Skip to main content
Log in

Commutation error in reduced order modeling of fluid flows

  • Published:
Advances in Computational Mathematics Aims and scope Submit manuscript

Abstract

For reduced order models (ROMs) of fluid flows, we investigate theoretically and computationally whether differentiation and ROM spatial filtering commute, i.e., whether the commutation error (CE) is nonzero. We study the CE for the Laplacian and two ROM filters: the ROM projection and the ROM differential filter. Furthermore, when the CE is nonzero, we investigate whether it has any significant effect on ROMs that are constructed by using spatial filtering. As numerical tests, we use the Burgers equation with viscosities ν = 10− 1 and ν = 10− 3 and a 2D flow past a circular cylinder at Reynolds numbers Re = 100 and Re = 500. Our investigation (i) measures the size of the CE in these test problems and (ii) shows that the CE has a significant effect on ROM development for high viscosities, but not so much for low viscosities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Baiges, J., Codina, R., Idelsohn, S.: Reduced-order subscales for POD models. Comput. Methods Appl. Mech. Engrg. 291, 173–196 (2015)

    Article  MathSciNet  Google Scholar 

  2. Ballarin, F., Manzoni, A., Quarteroni, A., Rozza, G.: Supremizer stabilization of POD–galerkin approximation of parametrized steady incompressible Navier–Stokes equations. Int. J. Numer. Meth. Engng. 102, 1136–1161 (2015)

    Article  MathSciNet  Google Scholar 

  3. Benosman, M., Borggaard, J., San, O., Kramer, B.: Learning-based robust stabilization for reduced-order models of 2D and 3D Boussinesq equations. Appl. Math. Model. 49, 162–181 (2017)

    Article  MathSciNet  Google Scholar 

  4. Bergmann, M., Ferrero, A., Iollo, A., Lombardi, E., Scardigli, A., Telib, H.: A zonal Galerkin-free POD model for incompressible flows. J. Comput. Phys. 352, 301–325 (2018)

    Article  MathSciNet  Google Scholar 

  5. Berselli, L.C., Iliescu, T., Layton, W.J.: Mathematics of large eddy simulation of turbulent flows. Scientific Computation. Springer, Berlin (2006)

    MATH  Google Scholar 

  6. Brenner, S., Scott, R.: The mathematical theory of finite element methods, vol. 15. Springer, Berlin (2007)

    Google Scholar 

  7. Brunton, S.L., Proctor, J.L., Kutz, J.N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. 113(15), 3932–3937 (2016)

    Article  MathSciNet  Google Scholar 

  8. Carlberg, K., Barone, M., Antil, H.: Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction. J. Comput. Phys. 330, 693–734 (2017)

    Article  MathSciNet  Google Scholar 

  9. Couplet, M., Sagaut, P., Basdevant, C.: Intermodal energy transfers in a proper orthogonal decomposition–Galerkin representation of a turbulent separated flow. J. Fluid Mech. 491, 275–284 (2003)

    Article  MathSciNet  Google Scholar 

  10. Fareed, H., Singler, J.R.: A note on incremental pod algorithms for continuous time data. Appl. Numer. Math. (2019)

  11. Feppon, F., Lermusiaux, P.F.J.: Dynamically orthogonal numerical schemes for efficient stochastic advection and Lagrangian transport. SIAM Rev. 60(3), 595–625 (2018)

    Article  MathSciNet  Google Scholar 

  12. Fick, L., Maday, Y., Patera, A.T., Taddei, T.: A stabilized POD model for turbulent flows over a range of Reynolds numbers: optimal parameter sampling and constrained projection. J. Comp. Phys. 371, 214–243 (2018)

    Article  MathSciNet  Google Scholar 

  13. Gouasmi, A., Parish, E.J., Duraisamy, K.: A priori estimation of memory effects in reduced-order models of nonlinear systems using the Mori–Zwanzig formalism. Proc. R. Soc. A 473(2205), 20170385 (2017)

    Article  MathSciNet  Google Scholar 

  14. Gunzburger, M., Jiang, N., Schneier, M.: An ensemble-proper orthogonal decomposition method for the nonstationary Navier-Stokes equations. SIAM J. Numer. Anal. 55(1), 286–304 (2017)

    Article  MathSciNet  Google Scholar 

  15. Hesthaven, J.S., Rozza, G., Stamm, B.: Certified reduced basis methods for parametrized partial differential equations. Springer, Berlin (2015)

    MATH  Google Scholar 

  16. Holmes, P., Lumley, J.L., Berkooz, G.: Turbulence, coherent structures, dynamical systems and symmetry. Cambridge (1996)

  17. Iliescu, T., Wang, Z.: Are the snapshot difference quotients needed in the proper orthogonal decomposition? SIAM J. Sci. Comput. 36(3), A1221–A1250 (2014)

    Article  MathSciNet  Google Scholar 

  18. Iliescu, T., Wang, Z.: Variational multiscale proper orthogonal decomposition: Navier-Stokes equations. Num. Meth. P.D.E.s 30(2), 641–663 (2014)

    Article  MathSciNet  Google Scholar 

  19. John, V.: Reference values for drag and lift of a two dimensional time-dependent flow around a cylinder. Int. J. Num. Meth. Fluids 44, 777–788 (2004)

    Article  Google Scholar 

  20. John, V., Linke, A., Merdon, C., Neilan, M., Rebholz, L.G.: On the divergence constraint in mixed finite element methods for incompressible flows. SIAM Rev. (2016)

  21. Kondrashov, D., Chekroun, M.D., Ghil, M.: Data-driven non-Markovian closure models. Phys. D 297, 33–55 (2015)

    Article  MathSciNet  Google Scholar 

  22. Kunisch, K., Volkwein, S.: Galerkin proper orthogonal decomposition methods for parabolic problems. Numer. Math. 90(1), 117–148 (2001)

    Article  MathSciNet  Google Scholar 

  23. Loiseau, J.C., Brunton, S.L.: Constrained sparse Galerkin regression. J. Fluid Mech. 838, 42–67 (2018)

    Article  MathSciNet  Google Scholar 

  24. Lu, F., Lin, K.K., Chorin, A.J.: Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation. Phys. D 340, 46–57 (2017)

    Article  MathSciNet  Google Scholar 

  25. Mohebujjaman, M., Rebholz, L.G., Iliescu, T.: Physically-constrained data-driven correction for reduced order modeling of fluid flows. Int. J. Num. Meth. Fluids 89(3), 103–122 (2019)

    Article  MathSciNet  Google Scholar 

  26. Mohebujjaman, M., Rebholz, L.G., Xie, X., Iliescu, T.: Energy balance and mass conservation in reduced order models of fluid flows. J. Comput. Phys. 346, 262–277 (2017)

    Article  MathSciNet  Google Scholar 

  27. Noack, B.R., Morzynski, M., Tadmor, G.: Reduced-order modelling for flow control, vol. 528 Springer Verlag (2011)

  28. Noack, B.R., Schlegel, M., Ahlborn, B., Mutschke, G., Morzynski, M., Comte, P., Tadmor, G.: A finite-time thermodynamics of unsteady fluid flows. J. Non-Equil. Thermody. 33(2), 103–148 (2008)

    Article  Google Scholar 

  29. Oberai, A.A., Jagalur-Mohan, J.: Approximate optimal projection for reduced-order models. Int. J. Num. Meth. Engng. 105(1), 63–80 (2016)

    Article  MathSciNet  Google Scholar 

  30. Östh, J., Noack, B.R., Krajnović, S., Barros, D., Borée, J.: On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body. J. Fluid Mech. 747, 518–544 (2014)

    Article  MathSciNet  Google Scholar 

  31. Pan, S., Duraisamy, K.: Data-driven discovery of closure models. SIAM J. Appl. Dyn. Syst. 17(4), 2381–2413 (2018)

    Article  MathSciNet  Google Scholar 

  32. Peherstorfer, B., Willcox, K.: Data-driven operator inference for nonintrusive projection-based model reduction. Comput. Methods Appl. Mech. Engrg. 306, 196–215 (2016)

    Article  MathSciNet  Google Scholar 

  33. Protas, B., Noack, B.R., Östh, J.: Optimal nonlinear eddy viscosity in Galerkin models of turbulent flows. J. Fluid Mech. 766, 337–367 (2015)

    Article  MathSciNet  Google Scholar 

  34. Quarteroni, A., Manzoni, A., Negri, F.: Reduced basis methods for partial differential equations: an introduction, vol. 92 Springer (2015)

  35. Rebholz, L., Xiao, M.: Improved accuracy in algebraic splitting methods for Navier-Stokes equations. SIAM J. Sci. Comput. 39(4), A1489–A1513 (2017)

    Article  MathSciNet  Google Scholar 

  36. Rebollo, T.C., Avila, E.D., Mármol, M. G., Ballarin, F., Rozza, G.: On a certified Smagorinsky reduced basis turbulence model. SIAM J. Numer. Anal. 55(6), 3047–3067 (2017)

    Article  MathSciNet  Google Scholar 

  37. San, O., Maulik, R.: Neural network closures for nonlinear model order reduction. Adv. Comput. Math. 44(6), 1717–1750 (2018)

    Article  MathSciNet  Google Scholar 

  38. Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010)

    Article  MathSciNet  Google Scholar 

  39. 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)

    Article  MathSciNet  Google Scholar 

  40. Strazzullo, M., Ballarin, F., Mosetti, R., Rozza, G.: Model reduction for parametrized optimal control problems in environmental marine sciences and engineering. SIAM J. Sci. Comput. 40(4), B1055–B1079 (2018)

    Article  MathSciNet  Google Scholar 

  41. Taira, K., Brunton, S.L., Dawson, S., Rowley, C.W., Colonius, T., McKeon, B.J., Schmidt, O.T., Gordeyev, S., Theofilis, V., Ukeiley, L.S.: Modal analysis of fluid flows: an overview. AIAA J.: 4013–4041 (2017)

    Article  Google Scholar 

  42. Wang, Z., Akhtar, I., Borggaard, J., Iliescu, T.: Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison. Comput. Meth. Appl. Mech. Eng. 237-240, 10–26 (2012)

    Article  MathSciNet  Google Scholar 

  43. Wells, D., Wang, Z., Xie, X., Iliescu, T.: An evolve-then-filter regularized reduced order model for convection-dominated flows. Int. J. Num. Meth. Fluids 84, 598–615 (2017)

    Article  MathSciNet  Google Scholar 

  44. Xie, X., Mohebujjaman, M., Rebholz, L.G., Iliescu, T.: Data-driven filtered reduced order modeling of fluid flows. SIAM J. Sci. Comput. 40(3), B834–B857 (2018)

    Article  MathSciNet  Google Scholar 

  45. Xie, X., Wells, D., Wang, Z., Iliescu, T.: Approximate deconvolution reduced order modeling. Comput. Methods Appl. Mech. Engrg. 313, 512–534 (2017)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

We thank the three anonymous reviewers for their constructive comments and suggestions, which significantly improved the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Birgul Koc.

Additional information

Communicated by: Anthony Nouy

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Changhong Mou and Traian Iliescu are partially supported by DMS-1821145

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koc, B., Mohebujjaman, M., Mou, C. et al. Commutation error in reduced order modeling of fluid flows. Adv Comput Math 45, 2587–2621 (2019). https://doi.org/10.1007/s10444-019-09739-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10444-019-09739-0

Keywords

Mathematics Subject Classification (2010)

Navigation