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A hybrid reduced order method for modelling turbulent heat transfer problems. (English) Zbl 1502.65087

Summary: A parametric, hybrid reduced order method based on the Proper Orthogonal Decomposition with both Galerkin projection and interpolation based on Radial Basis Functions method is presented. This method is tested on a case of turbulent non-isothermal mixing in a T-junction pipe, a common flow 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 Unsteady Reynolds Averaged Navier-Stokes full order model, is tested against the full order solver in a 3D T-junction pipe with parameterised velocity inlet boundary conditions.

MSC:

65M08 Finite volume methods for initial value and initial-boundary value problems involving PDEs
65D12 Numerical radial basis function approximation
76D05 Navier-Stokes equations for incompressible viscous fluids

Software:

ITHACA-FV; redbKIT

References:

[1] Benner, P.; Gugercin, S.; Willcox, K., A survey of projection-based model reduction methods for parametric dynamical systems, SIAM Rev, 57, 483-531 (2015) · Zbl 1339.37089
[2] Cordier, L.; Bergmann, M., Proper orthogonal decomposition: an overview, Lecture series 2002-04, 2003-03 and 2008-01 on post-processing of experimental and numerical data, Von Karman Institute for Fluid Dynamics, 2008., 46pages (2008), VKI
[3] Lumley, J. L., The structure of inhomogeneous turbulent flows, Atmospheric turbulence and radio wave propagation (1967)
[4] Sirovich, L., Turbulence and the dynamics of coherent structures part i: coherent structures, Q top Q Appl Math, 45, 3, 561-571 (1987) · Zbl 0676.76047
[5] Baltzer, J.; Adrian, R.; Wu, X., Turbulent boundary layer structure identification via POD, Proceedings of the summer program, 55 (2010)
[6] Bernero, S.; Fiedler, H. E., Application of particle image velocimetry and proper orthogonal decomposition to the study of a jet in a counterflow, Exp Fluids, 29, 7, S274-S281 (2000)
[7] Rempfer, D.; Fasel, H. F., Evolution of three-dimensional coherent structures in a flat-plate boundary layer, J Fluid Mech, 260, 1, 351 (1994)
[8] Bui-Thanh, T., Proper orthogonal decomposition extensions and their applications in steady aerodynamics (2003), Master thesis, High performance computation for engineered systems, Singapore-MIT Alliance, Master thesis
[9] Dolci, V.; Arina, R., Proper orthogonal decomposition as surrogate model for aerodynamic optimization (2016)
[10] Salmoiraghi, F.; Scardigli, A.; Telib, H.; Rozza, G., Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation, Int J Comut Fluid Dyn, 32, 4-5, 233-247 (2018) · Zbl 07474453
[11] Rama, R. R.; Skatulla, S.; Sansour, C., Real-time modelling of diastolic filling of the heart using the proper orthogonal decomposition with interpolation, Int J Solids Struct, 96, 409-422 (2016)
[12] Ravindran, S. S., A reduced-order approach for optimal control of fluids using proper orthogonal decomposition, Int J Numer Methods Fluids, 34, 5, 425-448 (2000) · Zbl 1005.76020
[13] Bourguet, R.; Braza, M.; Dervieux, A., Reduced-order modeling of transonic flows around an airfoil submitted to small deformations, J Comput Phys, 230, 1, 159-184 (2011) · Zbl 1427.76110
[14] Lorenzi, S.; Cammi, A.; Luzzi, L.; Rozza, G., POD-Galerkin method for finite volume approximation of Navier-Stokes and RANS equations, Comput Methods Appl Mech Eng, 311, 151-179 (2016) · Zbl 1439.76112
[15] Barone, M. F.; Kalashnikova, I.; Segalman, D. J.; Thornquist, H. K., Stable Galerkin reduced order models for linearized compressible flow, J Comput Phys, 228, 6, 1932-1946 (2009) · Zbl 1162.76025
[16] Rowley, C. W.; Colonius, T.; Murray, R. M., Model reduction for compressible flows using {POD} and Galerkin projection, Physica D, 189, 12, 115-129 (2004) · Zbl 1098.76602
[17] Ballarin, F.; Rozza, G., POD-Galerkin monolithic reduced order models for parametrized fluid-structure interaction problems, Int J Numer Methods Fluids, 82, 12, 1010-1034 (2016)
[18] Ballarin, F.; Manzoni, A.; Quarteroni, A.; Rozza, G., Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations, Int J Numer Methods Eng, 102, 5, 1136-1161 (2015) · Zbl 1352.76039
[19] Stabile, G.; Rozza, G., Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations, Comput Fluids, 173, 273-284 (2018) · Zbl 1410.76264
[20] Ballarin, F.; Faggiano, E.; Ippolito, S.; Manzoni, A.; Quarteroni, A.; Rozza, G., Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization, J Comput Phys, 315, 609-628 (2016) · Zbl 1349.76173
[21] ISBN 978-3-030-30705-9
[22] http://www.sciencedirect.com/science/article/pii/S0021999120302874 · Zbl 1437.76015
[23] Stabile, G.; Ballarin, F.; Zuccarino, G.; Rozza, G., A reduced order variational multiscale approach for turbulent flows, Adv Comput Math, 45, 2349-2368 (2019) · Zbl 1435.65165
[24] Carlberg, K.; Bou-Mosleh, C.; Farhat, C., Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations, Int J Numer Methods Eng, 86, 2, 155-181 (2010) · Zbl 1235.74351
[25] Xiao, D.; Fang, F.; Du, J.; Pain, C.; Navon, I.; Buchan, A., Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair, Comput Methods Appl Mech Eng, 255, 147-157 (2013) · Zbl 1297.76107
[26] Carlberg, K.; Farhat, C.; Cortial, J.; Amsallem, D., The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows, J Comput Phys, 242, 623-647 (2013) · Zbl 1299.76180
[27] Experiments and CFD code applications to nuclear reactor safety (XCFD4NRS)
[28] Walker, C.; Simiano, M.; Zboray, R.; Prasser, H.-M., Investigations on mixing phenomena in single-phase flow in a T-junction geometry, Nucl Eng Des, 239, 1, 116-126 (2009)
[29] Naik-Nimbalkar, V.; Patwardhan, A.; Banerjee, I.; Padmakumar, G.; Vaidyanathan, G., Thermal mixing in T-junctions, Chem Eng Sci, 65, 22, 5901-5911 (2010)
[30] SI : CFD4NRS-3
[31] Tunstall, R.; Laurence, D.; Prosser, R.; Skillen, A., Large eddy simulation of a T-junction with upstream elbow: the role of dean vortices in thermal fatigue, Appl Therm Eng, 107, 672-680 (2016)
[32] Kuczaj, A.; Komen, E.; Loginov, M., Large-eddy simulation study of turbulent mixing in a T-junction, Nucl Eng Des, 240, 9, 2116-2122 (2010)
[33] Sartori, A.; Cammi, A.; Luzzi, L.; Rozza, G., A reduced basis approach for modeling the movement of nuclear reactor control rods, J Nucl Eng RadiatSci, 2, 2, 21019 (2016)
[34] Buchan, A.; Pain, C.; Fang, F.; Navon, I., A pod reduced-order model for eigenvalue problems with application to reactor physics, Int J Numer Methods Eng, 95, 12, 1011-1032 (2013) · Zbl 1352.82018
[35] Georgaka, S.; Stabile, G.; Rozza, G.; J. Bluck, M., Parametric POD-Galerkin model order reduction for unsteady-state heat transfer problems, Commun Comput Phys, 27, 1, 1-32 (2019) · Zbl 1473.78016
[36] Busto, S.; Stabile, G.; Rozza, G.; Vzquez-Cendn, M., POD-Galerkin reduced order methods for combined Navier-Stokes transport equations based on a hybrid FV-FE solver, Comput Math Appl, 79, 2, 256-273 (2020) · Zbl 1443.65196
[37] Raghupathy, A. P.; Ghia, U.; Ghia, K.; Maltz, W., Boundary-condition-independent reduced-order modeling of complex 2d objects by POD-Galerkin methodology, 2009 25th Annual IEEE semiconductor thermal measurement and management symposium (2009), IEEE
[38] Dehghan, M.; Abbaszadeh, M., Proper orthogonal decomposition variational multiscale element free Galerkin (POD-VMEFG) meshless method for solving incompressible navier-stokes equation, Comput Methods Appl Mech Eng, 311, 856-888 (2016) · Zbl 1439.76060
[39] Xiao, D.; Fang, F.; Pain, C.; Navon, I.; Salinas, P.; Muggeridge, A., Non-intrusive reduced order modeling of multi-phase flow in porous media using the POD-RBF method, J Comput Phys (2015)
[40] Ostrowski, Z.; Biaecki, R. A.; Kassab, A. J., Solving inverse heat conduction problems using trained POD-RBF network inverse method, Inverse Probl Sci Eng, 16, 1, 39-54 (2008) · Zbl 1158.35433
[41] Wilcox, D. C., Turbulence modeling for CFD, vol. 2 (1998), DCW industries La Canada, CA
[42] Versteeg, H. K.; Malalasekera, W., An introduction to computational fluid dynamics. the finite volume method (1995), Longman Group Ltd.: Longman Group Ltd. London
[43] ISBN 3319168738, 9783319168739 · Zbl 1329.76001
[44] Haasdonk, B., Convergence rates of the POD-Greedy method, ESAIM, 47, 3, 859-873 (2013) · Zbl 1277.65074
[45] Urban, K.; Volkwein, S.; Zeeb, O., Greedy sampling using nonlinear optimization, Reduced order methods for modeling and computational reduction, 137-157 (2014), Springer · Zbl 1322.65070
[46] Hoang, K. C.; Kerfriden, P.; Khoo, B.; Bordas, S., An efficient goal-oriented sampling strategy using reduced basis method for parametrized elastodynamic problems, Numer Methods Partial Differ Equ, 31, 2, 575-608 (2015) · Zbl 1325.74143
[47] Elsevier Editor
[48] Brands, B.; Mergheim, J.; Steinmann, P., Reduced-order modelling for linear heat conduction with parametrised moving heat sources, GAMM Mitteilungen, 39, 2, 170-188 (2016) · Zbl 1397.74050
[49] Rozza, G.; Huynh, D. B.P.; Patera, A. T., Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations, Arch Comput Methods Eng, 15, 3, 229 (2008) · Zbl 1304.65251
[50] Kalashnikova, I.; Barone, M. F., On the stability and convergence of a Galerkin reduced order model (ROM) of compressible flow with solid wall and far-field boundary treatment, Int J Numer Methods Eng, 83, 10, 1345-1375 (2010) · Zbl 1202.74123
[51] Quarteroni, A.; Manzoni, A.; Negri, F., Reduced basis methods for partial differential equations (2016), Springer International Publishing · Zbl 1337.65113
[52] Chinesta, F.; Ladeveze, P.; Cueto, E., A short review on model order reduction based on proper generalized decomposition, Arch Comput Methods Eng, 18, 4, 395 (2011)
[53] Dumon, A.; Allery, C.; Ammar, A., Proper general decomposition (PGD) for the resolution of Navier-Stokes equations, J Comput Phys, 230, 4, 1387-1407 (2011) · Zbl 1391.76099
[54] Rozza, G.; Veroy, K., On the stability of the reduced basis method for Stokes equations in parametrized domains, Comput Methods Appl Mech Eng, 196, 7, 1244-1260 (2007) · Zbl 1173.76352
[55] Ballarin, F.; Manzoni, A.; Quarteroni, A.; Rozza, G., Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations, Int J Numer Methods Eng, 102, 5, 1136-1161 (2014) · Zbl 1352.76039
[56] Graham, W. R.; Peraire, J.; Tang, K. Y., Optimal control of vortex shedding using low-order models. Part i:open-loop model development, Int J Numer Methods Eng, 44, 7, 945-972 (1999) · Zbl 0955.76026
[57] Stabile, G.; Hijazi, S.; Mola, A.; Lorenzi, S.; Rozza, G., POD-Galerkin reduced order methods for CFD using finite volume discretisation: vortex shedding around a circular cylinder, Commun Appl IndMath, 8, 1, 210-236 (2017) · Zbl 1383.35175
[58] Feng, J.; Frahi, T.; Baglietto, E., Structure-based URANS simulations of thermal mixing in T-junctions, Nucl Eng Des, 340, 275-299 (2018)
[59] Tunstall, R.; Laurence, D.; Prosser, R.; Skillen, A., Benchmarking LES with wall-functions and RANS for fatigue problems in thermal-hydraulics systems, Nucl Eng Des, 308, 170-181 (2016)
[60] OpenFOAM website. https://openfoam.org/ Accessed: 13-10-2017.
[61] Stabile G., Rozza G.. ITHACA-FV - In real time highly advanced computational applications for finite volumes. Accessed: 2018-01-30; http://www.mathlab.sissa.it/ithaca-fv.
[62] Issa, R., Solution of the implicitly discretised fluid flow equations by operator-splitting, J Comput Phys, 62, 1, 40-65 (1986) · Zbl 0619.76024
[63] Liu, F., A thorough description of how wall functions are implemented in OpenFOAM, Proceedings of CFD with OpenSource software, 2016, Edited by Nilsson H. (2016)
[64] Noack, B. R.; Papas, P.; Monkewitz, P. A., The need for a pressure-term representation in empirical Galerkin models of incompressible shear flows, J Fluid Mech, 523, 339-365 (2005) · Zbl 1065.76102
[65] Caiazzo, A.; Iliescu, T.; John, V.; Schyschlowa, S., A numerical investigation of velocity-pressure reduced order models for incompressible flows, J Comput Phys, 259, 598-616 (2014) · Zbl 1349.76050
[66] Star, K.; Stabile, G.; Georgaka, S.; Belloni, F.; Rozza, G.; Degroote, J., Pod-Galerkin reduced order model of the Boussinesq approximation for buoyancy-driven enclosed flows, Building theory and applications: proceedings of M&C 2019, 2452-2461 (2019), American Nuclear Society (ANS)
[67] Vergari, L.; Cammi, A.; Lorenzi, S., Reduced order modeling approach for parametrized thermal-hydraulics problems: inclusion of the energy equation in the POD-FV-ROM method, Prog Nucl Energy, 118, 103071 (2020)
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