In this work, reduced order models are presented for fluid flows characterized by different Mach numbers, ranging from low-speed, highly viscous fluid flows to weakly compressible flows. To populate the initial database of high-fidelity solutions, a high-fidelity solver based on the discontinuous Galerkin method was used, given its capability to deal with both fluids at low Reynolds and convection-dominated problems. Two distinct approaches, depending on the type of equations -either compressible or incompressible- which are solved at the full order level, were used to enhance the stability of the resulting reduced order models. Both aforementioned approaches rely on the proper orthogonal decomposition and on the manipulation of the governing equations when projected onto the reduced space. Specifically, these manipulations consist of ⅰ) the enforcement of the Poisson pressure equations when incompressible solvers are used, and ⅱ) the linearization of the governing equations when compressibility is kept into account. Test cases of the proposed methodology are presented, carried out on both internal and external flows.
| Citation: |
| [1] |
I. Akhtar, A. H. Nayfeh and C. J. Ribbens, On the stability and extension of reduced-order Galerkin models in incompressible flows, Theoretical and Computational Fluid Dynamics, 23 (2009), 213-237.
doi: 10.1007/s00162-009-0112-y.
|
| [2] |
D. Amsallem and C. Farhat, Stabilization of projection-based reduced-order models, International Journal for Numerical Methods in Engineering, 91 (2012), 358-377.
doi: 10.1002/nme.4274.
|
| [3] |
D. N Arnold, An Interior Penalty finite element method with discontinuous elements, SIAM, J. Numerical Analysis, 19 (1982), 742-760.
doi: 10.1137/0719052.
|
| [4] |
N. Aubry, P. Holmes, J. L. Lumley and E. Stone, The dynamics of coherent structures in the wall region of a turbulent boundary layer, Journal of Fluid Mechanics, 192 (1988), 115-173.
doi: 10.1017/S0022112088001818.
|
| [5] |
S. Badia and A. Hierro, On discrete maximum principles for discontinuous Galerkin methods, Computer Methods in Applied Mechanics and Engineering, 286 (2015), 107-122.
doi: 10.1016/j.cma.2014.12.006.
|
| [6] |
A. Baggag, H. Atkins and D. Keyes, Parallel implementation of the discontinuous Galerkin method, No. NAS 1.26: 209546 (1999).
|
| [7] |
M. Balajewicz, I. Tezaur and E. Dowell, Minimal subspace rotation on the Stiefel manifold for stabilization and enhancement of projection-based reduced order models for the compressible Navier–Stokes equations, Journal of Computational Physics, 321 (2016), 224-241.
doi: 10.1016/j.jcp.2016.05.037.
|
| [8] |
M. F. Barone, I. Kalashnikova, D. J. Segalman and H. K. Thornquist, Stable Galerkin reduced order models for linearized compressible flow, Journal of Computational Physics, 228 (2009), 1932-1946.
doi: 10.1016/j.jcp.2008.11.015.
|
| [9] |
F. Bassi and S. Rebay, Accurate 2D Euler computations by means of a high order discontinuous finite element method, Fourteenth International Conference on Numerical Methods in Fluid Dynamics, 453 (1995), 234-240.
doi: 10.1007/3-540-59280-6_128.
|
| [10] |
F. Bassi and S. Rebay, A high-order accurate discontinuous finite element method for the numerical solution of the compressible Navier-Stokes equations, Journal of Computational Physics, 131 (1997), 267-279.
doi: 10.1006/jcph.1996.5572.
|
| [11] |
C. E. Baumann and J. T. Oden, A discontinuous hp finite element method for the Euler and Navier-Stokes equations, International Journal for Numerical Methods in Fluids, 31 (1999), 79-95, https://doi.org/10.1002/(SICI)1097-0363(19990915)31: 1 < 79: : AID-FLD956 > 3.0.CO; 2-C
|
| [12] |
P. Benner, M. Ohlberger, A. Pater, G. Rozza and K. Urban, Model Reduction of Parametrized Systems, MS & A series, Vol. 17, Springer, 2017.
doi: 10.1007/978-3-319-58786-8.
|
| [13] |
M. Bergmann, C.-H. Bruneau and A. Iollo, Enablers for robust POD models, Journal of Computational Physics, 228 (2009), 516-538.
doi: 10.1016/j.jcp.2008.09.024.
|
| [14] |
T. Bui-Thanh, K. Willcox, O. Ghattas and B. van Bloemen Waanders, Goal-oriented, model-constrained optimization for reduction of large-scale systems, Journal of Computational Physics, 224 (2007), 880-896.
doi: 10.1016/j.jcp.2006.10.026.
|
| [15] |
J. Chan, Entropy stable reduced order modeling of nonlinear conservation laws, Journal of Computational Physics, 423 (2020), 109789, 27 pp.
doi: 10.1016/j.jcp.2020.109789.
|
| [16] |
F. Chinesta, A. Huerta, G. Rozza and K. Willcox, Model order reduction, Encyclopedia of Computational Mechanics, 2016, John Wiley & Sons, New York, 1-36.
|
| [17] |
B. Cockburn, Discontinuous Galerkin methods for convection-dominated problems, High-Order Methods for Computational Physics, Springer, Berlin Heidelberg, 1999, 69-224.
doi: 10.1007/978-3-662-03882-6_2.
|
| [18] |
B. Cockburn, G. E. Karniadakis and C. W. Shu, Discontinuous Galerkin Methods: Theory, Computation and Applications, Springer Science & Business Media, 2012.
|
| [19] |
B. Cockburn and C.-W. Shu, Runge-Kutta Discontinuous Galerkin Methods for convective dominated problem, Journal of Computational Physics, 16 (2001), 173-261.
doi: 10.1023/A:1012873910884.
|
| [20] |
Y. Feng, W. Yang, L. Sun, Z. Lin and Y. Zhang, High-performance implementation of matrix-free Runge-Kutta discontinuous Galerkin method for Euler equations, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018, 59-66.
doi: 10.1109/HPCC/SmartCity/DSS.2018.00040.
|
| [21] |
A. Ferrero, A. Iollo and F. Larocca, Global and local POD models for the prediction of compressible flows with DG methods, International Journal for Numerical Methods in Engineering, 116 (2018), 332-357.
doi: 10.1002/nme.5927.
|
| [22] |
L. Fick, Y. Maday, A. T. Patera and T. Taddei, A reduced basis technique for long-time unsteady turbulent flows, preprint, 2017, arXiv: 1710.03569.
|
| [23] |
G. Guennebaud, B. Jacob, P. Avery, A. Bachrach, S. Barthelemy and others, Eigen V3, 2020.
|
| [24] |
J. S. Hesthaven, G. Rozza and B. Stamm, Certified Reduced Basis Methods for Parametrized Partial Differential Equations, Springer, 2016.
doi: 10.1007/978-3-319-22470-1.
|
| [25] |
J. S. Hesthaven and T. Warburton, Nodal Discontinuous Galerkin Methods: Algorithms, Analysis, and Applications, Springer, New York, 2008.
doi: 10.1007/978-0-387-72067-8.
|
| [26] |
S. Hijazi, G. Stabile, A. Mola and G. Rozza, Data-driven POD-Galerkin reduced order model for turbulent flows, Journal of Computational Physics, 416 (2020), 232-261.
doi: 10.1016/j.jcp.2020.109513.
|
| [27] |
P. Holmes, J. L. Lumley, G. Berkooz and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge university press, 2012.
doi: 10.1017/CBO9780511919701.
|
| [28] |
K. Ito and S. S. Ravindran, A reduced-order method for simulation and control of fluid flows, Journal of Computational Physics, 143 (1998), 403-425.
doi: 10.1006/jcph.1998.5943.
|
| [29] |
I. Kalashnikova and S. Arunajatesan, A stable Galerkin reduced order model (ROM) for compressible flow, 10th World Congress for Computational Mechanics (WCCM), 2012.
|
| [30] |
I. Kalashnikova and M. F. Barone, On the stability and convergence of a Galerkin reduced order model (ROM) of compressible flow with solid wall and far-field boundary treatment, International Journal for Numerical Methods in Engineering, 83 (2010), 1345-1375.
doi: 10.1002/nme.2867.
|
| [31] |
I. Kalashnikova and M. Barone, Stable and efficient Galerkin reduced order models for non-linear fluid flow, 6th AIAA Theoretical Fluid Mechanics Conference, 2011.
doi: 10.2514/6.2011-3110.
|
| [32] |
X. Liu, Z. Wang, H. Ji and H. Gong, Application and comparison of several adaptive sampling algorithms in reduced order modeling, Heliyon, 10 (2024).
doi: 10.1016/j.heliyon.2024.e34928.
|
| [33] |
N. C. Nguyen and J. Peraire, Efficient and accurate nonlinear model reduction via first-order empirical interpolation, J. Comput. Phys., 494 (2023), Paper No. 112512, 19 pp, arXiv: 2305.00466.
doi: 10.1016/j.jcp.2023.112512.
|
| [34] |
N. C. Nguyen, J. Peraire and B. Cockburn, An implicit high-order hybridizable discontinuous Galerkin method for linear convection-diffusion equations, Journal of Computational Physics, 228 (2009), 3232-3254.
doi: 10.1016/j.jcp.2009.01.030.
|
| [35] |
N. C. Nguyen, J. Peraire and B. Cockburn, An implicit high-order hybridizable discontinuous Galerkin method for the incompressible Navier–Stokes equations, Journal of Computational Physics, 230 (2011), 1147-1170.
doi: 10.1016/j.jcp.2010.10.032.
|
| [36] |
W. Pazner and P.-O. Persson, Stage-parallel fully implicit Runge–Kutta solvers for discontinuous Galerkin fluid simulations, Journal of Computational Physics, 335 (2017), 700-717.
doi: 10.1016/j.jcp.2017.01.050.
|
| [37] |
B. Peherstorfer, D. Butnaru, K. Willcox and H.-J. Bungartz, Localized discrete empirical interpolation method, SIAM Journal on Scientific Computing, 36 (2014), A168–A192.
doi: 10.1137/130924408.
|
| [38] |
R. Rannacher, On Chorin's projection method for the incompressible Navier-Stokes equations, in The Navier-Stokes Equations II—Theory and Numerical Methods, Springer, 1992,167-183.
doi: 10.1007/BFb0090341.
|
| [39] |
W. H. Reed and T. R. Hill, Triangular Methods for the Neutron Transport Equation, Los Alamos Scientific Laboratory Report, LA-UR-73-479, 1973.
|
| [40] |
C. W. Rowley, Model reduction for fluids, using balanced proper orthogonal decomposition, International Journal of Bifurcation and Chaos, 15 (2005), 997-1013.
doi: 10.1142/S0218127405012429.
|
| [41] |
A. Quarteroni, A. Manzoni and F. Negri, Reduced Basis Methods for Partial Differential Equations: An Introduction, Springer, 2015.
doi: 10.1007/978-3-319-15431-2.
|
| [42] |
D. Sipp, M. Fosas de Pando and P. J. Schmid, Nonlinear model reduction: A comparison between POD-Galerkin and POD-DEIM methods, Computers & Fluids, 208 (2020), 104628.
doi: 10.1016/j.compfluid.2020.104628.
|
| [43] |
S. Sirisup and G. E. Karniadakis, Stability and accuracy of periodic flow solutions obtained by a POD-penalty method, Physica D: Nonlinear Phenomena, 202 (2005), 218-237.
doi: 10.1016/j.physd.2005.02.006.
|
| [44] |
G. Stabile, S. Hijazi, A. Mola, S. Lorenzi and G. Rozza, POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: Vortex shedding around a circular cylinder, Communications in Applied and Industrial Mathematics, 8 (2017), 210-236.
doi: 10.1515/caim-2017-0011.
|
| [45] |
A. Uranga, P.-O. Persson, M. Drela and J. Peraire, Implicit large eddy simulation of transition to turbulence at low Reynolds numbers using a discontinuous Galerkin method, International Journal for Numerical Methods in Engineering, 87 (2011), 232-261.
doi: 10.1002/nme.3036.
|
| [46] |
K. Willcox and J. Peraire, Balanced model reduction via the proper orthogonal decomposition, AIAA journal, 40 (2002), 2323-2330.
doi: 10.2514/3.15326.
|
| [47] |
M. Yano, Discontinuous Galerkin reduced basis empirical quadrature procedure for model reduction of parametrized nonlinear conservation laws, Advances in Computational Mathematics, 45 (2019), 2287-2320.
doi: 10.1007/s10444-019-09710-z.
|
| [48] |
M. J. Zahr and P.-O. Persson, An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems, Journal of Computational Physics, 326 (2016), 516-543.
doi: 10.1016/j.jcp.2016.09.012.
|
| [49] |
Gmesh, A Three-Dimensional Finite Element Mesh Generator with Built-in pre- and Post-Processing Facilities. Available from: http://gmsh.info/.
|
| [50] |
HopeFOAM extension of OpenFOAM, 2017. Available from: https://github.com/HopeFOAM/HopeFOAM/.
|
| [51] |
ITHACA-DG. Available from: https://mathlab.sissa.it/ithaca-dg/.
|
| [52] |
OpenFOAM, 2020. Available from: https://www.openfoam.com/.
|
Triangularization of the duct
First four velocity modes (from top to bottom) for the square cylinder test case
First four pressure modes for the square cylinder test case
Velocity fields: reference solution (top) and reduced order solution for
Difference between full-order velocity field and the reduced order one (
Difference between full-order pressure field and the reduced order one (
L2-norm of the errors for velocity and pressure, on the left and on the right, respectively: total error (solid lines), projection error (dashed lines with stars), and integration error (dashed lines with dots)
POD cumulative value of the normalized eigenvalues for velocity (triangles) and pressure (dots)
Mesh of the computational domain of the duct
The first four modes for the velocity
POD cumulative value of the normalized eigenvalues for velocity (triangles) and density (dots)
L2-norm of the errors for velocity and density, on the left and on the right, respectively: total error (solid lines), projection error (dashed lines with stars), and integration error (dashed lines with dots)
Comparison between solutions at the same time instant. FOM solution on the top, ROM on the bottom
Mesh of the computational domain for the NACA 0012
The first three modes for velocity and density on the left and on the right, respectively
Flow velocity along the x- and y- axis (on the top and on the bottom, respectively) around the NACA0012 airfoil; on the left, the high-fidelity solution, and on the right, the correspondent ROM solution
Time histories of velocity vector components
Errors plotted against time: on the left, projection error (gray dotted line) and total error (continuous black line); on the right, total errors when different numbers of modes are considered (