Benchmarking Uncertainty Quantification Methods Using the NACA 2412 Airfoil with Geometrical and Operational Uncertainties

[1]  Timothy W. Simpson,et al.  Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? , 2014 .

[2]  Grégory Coussement,et al.  Non-intrusive Probabilistic Collocation Method for Operational, Geometrical, and Manufacturing Uncertainties in Engineering Practice , 2019 .

[3]  Grégory Coussement,et al.  Quantification of Combined Operational and Geometrical Uncertainties in Turbo-Machinery Design , 2015 .

[4]  Haitao Liu,et al.  A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design , 2017, Structural and Multidisciplinary Optimization.

[5]  Pénélope Leyland,et al.  A Continuation Multi Level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible inviscid aerodynamics , 2017 .

[6]  L. Mathelin,et al.  A Stochastic Collocation Algorithm for Uncertainty Analysis , 2003 .

[7]  Robert Scheichl,et al.  Finite Element Error Analysis of Elliptic PDEs with Random Coefficients and Its Application to Multilevel Monte Carlo Methods , 2013, SIAM J. Numer. Anal..

[8]  Leo Wai-Tsun Ng,et al.  Multifidelity Uncertainty Quantification Using Non-Intrusive Polynomial Chaos and Stochastic Collocation , 2012 .

[9]  Michael B. Giles,et al.  Multilevel Monte Carlo Path Simulation , 2008, Oper. Res..

[10]  R. M. Hicks,et al.  Wing Design by Numerical Optimization , 1977 .

[11]  M. Drela XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils , 1989 .

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

[13]  Andrea Barth,et al.  Multilevel Monte Carlo method for parabolic stochastic partial differential equations , 2013 .

[14]  Liang Gao,et al.  A hybrid variable-fidelity global approximation modelling method combining tuned radial basis function base and kriging correction , 2013 .

[15]  Andrea Barth,et al.  Multi-level Monte Carlo Finite Element method for elliptic PDEs with stochastic coefficients , 2011, Numerische Mathematik.

[16]  A. V. D. Vaart,et al.  Asymptotic Statistics: Frontmatter , 1998 .

[17]  Kyung K. Choi,et al.  Metamodeling Method Using Dynamic Kriging for Design Optimization , 2011 .

[18]  Karl Pearson,et al.  METHOD OF MOMENTS AND METHOD OF MAXIMUM LIKELIHOOD , 1936 .

[19]  Dominique Pelletier,et al.  APPLICATIONS OF CONTINUOUS SENSITIVITY EQUATIONS TO FLOWS WITH TEMPERATURE-DEPENDENT PROPERTIES , 2003 .

[20]  Edward N. Tinoco,et al.  Summary of Data from the Sixth AIAA CFD Drag Prediction Workshop: CRM Cases 2 to 5 , 2017 .

[21]  Stefan Heinrich,et al.  Monte Carlo Complexity of Global Solution of Integral Equations , 1998, J. Complex..

[22]  Frederick Stern,et al.  Multidisciplinary Design Optimization of a 3D Composite Hydrofoil via Variable Accuracy Architecture , 2018, 2018 Multidisciplinary Analysis and Optimization Conference.

[23]  H. Schlichting Zur Enstehung der Turbulenz bei der Plattenströmung , 1933 .

[24]  M. Giles,et al.  Viscous-inviscid analysis of transonic and low Reynolds number airfoils , 1986 .

[25]  Richard P. Dwight,et al.  Uncertainty quantification for a sailing yacht hull, using multi-fidelity kriging , 2015 .

[26]  Stefan Heinrich,et al.  Monte Carlo Complexity of Parametric Integration , 1999, J. Complex..

[27]  K. K. Choi,et al.  Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification , 2014, Structural and Multidisciplinary Optimization.

[28]  Gene H. Golub,et al.  Calculation of Gauss quadrature rules , 1967, Milestones in Matrix Computation.

[29]  Jeroen A. S. Witteveen,et al.  Probabilistic Collocation: An Efficient Non-Intrusive Approach for Arbitrarily Distributed Parametric Uncertainties , 2007 .