Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives

In this work, Uncertainty Quantification (UQ) based on non-intrusive Polynomial Chaos Expansion (PCE) is applied to the CFD problem of the flow past an airfoil with parameterized angle of attack and inflow velocity. To limit the computational cost associated with each of the simulations required by the non-intrusive UQ algorithm used, we resort to a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD)-Galerkin approach. A first set of results is presented to characterize the accuracy of the POD-Galerkin ROM developed approach with respect to the Full Order Model (FOM) solver (OpenFOAM). A further analysis is then presented to assess how the UQ results are affected by substituting the FOM predictions with the surrogate ROM ones.

[1]  M. Stein Large sample properties of simulations using latin hypercube sampling , 1987 .

[2]  G. Rozza,et al.  An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics , 2018, 1810.12364.

[3]  Gianluigi Rozza,et al.  Comparison Between Reduced Basis and Stochastic Collocation Methods for Elliptic Problems , 2014, J. Sci. Comput..

[4]  Weeratunge Malalasekera,et al.  An introduction to computational fluid dynamics - the finite volume method , 2007 .

[5]  G. Rozza,et al.  POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations , 2016 .

[6]  Hrvoje Jasak,et al.  A tensorial approach to computational continuum mechanics using object-oriented techniques , 1998 .

[7]  Gianluigi Rozza,et al.  The Effort of Increasing Reynolds Number in Projection-Based Reduced Order Methods: From Laminar to Turbulent Flows , 2018, Lecture Notes in Computational Science and Engineering.

[8]  M. Gunzburger,et al.  Reduced-order modeling of time-dependent PDEs with multiple parameters in the boundary data , 2007 .

[9]  Ali H. Nayfeh,et al.  On the stability and extension of reduced-order Galerkin models in incompressible flows , 2009 .

[10]  Hrvoje Jasak,et al.  Error analysis and estimation for the finite volume method with applications to fluid flows , 1996 .

[11]  G. Rozza,et al.  POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder , 2017, 1701.03424.

[12]  Gianluigi Rozza,et al.  A Reduced Basis Method for Evolution Schemes with Parameter-Dependent Explicit Operators , 2008 .

[13]  Gianluigi Rozza,et al.  Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations , 2015 .

[14]  Gianluigi Rozza,et al.  Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows , 2019, J. Comput. Phys..

[15]  S. Isukapalli UNCERTAINTY ANALYSIS OF TRANSPORT-TRANSFORMATION MODELS , 1999 .

[16]  N. Wiener The Homogeneous Chaos , 1938 .

[17]  A. Strauß Theory Of Wing Sections Including A Summary Of Airfoil Data , 2016 .

[18]  M. Darwish,et al.  The Finite Volume Method in Computational Fluid Dynamics: An Advanced Introduction with OpenFOAM® and Matlab , 2015 .

[19]  G. Rozza,et al.  Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations , 2017, Computers & Fluids.

[20]  Max Gunzburger,et al.  POD and CVT-based reduced-order modeling of Navier-Stokes flows , 2006 .

[21]  B. Haasdonk,et al.  REDUCED BASIS METHOD FOR FINITE VOLUME APPROXIMATIONS OF PARAMETRIZED LINEAR EVOLUTION EQUATIONS , 2008 .

[22]  Gianluigi Rozza,et al.  Reduced Basis Methods for Uncertainty Quantification , 2017, SIAM/ASA J. Uncertain. Quantification.

[23]  Jens L. Eftang,et al.  Reduced basis methods for parametrized partial differential equations , 2011 .

[24]  T. Barth,et al.  Finite Volume Methods: Foundation and Analysis , 2004 .

[25]  Youngsoo Choi,et al.  Conservative model reduction for finite-volume models , 2017, J. Comput. Phys..

[26]  Charles-Henri Bruneau,et al.  Enablers for robust POD models , 2009, J. Comput. Phys..

[27]  Chettapong Janya-anurak,et al.  Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos , 2017 .

[28]  A. Quarteroni,et al.  Numerical solution of parametrized Navier–Stokes equations by reduced basis methods , 2007 .

[29]  J. Hesthaven,et al.  Certified Reduced Basis Methods for Parametrized Partial Differential Equations , 2015 .

[30]  Stefan Volkwein,et al.  Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..

[31]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[32]  M. Darwish,et al.  The Finite Volume Method , 2016 .

[33]  D. Spalding,et al.  A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows , 1972 .

[34]  H. Najm,et al.  Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection , 2003 .

[35]  Bernard Haasdonk,et al.  Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation , 2012, SIAM J. Sci. Comput..

[36]  Jeroen A. S. Witteveen,et al.  Effect of uncertainty on the bifurcation behavior of pitching airfoil stall flutter , 2009 .

[37]  B. R. Noack,et al.  A low‐dimensional Galerkin method for the three‐dimensional flow around a circular cylinder , 1994 .

[38]  G. Rozza,et al.  On the stability of the reduced basis method for Stokes equations in parametrized domains , 2007 .

[39]  Ramon Codina,et al.  Reduced-Order Modelling Strategies for the Finite Element Approximation of the Incompressible Navier-Stokes Equations , 2014 .

[40]  A. Quarteroni,et al.  Reduced Basis Methods for Partial Differential Equations: An Introduction , 2015 .

[41]  Gianluigi Rozza,et al.  Model reduction methods , 2017 .