Research article

A discontinuous Galerkin Method based on POD model reduction for Euler equation

  • Received: 25 July 2023 Revised: 29 November 2023 Accepted: 20 December 2023 Published: 15 January 2024
  • This paper considers the work of combining the proper orthogonal decomposition (POD) reduced-order method with the discontinuous Galerkin (DG) method to solve three-dimensional time-domain Euler equations. The POD-DG formulation is established by constructing the POD base vector space, based on POD technology one can apply the Galerkin projection of the DG scheme to this dimension reduction space for calculation. Its overall goal is to overcome the disadvantages of high computational cost and memory requirement in the DG algorithm, reduce the degrees of freedom (DOFs) of the calculation model, and save the calculation time while ensuring acceptable accuracy. Numerical experiments verify these advantages of the proposed POD-DG method.

    Citation: Lan Zhu, Li Xu, Jun-Hui Yin, Shu-Cheng Huang, Bin Li. A discontinuous Galerkin Method based on POD model reduction for Euler equation[J]. Networks and Heterogeneous Media, 2024, 19(1): 86-105. doi: 10.3934/nhm.2024004

    Related Papers:

  • This paper considers the work of combining the proper orthogonal decomposition (POD) reduced-order method with the discontinuous Galerkin (DG) method to solve three-dimensional time-domain Euler equations. The POD-DG formulation is established by constructing the POD base vector space, based on POD technology one can apply the Galerkin projection of the DG scheme to this dimension reduction space for calculation. Its overall goal is to overcome the disadvantages of high computational cost and memory requirement in the DG algorithm, reduce the degrees of freedom (DOFs) of the calculation model, and save the calculation time while ensuring acceptable accuracy. Numerical experiments verify these advantages of the proposed POD-DG method.



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    [1] C. R. Nastase, D. J. Mavriplis, A parallel hp-multigrid solver for three-dimensional discontinuous Galerkin discretizations of the Euler equations, 45th AIAA Aerospace Sciences Meeting and Exhibit, (2007), 512. https://doi.org/10.2514/6.2007-512 doi: 10.2514/6.2007-512
    [2] H. Luo, J. D. Baum, R. Löhner, A p-multigrid discontinuous Galerkin method for the Euler equations on unstructured grids, J. Comput. Phys., 211 (2006), 767–783. https://doi.org/10.1016/j.jcp.2005.06.019 doi: 10.1016/j.jcp.2005.06.019
    [3] R. Hartmann, P. Houston, Adaptive discontinuous Galerkin finite element methods for the compressible Euler equations, J. Comput. Phys., 183 (2002), 508–532. https://doi.org/10.1063/1.5033621 doi: 10.1063/1.5033621
    [4] F. Bassi, S. Rebay., High-order accurate discontinuous finite element solution of the 2D Euler equations, J. Comput. Phys., 138 (1997), 251–285. https://doi.org/10.1006/jcph.1997.5454 doi: 10.1006/jcph.1997.5454
    [5] F. Bassi, S. Rebay., A high-order accurate discontinuous finite element method for the numerical solution of the compressible Navier-Stokes equations, J. Comput. Phys., 131 (1997), 267–279. https://doi.org/10.1006/jcph.1996.5572 doi: 10.1006/jcph.1996.5572
    [6] L. Chen, M. B. Ozakin, R. Zhao, H. Bagci, A locally-implicit discontinuous Galerkin time-domain method to simulate metasurfaces using generalized sheet transition conditions, IEEE Trans. Antennas Propag., 71 (2023), 869–881. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887215 doi: 10.1109/AP-S/USNC-URSI47032.2022.9887215
    [7] G. S. Baruzzi, Wagdi Habashi, A second order finite element method for the solution of the transonic Euler and Navier-Stokes equations, Int. J. Numer. Methods Fluids., 20 (1995), 671–693. https://doi.org/10.1002/fld.1650200802 doi: 10.1002/fld.1650200802
    [8] M. Gurris, D. Kuzmin, S. Turek., Implicit finite element schemes for the stationary compressible Euler equations, Int. J. Numer. Methods Fluids., 69 (2012), 1–28. https://doi.org/10.1002/fld.2532 doi: 10.1002/fld.2532
    [9] A. Jameson, D. Mavriplis, Finite volume solution of the two-dimensional Euler equations on a regular triangular mesh, AIAA J, 24 (1986), 611–618. https://doi.org/10.2514/3.9315 doi: 10.2514/3.9315
    [10] W. K. Anderson, Comparison of finite volume flux vector splittings for the Euler equations, AIAA J., 24 (2015), 1453–1460. https://doi.org/10.2514/3.9465 doi: 10.2514/3.9465
    [11] L. Acedo, S. B. Yuste, An explicit finite difference method and a new von Neumann-type stability analysis for fractional diffusion equations, SIAM J. Numer. Anal., 42 (2005), 1862–1874. https://doi.org/10.1137/030602666 doi: 10.1137/030602666
    [12] R. F. Warming, B. J. Hyett, The modified equation approach to the stability and accuracy of finite difference method, J. Comput. Phys., 14 (1974), 159–179. https://doi.org/10.1016/0021-9991(74)90011-4 doi: 10.1016/0021-9991(74)90011-4
    [13] U. Baur, P. Benner, L. Feng, Model order reduction for linear and nonlinear systems: A system-theoretic perspective, Arch. Comput. Methods Eng., 21 (2014), 331–358. https://doi.org/10.1007/s11831-014-9111-2 doi: 10.1007/s11831-014-9111-2
    [14] Z. Bai, Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems, Appl. Numer. Math., 43 (2002), 9–44. https://doi.org/10.1016/S0168-9274(02)00116-2 doi: 10.1016/S0168-9274(02)00116-2
    [15] S. Renee, M. Laura, P. Benjamin, W. Karen, Projection-based model reduction: Formulations for physics-based machine learning, Comput Fluids., 179 (2019), 704–717. https://doi.org/10.1016/j.compfluid.2018.07.021 doi: 10.1016/j.compfluid.2018.07.021
    [16] J. Jiang, Y. Chen, N. Akil, Offline-enhanced reduced basis method through adaptive construction of the surrogate training set, J Sci Comput., 73 (2017), 853–875. https://doi.org/10.1007/s10915-017-0551-3 doi: 10.1007/s10915-017-0551-3
    [17] Z. Peng, Y. Chen, Y. Cheng, F. Li, A reduced basis method for radiative transfer equation, J Sci Comput., 91 (2022), 5. https://doi.org/10.1007/s10915-022-01782-2 doi: 10.1007/s10915-022-01782-2
    [18] D. Binion., X. Chen, A Krylov enhanced proper orthogonal decomposition method for efficient nonlinear model reduction, Finite Elem. Anal. Des., 47 (2011), 728–738. https://doi.org/10.1016/j.finel.2011.02.004 doi: 10.1016/j.finel.2011.02.004
    [19] L. Sirovich., Turbulence and the dynamics of coherent structures part Ⅰ: Coherent structures, Appl. Math., 45 (1986), 561–571. https://doi.org/10.1090/qam/910464 doi: 10.1090/qam/910464
    [20] C. L. Pettit, P. S. Beran., Application of proper orthogonal decomposition to the discrete Euler equations, Int. J. Numer. Methods Eng., 55 (2002), 479–497. https://doi.org/10.1002/nme.510 doi: 10.1002/nme.510
    [21] J. Goss, K. Subbarao., Inlet shape optimization based on POD model reduction of the Euler equations, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, (2008), 5809. https://doi.org/10.2514/6.2008-5809 doi: 10.2514/6.2008-5809
    [22] G. Kerschen, J. C. Golinval, A. F. Vakakis, L. A. Bergman, The method of proper orthogonal decomposition for dynamical characterization and order reduction of mechanical systems: an overview, Nonlinear Dyn., 41 (2005), 147–169. https://doi.org/10.1007/s11071-005-2803-2 doi: 10.1007/s11071-005-2803-2
    [23] Q. Wang, N. Ripamonti, J. S. Hesthaven, Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism, J Comput Phys., 410 (2020), 109402. https://doi.org/10.1016/j.jcp.2020.109402 doi: 10.1016/j.jcp.2020.109402
    [24] T. Akman, Error estimates for space–time discontinuous Galerkin formulation based on proper orthogonal decomposition, Appl. Anal., 96 (2017), 461–482. https://doi.org/10.1080/00036811.2016.1143930 doi: 10.1080/00036811.2016.1143930
    [25] C. Gräßle, M. Hinze, POD reduced order modeling for evolution equations utilizing arbitrary finite element discretizations, Adv. Comput. Math., 44 (2018), 1941–1978. https://doi.org/10.1007/s10444-018-9620-x doi: 10.1007/s10444-018-9620-x
    [26] Z. D. Luo, F. Teng, J. Chen, A POD-based reduced-order Crank-Nicolson finite volume element extrapolating algorithm for 2D Sobolev equations, Math. Comput. Simul., 146 (2018) 118–133. https://doi.org/10.1016/j.matcom.2017.11.002 doi: 10.1016/j.matcom.2017.11.002
    [27] S. F Zhu, L. Dedè, A. Quarteroni, Isogeometric analysis and proper orthogonal decomposition for parabolic problems, Numer Math., 135 (2017), 333–370. https://doi.org/10.1007/s00211-016-0802-5 doi: 10.1007/s00211-016-0802-5
    [28] R. C. Li, Q. B. Wu, S. F. Zhu, Proper orthogonal decomposition with SUPG-stabilized isogeometric analysis for reduced order modelling of unsteady convection-dominated convection-diffusion-reaction problems, J Comput Phys., 387 (2019), 280–302. https://doi.org/10.1016/j.jcp.2019.02.051 doi: 10.1016/j.jcp.2019.02.051
    [29] S. F Zhu, L. Dedè, A. Quarteroni, Isogeometric analysis and proper orthogonal decomposition for the acoustic wave equation, ESAIM: M2AN, 51 (2017), 1197–1221. https://doi.org/10.1051/m2an/2016056 doi: 10.1051/m2an/2016056
    [30] S. Jun, K. H Park, H. M Kang, D. H Lee, M. Cho, Reduced order model of three-dimensional Euler equations using proper orthogonal decomposition basis, J Mech Sci Technol., 24 (2010), 601–608. https://doi.org/10.1007/s12206-010-0106-0 doi: 10.1007/s12206-010-0106-0
    [31] R. Hartmann, Error estimation and adjoint based refinement for an adjoint consistent DG discretisation of the compressible Euler equations, Int J Comput Sci Mat, 1 (2007), 207–220. https://doi.org/10.1504/IJCSM.2007.016532 doi: 10.1504/IJCSM.2007.016532
    [32] X. Zhang, C. Shu, Positivity-preserving high order discontinuous Galerkin schemes for compressible Euler equations with source terms, J Comput Phys., 230 (2011), 1238–1248. https://doi.org/10.1016/j.jcp.2010.10.036 doi: 10.1016/j.jcp.2010.10.036
    [33] M Boizard, R Boyer, G Favier, P. Larzabal, Fast multilinear Singular Values Decomposition for higher-order Hankel tensors, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), (2014), 437–440. https://doi.org/10.1109/SAM.2014.6882436 doi: 10.1109/SAM.2014.6882436
    [34] P. Batten, N. Clarke, C. Lambert, D. M. Causon, On the choice of wavespeeds for the HLLC Riemann solver, SIAM J Sci Comput, 18 (1997), 1553–1570. https://doi.org/10.1137/S1064827593260140 doi: 10.1137/S1064827593260140
    [35] S. Simon, J. C. Mandal, A cure for numerical shock instability in HLLC Riemann solver using antidiffusion control, Comput Fluids., 174 (2018), 144–166. https://doi.org/10.1016/j.compfluid.2018.07.001 doi: 10.1016/j.compfluid.2018.07.001
    [36] S. Simon, J. C. Mandal, A simple cure for numerical shock instability in the HLLC Riemann solver, J Comput Phys., 378 (2019), 477–496. https://doi.org/10.1016/j.jcp.2018.11.022 doi: 10.1016/j.jcp.2018.11.022
    [37] Y. Zhu., A. C. Cangellaris, Multigrid Finite Element Methods for Electromagnetic Field Modeling, New York: John Wiley & Sons, 2006.
    [38] B. Cockburn, S. Hou, C. W Shu, TVD Runge-Kutta local projection discontinuous Galerkin finite element method for conservation laws Ⅳ: the multidimensional case, Math Comp, 55 (1990), 545–581. https://doi.org/10.1090/S0025-5718-1990-1010597-0 doi: 10.1090/S0025-5718-1990-1010597-0
    [39] H. Zhang, J. Y Yan, X. Qian, X. M Gu, S. H Song, On the preserving of the maximum principle and energy stability of high-order implicit-explicit Runge-Kutta schemes for the space-fractional Allen-Cahn equation, Numer. Algorithms, 88 (2021), 1309–1336. https://doi.org/10.1007/s11075-021-01077-x doi: 10.1007/s11075-021-01077-x
    [40] H. Luo, J. Baum, R. Löhner, A fast p-Multigrid Discontinuous Galerkin Method for Compressible Flows at All Speeds, AIAA J, 46 (2008), 635–652. https://doi.org/10.2514/1.28314 doi: 10.2514/1.28314
    [41] J. Burkardt, M. Gunzburger, H. C. Lee, POD and CVT-based reduced-order modeling of Navier-Stokes flows, Comput. Methods Appl. Mech. Eng., 196 (2006), 337–355. https://doi.org/10.1016/j.cma.2006.04.004 doi: 10.1016/j.cma.2006.04.004
    [42] Z. Luo, J. Gao, A POD reduced-order finite difference time-domain extrapolating scheme for the 2D Maxwell equations in a lossy medium, J. Math. Anal. Appl., 444 (2016), 433–451. https://doi.org/10.1016/j.jmaa.2016.06.036 doi: 10.1016/j.jmaa.2016.06.036
    [43] K. Kunisch, S. Volkwein, Galerkin proper orthogonal decomposition methods for parabolic problems, Numer. Math., 90 (2001), 117–148. https://doi.org/10.1007/s002110100282 doi: 10.1007/s002110100282
    [44] V. Schmitt, Pressure distributions on the ONERA M6-wing at transonic Mach numbers, experimental data base for computer program assessment, AGARD AR138, (1979).
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