1-3, pp.271-280, 2001.

A. A. Miranda, Y. A. Le-borgne, and G. Bontempi, New routes from minimal approximation error to principal components, Neural Processing Letters, vol.27, issue.3, pp.197-207, 2008.
DOI : 10.1007/s11063-007-9069-2

URL : http://www.ulb.ac.be/di/map/yleborgn/pub/NPL_PCA_07.pdf

A. Romei, G. Persico, and P. M. Congedo, Assessment of deterministic shape optimizations within a stochastic framework for supersonic organic rankine cycle nozzle cascades, Journal of Engineering for Gas Turbines and Power, 2018.