A Reduced Order Approach for the Embedded Shifted Boundary FEM and a Heat Exchange System on Parametrized Geometries

A model order reduction technique is combined with an embedded boundary finite element method with a POD-Galerkin strategy. The proposed methodology is applied to parametrized heat transfer problems and we rely on a sufficiently refined shape-regular background mesh to account for parametrized geometries. In particular, the employed embedded boundary element method is the Shifted Boundary Method (SBM), recently proposed in Main and Scovazzi, J Comput Phys [17]. This approach is based on the idea of shifting the location of true boundary conditions to a surrogate boundary, with the goal of avoiding cut cells near the boundary of the computational domain. This combination of methodologies has multiple advantages. In the first place, since the Shifted Boundary Method always relies on the same background mesh, there is no need to update the discretized parametric domain. Secondly, we avoid the treatment of cut cell elements, which usually need particular attention. Thirdly, since the whole background mesh is considered in the reduced basis construction, the SBM allows for a smooth transition of the reduced modes across the immersed domain boundary. The performances of the method are verified in two dimensional heat transfer numerical examples.

[1]  Gianluigi Rozza,et al.  Reduced basis approximation and a posteriori error estimation for Stokes flows in parametrized geometries: roles of the inf-sup stability constants , 2013, Numerische Mathematik.

[2]  N. Nguyen,et al.  EFFICIENT REDUCED-BASIS TREATMENT OF NONAFFINE AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS , 2007 .

[3]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[4]  L. Sirovich TURBULENCE AND THE DYNAMICS OF COHERENT STRUCTURES PART I : COHERENT STRUCTURES , 2016 .

[5]  Gianluigi Rozza,et al.  Model Order Reduction: a survey , 2016 .

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

[7]  Gianluigi Rozza,et al.  Projection-based reduced order models for a cut finite element method in parametrized domains , 2019, Comput. Math. Appl..

[8]  Ting Song,et al.  The shifted boundary method for hyperbolic systems: Embedded domain computations of linear waves and shallow water flows , 2018, J. Comput. Phys..

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

[10]  Gianluigi Rozza,et al.  Model Reduction of Parametrized Systems , 2017 .

[11]  Gianluigi Rozza,et al.  Model Order Reduction: a survey , 2016 .

[12]  Alex Main,et al.  The shifted boundary method for embedded domain computations. Part II: Linear advection-diffusion and incompressible Navier-Stokes equations , 2018, J. Comput. Phys..

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

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

[15]  Juan Du,et al.  Non-linear model reduction for the Navier-Stokes equations using residual DEIM method , 2014, J. Comput. Phys..

[16]  Gianluigi Rozza,et al.  Reduced basis methods for Stokes equations in domains with non-affine parameter dependence , 2009 .

[17]  Alex Main,et al.  The shifted boundary method for embedded domain computations. Part I: Poisson and Stokes problems , 2017, J. Comput. Phys..

[18]  A. Patera,et al.  Reduced-basis approximation of the viscous Burgers equation: rigorous a posteriori error bounds , 2003 .

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

[20]  Charbel Farhat,et al.  Reduction of nonlinear embedded boundary models for problems with evolving interfaces , 2014, J. Comput. Phys..

[21]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[22]  Gianluigi Rozza,et al.  A reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow , 2018, Computer Methods in Applied Mechanics and Engineering.

[23]  Matthew F. Barone,et al.  On the stability and convergence of a Galerkin reduced order model (ROM) of compressible flow with solid wall and far‐field boundary treatment , 2010 .

[24]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[25]  F. Chinesta,et al.  A Short Review in Model Order Reduction Based on Proper Generalized Decomposition , 2018 .

[26]  A. Patera,et al.  A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations , 2005 .

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

[28]  Charbel Farhat,et al.  The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows , 2012, J. Comput. Phys..

[29]  C. Allery,et al.  Proper general decomposition (PGD) for the resolution of Navier-Stokes equations , 2011, J. Comput. Phys..

[30]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .