Skip to main content
Log in

Numerical investigation of two second-order, stabilized SAV ensemble methods for the Navier–Stokes equations

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

Abstract

In this report we present a second-order, stabilized SAV based, Crank–Nicolson leap-frog (CNLF) ensemble method, and perform a comprehensive numerical study of it as well as the Crank–Nicolson ensemble method with a linear extrapolation (CNLE) presented in Jiang and Yang (SIAM J. Sci. Comput. 43:A2869–A2896, 2021). Both methods are extremely efficient as only one linear system with multiple right hands needs to be solved at each time for a (potentially large) number of realizations of the flow problems. In particular the coefficient matrix of the fully discretized system is a constant matrix that does not change from one time step to another. We present extensive testing of these two methods and demonstrate the advantages of each. We also present long time stability analysis for both methods.

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. Albensoeder, S., Kuhlmann, H.: Accurate three-dimensional lid-driven cavity flow. J. Comput. Phys. 206, 536–558 (2005)

    Article  MATH  Google Scholar 

  2. Arnold, V.: Sur la topologic des ecoulements stationnaires des fluides parfaits. Comptes Rendus Hebdomadaires des Seances de l’Academie des Sciences 261, 17–20 (1965)

    Google Scholar 

  3. Babus̆ka, I., Nobile, F., Tempone, R.: A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J. Numer. Anal. 45, 1005–1034 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ben-Artzi, M., Croisille, J.-P., Fishelov, D.: Navier-stokes Equations in Planar Domains. Imperial College Press, London (2013)

    Book  MATH  Google Scholar 

  5. Calandra, H., Gratton, S., Langou, J., Pinel, X., Vasseur, X.: Flexible variants of block restarted GMRES methods with application to geophysics. SIAM J. Sci. Comput. 34(2), 714–736 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  6. Childress, S.: New solutions of the kinematic dynamo problem. J. Math. Phys. 11, 3063–3076 (1970)

    Article  MathSciNet  Google Scholar 

  7. Connors, J.: An ensemble-based conventional turbulence model for fluid-fluid interaction. Int. J. Numer. Anal. Model. 15, 492–519 (2018)

    MathSciNet  MATH  Google Scholar 

  8. Elman, H.C., Silvester, D.J., Wathen, A.J.: Finite Elements and Fast Iterative Solvers: With Applications in Incompressible Fluid Dynamics. Oxford University Press, New York (2005)

    MATH  Google Scholar 

  9. Fiordilino, J.: A second order ensemble timestepping algorithm for natural convection. SIAM J. Numer. Anal. 56, 816–837 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  10. Fiordilino, J.: Ensemble time-stepping algorithms for the heat equation with uncertain conductivity. Numer. Methods Partial Diff. Equa. 34, 1901–1916 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  11. Fiordilino, J., Khankan, S.: Ensemble timestepping algorithms for natural convection. Int. J. Numer. Anal. Model. 15, 524–551 (2018)

    MathSciNet  MATH  Google Scholar 

  12. Gallopulos, E., Simoncini, V.: Convergence of BLOCK GMRES and matrix polynomials. Lin. Alg. Appl. 247, 97–119 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  13. Guermond, J.-L., Quartapelle, L.: On stability and convergence of projection methods based on pressure Poisson equation. Int. J. Numer. Methods Fluids 26, 1039–1053 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  14. Gunzburger, M., Iliescu, T., Schneier, M.: A Leray regularized ensemble-proper orthogonal decomposition method for parameterized convection-dominated flows. IMA J. Numer. Anal. 40, 886–913 (2020)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  16. Gunzburger, M., Jiang, N., Schneier, M.: A higher-order ensemble/proper orthogonal decomposition method for the nonstationary Navier-Stokes equations. Int. J. Numer. Anal. Model. 15, 608–627 (2018)

    MathSciNet  MATH  Google Scholar 

  17. Gunzburger, M., Jiang, N., Wang, Z.: An efficient algorithm for simulating ensembles of parameterized flow problems. IMA J. Numer. Anal. 39, 1180–1205 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gunzburger, M., Jiang, N., Wang, Z.: A second-order time-stepping scheme for simulating ensembles of parameterized flow problems. Comput. Methods Appl. Math. 19, 681–701 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  19. He, X., Jiang, N., Qiu, C.: An artificial compressibility ensemble algorithm for a stochastic Stokes-Darcy model with random hydraulic conductivity and interface conditions. Int. J. Numer. Methods Eng. 121, 712–739 (2020)

    Article  MathSciNet  Google Scholar 

  20. Hecht, F.: New development in FreeFem++. J. Numer. Math. 20, 251–265 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Helton, J.C., Davis, F.J.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab. Eng. Syst. Saf. 81, 23–69 (2003)

    Article  Google Scholar 

  22. Hosder, S., Walters, R., Perez, R.: A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations, AIAA-Paper 2006-891. In: 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno. CD-ROM (January 2006)

  23. Jiang, N.: A higher order ensemble simulation algorithm for fluid flows. J. Sci. Comput. 64, 264–288 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Jiang, N.: A second-order ensemble method based on a blended backward differentiation formula timestepping scheme for time-dependent Navier-Stokes equations. Numer. Methods Partial Diff. Equa. 33, 34–61 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  25. Jiang, N.: A pressure-correction ensemble scheme for computing evolutionary Boussinesq equations. J. Sci. Comput. 80, 315–350 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  26. Jiang, N., Kaya, S., Layton, W.: Analysis of model variance for ensemble based turbulence modeling. Comput. Methods Appl. Math. 15, 173–188 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  27. Jiang, N., Layton, W.: An algorithm for fast calculation of flow ensembles. Int. J. Uncertain. Quantif. 4, 273–301 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  28. Jiang, N., Layton, W.: Numerical analysis of two ensemble eddy viscosity numerical regularizations of fluid motion. Numer. Methods Partial Diff. Equa. 31, 630–651 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  29. Jiang, N., Li, Y., Yang, H.: An artificial compressibility Crank–Nicolson leap-frog method for the Stokes–Darcy model and application in ensemble simulations. SIAM J. Numer. Anal. 59, 401–428 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  30. Jiang, N., Li, Y., Yang, H.: A second order ensemble method with different subdomain time steps for simulating coupled surface-groundwater flows, accepted in Numerical Methods for Partial Differential Equations, in press. https://onlinelibrary.wiley.com/doi/10.1002/num.22846 (2022)

  31. Jiang, N., Qiu, C.: An efficient ensemble algorithm for numerical approximation of stochastic Stokes-Darcy equations. Comput. Methods Appl. Mech. Eng. 343, 249–275 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  32. Jiang, N., Qiu, C.: Numerical analysis of a second order ensemble algorithm for numerical approximation of stochastic Stokes-Darcy equations. J. Comput. Appl. Math. 406, 113934 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  33. Jiang, N., Schneier, M.: An efficient, partitioned ensemble algorithm for simulating ensembles of evolutionary MHD flows at low magnetic Reynolds number. Numer. Methods Partial Diff. Equ. 34, 2129–2152 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  34. Jiang, N., Tran, H.: Analysis of a stabilized CNLF method with fast slow wave splittings for flow problems. Comput. Methods Appl. Math. 15(3), 307–330 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  35. Jiang, N., Yang, H.: Stabilized scalar auxiliary variable ensemble algorithms for parameterized flow problems. SIAM J. Sci. Comput. 43, A2869–A2896 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  36. Jiang, N., Yang, H.: SAV decoupled ensemble algorithms for fast computation of Stokes-Darcy flow ensembles. Comput. Methods Appl. Mech. Eng. 387, 114150 (2021)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  38. Ju, L., Leng, W., Wang, Z., Yuan, S.: Numerical investigation of ensemble methods with block iterative solvers for evolution problems. Discret. Cont. Dyna. Syst. - Series B 25, 4905–4923 (2020)

    MathSciNet  MATH  Google Scholar 

  39. Kuo, F., Schwab, C., Sloan, I.: Quasi-Monte Carlo finite element methods for a class of elliptic partial differential equations with random coefficients. SIAM J. Numer. Anal. 50, 3351–3374 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  40. Layton, W., Takhirov, A., Sussman, M.: Instability of Crank-Nicolson leap-frog for nonautonomous systems. Int. J. Numer. Anal. Model. Ser. B 5, 289–298 (2014)

    MathSciNet  MATH  Google Scholar 

  41. Li, X., Shen, J.: Error analysis of the SAV-MAC scheme for the Navier-Stokes equations. SIAM J. Numer. Anal. 58, 2465–2491 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  42. Li, X., Shen, J., Liu, Z.: New SAV-pressure correction methods for the Navier-Stokes equations: stability and error analysis. Math. Comput. 91, 141–167 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  43. Lin, L., Yang, Z., Dong, S.: Numerical approximation of incompressible Naiver-Stokes equations based on an auxiliary energy variable. J. Comput. Phys. 388, 1–22 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  44. Luo, Y., Wang, Z.: An ensemble algorithm for numerical solutions to deterministic and random parabolic PDEs. SIAM J. Numer. Anal. 56, 859–876 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  45. Luo, Y., Wang, Z.: A multilevel Monte Carlo ensemble scheme for random parabolic PDEs. SIAM J. Sci. Comput. 41, A622–A642 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  46. McCarthy, J.F.: Block-conjugate-gradient method. Phys. Rev. D 40, 2149 (1989)

    Article  Google Scholar 

  47. Mohebujjaman, M., Rebholz, L.: An efficient algorithm for computation of MHD flow ensembles. Comput. Methods Appl. Math. 17, 121–137 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  48. Reagan, M., Najm, H.N., Ghanem, R.G., Knio, O.M.: Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection. Combus. Flame 132, 545–555 (2003)

    Article  Google Scholar 

  49. Schäfer, M., Turek, S.: Benchmark computations of laminar flow around cylinder. In: Flow Simulation with HighPerformance Computers II, Notes Numer. Fluid Mech, vol. 52, pp 547–566. Vieweg, Wiesbaden (1996)

  50. Takhirov, A., Neda, M., Waters, J.: Time relaxation algorithm for flow ensembles. Numer. Methods Partial Diff. Equ. 32, 757–777 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  51. Takhirov, A., Waters, J.: Ensemble algorithm for parametrized flow problems with energy stable open boundary conditions. Comput. Methods Appl. Math. 20, 531–554 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  52. Tavelli, M., Dumbser, M.: A staggered space-time discontinuous Galerkin method for the three-dimensional incompressible Navier-Stokes equations on unstructured tetrahedral meshes. J. Comput. Phys. 319, 294–323 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  53. Xiu, D., Hesthaven, J.S.: High-order collocation methods for differential equations with random inputs. SIAM J. Sci. Comput. 27, 1118–1139 (2005)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Funding

N. Jiang was partially supported by the US National Science Foundation grants DMS-1720001, DMS-2120413, and DMS-2143331. H. Yang was supported in part by the National Natural Science Foundation of China under grant 11801348, the key research projects of general universities in Guangdong Province (grant no. 2019KZDXM034), and the basic research and applied basic research projects in Guangdong Province (Projects of Guangdong, Hong Kong and Macao Center for Applied Mathematics, grant no. 2020B1515310018).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huanhuan Yang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Communicated by: Gianluigi Rozza

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, N., Yang, H. Numerical investigation of two second-order, stabilized SAV ensemble methods for the Navier–Stokes equations. Adv Comput Math 48, 65 (2022). https://doi.org/10.1007/s10444-022-09977-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10444-022-09977-9

Keywords

Mathematics Subject Classification (2010)

Navigation