A Hybrid Reduced Order Method for Modelling Turbulent Heat Transfer Problems

A parametric, hybrid reduced order model approach based on the Proper Orthogonal Decomposition with both Galerkin projection and interpolation based on Radial Basis Functions method is presented. This method is tested against a case of turbulent non-isothermal mixing in a T-junction pipe, a common ow arrangement found in nuclear reactor cooling systems. The reduced order model is derived from the 3D unsteady, incompressible Navier-Stokes equations weakly coupled with the energy equation. For high Reynolds numbers, the eddy viscosity and eddy diffusivity are incorporated into the reduced order model with a Proper Orthogonal Decomposition (nested and standard) with Interpolation (PODI), where the interpolation is performed using Radial Basis Functions. The reduced order solver, obtained using a k-{\omega} SST URANS full order model, is tested against the full order solver in a 3D T-junction pipe with parametric velocity inlet boundary conditions.

[1]  Emilio Baglietto,et al.  STRUCTure-based URANS simulations of thermal mixing in T-junctions , 2018, Nuclear Engineering and Design.

[2]  Antonio Cammi,et al.  Reduced order modeling approach for parametrized thermal-hydraulics problems: inclusion of the energy equation in the POD-FV-ROM method , 2020 .

[3]  Laurent Cordier,et al.  Proper Orthogonal Decomposition: an overview , 2008 .

[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]  L. Sirovich Turbulence and the dynamics of coherent structures. III. Dynamics and scaling , 1987 .

[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]  Karen Willcox,et al.  A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems , 2015, SIAM Rev..

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

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

[11]  Bernard Haasdonk,et al.  Convergence Rates of the POD–Greedy Method , 2013 .

[12]  Horst-Michael Prasser,et al.  Investigations on mixing phenomena in single-phase flow in a T-junction geometry , 2009 .

[13]  Ionel M. Navon,et al.  Non-intrusive reduced order modeling of multi-phase flow in porous media using the POD-RBF method , 2015 .

[14]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .

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

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

[17]  Pierre Kerfriden,et al.  An efficient goal‐oriented sampling strategy using reduced basis method for parametrized elastodynamic problems , 2015 .

[18]  P. Steinmann,et al.  Reduced‐order modelling for linear heat conduction with parametrised moving heat sources , 2016 .

[19]  G. Rozza,et al.  Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation , 2018, International Journal of Computational Fluid Dynamics.

[20]  Dominique Laurence,et al.  Benchmarking LES with wall-functions and RANS for fatigue problems in thermal–hydraulics systems , 2016 .

[21]  Alain Dervieux,et al.  Reduced-order modeling of transonic flows around an airfoil submitted to small deformations , 2011, J. Comput. Phys..

[22]  Gianluigi Rozza,et al.  A reduced order variational multiscale approach for turbulent flows , 2018, Advances in Computational Mathematics.

[23]  Hermann F. Fasel,et al.  Dynamics of three-dimensional coherent structures in a flat-plate boundary layer , 1994, Journal of Fluid Mechanics.

[24]  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 .

[25]  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..

[26]  G. Padmakumar,et al.  Thermal mixing in T-junctions , 2010 .

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

[28]  Traian Iliescu,et al.  A numerical investigation of velocity-pressure reduced order models for incompressible flows , 2014, J. Comput. Phys..

[29]  Gianluigi Rozza,et al.  Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization , 2016, J. Comput. Phys..

[30]  E.M.J. Komen,et al.  Large-Eddy Simulation study of turbulent mixing in a T-junction , 2010 .

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

[32]  G. Rozza,et al.  A Reduced Basis Approach for Modeling the Movement of Nuclear Reactor Control Rods , 2016 .

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

[34]  Bernd R. Noack,et al.  The need for a pressure-term representation in empirical Galerkin models of incompressible shear flows , 2005, Journal of Fluid Mechanics.

[35]  A. Gosman,et al.  Solution of the implicitly discretised reacting flow equations by operator-splitting , 1986 .

[36]  A. J. Kassab,et al.  Solving inverse heat conduction problems using trained POD-RBF network inverse method , 2008 .

[37]  D. Wilcox Turbulence modeling for CFD , 1993 .

[38]  Ionel M. Navon,et al.  Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair , 2013 .

[39]  R. Adrian,et al.  Turbulent boundary layer structure identification via POD , 2010 .

[40]  Valentina Dolci,et al.  Proper Orthogonal Decomposition as Surrogate Model for Aerodynamic Optimization , 2016 .

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

[42]  C. N. Sökmen,et al.  CFD modeling of thermal mixing in a T-junction geometry using LES model , 2012 .

[43]  Bernhard Wieland,et al.  Reduced basis methods for partial differential equations with stochastic influences , 2013 .

[44]  Carlo Sansour,et al.  Real-time modelling of diastolic filling of the heart using the proper orthogonal decomposition with interpolation , 2016 .

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

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

[47]  R. Murray,et al.  Model reduction for compressible flows using POD and Galerkin projection , 2004 .

[48]  Mostafa Abbaszadeh,et al.  Proper orthogonal decomposition variational multiscale element free Galerkin (POD-VMEFG) meshless method for solving incompressible Navier–Stokes equation , 2016 .

[49]  Wr Graham,et al.  OPTIMAL CONTROL OF VORTEX SHEDDING USING LOW-ORDER MODELS. PART I-OPEN-LOOP MODEL DEVELOPMENT , 1999 .

[50]  Gianluigi Rozza,et al.  POD-Galerkin reduced order methods for combined Navier-Stokes transport equations based on a hybrid FV-FE solver , 2018, Comput. Math. Appl..

[51]  Bui Thanh Tan,et al.  Proper Orthogonal Decomposition Extensions and Their Applications in Steady Aerodynamics , 2003 .

[52]  Gianluigi Rozza,et al.  Parametric POD-Galerkin Model Order Reduction for Unsteady-State Heat Transfer Problems , 2018, Communications in Computational Physics.

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

[54]  Matthew F. Barone,et al.  Stable Galerkin reduced order models for linearized compressible flow , 2009, J. Comput. Phys..

[55]  Th. Frank,et al.  Simulation of turbulent and thermal mixing in T-junctions using URANS and scale-resolving turbulence models in ANSYS CFX , 2010 .

[56]  Stefan Volkwein,et al.  Greedy Sampling Using Nonlinear Optimization , 2014 .

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

[58]  Gianluigi Rozza,et al.  POD–Galerkin monolithic reduced order models for parametrized fluid‐structure interaction problems , 2016 .

[59]  Joris Degroote,et al.  POD-Galerkin reduced order model of the Boussinesq approximation for buoyancy-driven enclosed flows , 2019 .

[60]  H. E. Fiedler,et al.  Application of particle image velocimetry and proper orthogonal decomposition to the study of a jet in a counterflow , 2000 .

[61]  Dominique Laurence,et al.  Large eddy simulation of a T-Junction with upstream elbow: The role of Dean vortices in thermal fatigue , 2016 .

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

[63]  Urmila Ghia,et al.  Boundary-condition-independent reduced-order modeling of complex 2D objects by POD-Galerkin methodology , 2009, 2009 25th Annual IEEE Semiconductor Thermal Measurement and Management Symposium.

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

[65]  S. Ravindran A reduced-order approach for optimal control of fluids using proper orthogonal decomposition , 2000 .

[66]  Christopher C. Pain,et al.  A POD reduced‐order model for eigenvalue problems with application to reactor physics , 2013 .