Finally, we apply the method to some test examples.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:In this study, we first consider the time-relaxation model, which consists of adding the term $\kappa \left( u-\overline{u}\right) $ to the heat equation.
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Key Findings & Data:Then, an explicit discretization scheme for the model is introduced to find the finite difference solutions.
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Conclusion & Impact:Finally, we apply the method to some test examples.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Heat equation · time-relaxation model · differential filter
Abstract
In this study, we first consider the time-relaxation model, which consists of adding the term $\kappa \left( u-\overline{u}\right) $ to the heat equation. Then, an explicit discretization scheme for the model is introduced to find the finite difference solutions. We first obtain the solutions by using the scheme and then investigate the method’s consistency, stability, and convergence properties. We prove that the method is consistent and unconditionally stable for any given value of $r$ and appropriate values of $\kappa$ and $\delta$. As a result, the method obtained by adding the time relaxation term to the first-order finite-difference explicit method behaves like the second-order implicit method. Finally, we apply the method to some test examples.
İlhan, Ö., Işık, O. R., Bozkurt, S. (2023). Numerical analysis of a time relaxation finite difference method for the heat equation. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Volume: 72 Issue: 4. https://doi.org/10.31801/cfsuasmas.1243992
Adams, N. A., Stolz, S., Deconvolution methods for subgrid-scale approximation in LES, in Modern Simulation Strategies for Turbulent Flow (eds. B. Geurts and R.T. Edwards), (2001), 21–41.
Burden, R. L., Faires, J. D., Numerical Analysis, Brooks/Cole, Pacific Grove, 9th Edition, 2011.
Cibik, A., Eroglu, F. G., Kaya, S., Numerical analysis of an efficient second order time filtered backward Euler method for MHD equations, Journal of Scientific Computing, 82(2) (2020), 1–25. https://doi.org/10.48550/arXiv.1906.07922
Cooper, J., Introduction to Partial Differential Equations with Matlab, Birkhauser, Bosten, 1998.
Ervin, V. J., Layton, W., Neda, M., Numerical analysis of a higher order time relaxation model of fluids, Int. J. Numer. Anal. Model., 4 (2007), 648–670.
Fletcher, C. A. J., Computational Techniques for Fluid Dynamics, Springer-Verlag, Berlin, 1998.
Gresho, P. M., Sani, R. L., Incompressible Flow and the Finite Element Method, John Wiley & Sons, Inc, 1998.
Guzel, A., Layton, W., Time filters increase accuracy of the fully implicit method, BIT Numer. Math., 58(3) (2018), 301–315. DOI:10.1007/s10543-018-0695-z
Isik, O. R., Spin up problem and accelerating convergence to steady state, Applied Mathematical Modelling, 37(5) (2013), 3242–3253. https://doi.org/10.1016/j.apm.2012.07.033
Isik, O. R., Takhirov, A., Zheng, H., Second order time relaxation model for accelerating convergence to steady-state equilibrium for Navier-Stokes equations, Applied Numerical Mathematics, 119 (2017), 67–78. https://doi.org/10.1016/j.apnum.2017.03.016
Isik, O. R., Yuksel, G., Demir, B., Analysis of second order and unconditonally stable for BDF2-AB2 method for the Navier-Stokes Equations with nonlinear time relaxation, Numerical Methods for Partial Differential Equations, 34(6) (2018), 2060–2078. https://doi.org/10.1002/num.22276
Layton, W., Pruett, C. D., Rebholz, L. G. Temporally regularized direct numerical simulation, Appl. Math. Comput., 216 (2010), 3728–3738. DOI:10.1016/j.amc.2010.05.031
Morton, K. W., Mayers, D. F., Numerical Solution of Partial Differential Equations: An Introduction, Cambridge University Press, 1994.
Neda, M., Discontinuous time relaxation method for the time-dependent Navier-Stokes equations, Advance in Numerical Analysis, 2010, Article ID 419021, 21 pages, (2010). https://doi.org/10.1155/2010/419021
Pruett, C. D., Gatski, T. B., Grosch, C. E., Thacker, W. D., The temporally filtered Navier–Stokes equations: properties of the residual-stres, Phys. Fluids, 15 (2003), 2127–2140. https://doi.org/10.1063/1.1582858
Rosenau, P., Extending hydrodynamics via the regularization of the Chapman-Enskog expansion, Phys. Rev., A40(7193) (1989). https://doi.org/10.1103/PhysRevA.40.7193
Schochet, S., Tadmor, E., The regularized Chapman-Enskog expansion for scalar conservation laws, Arch. Ration. Mech. Anal., 119(95) (1992). https://doi.org/10.1007/BF00375117
Smith, G. D., Numerical Solution of Partial Differential Equations: Finite Difference Methods, Oxford University Press, 1985.
Stolz, S., Adams, N. A., Kleiser, L., The approximate deconvolution model for LES of compressible flows and its application to shock-turbulent boundary layer interaction, Phys. Fluids, 13(2985) (2001). https://doi.org/10.1063/1.1397277
Williams, P. D., A proposed modification to the Robert-Asselin time filter, Mon. Weather Re., 137(8) (2009), 2538–2546. https://doi.org/10.1175/2009MWR2724.1
Williams, P. D., The RAW filter: An improvement to the Robert-Asselin filter in semiimplicit integrations, Mon. Weather Rev., 139(6) (2011), 1996–2007. https://doi.org/10.1175/2010MWR3601.1