In this letter, we revisit the invariant energy quadratization (IEQ) method and provide a new perspective on its ability to preserve the original energy dissipation laws. The IEQ method has been widely used to design energy stable numerical schemes for phase-field or gradient flow models. Although there are many merits of the IEQ method, one major disadvantage is that the IEQ method usually respects a modified energy law, where the modified energy is expressed in the auxiliary variables. Still, the dissipation laws in terms of the original energy are not guaranteed by the IEQ method. Using the widely-used Cahn-Hilliard equation as an example, we demonstrate that the Runge-Kutta IEQ method indeed can preserve the original energy dissipation laws for certain situations up to arbitrary high-order accuracy. Interested readers are encouraged to extend this idea to more general cases and apply it to other thermodynamically consistent models.
Citation: Zengyan Zhang, Yuezheng Gong, Jia Zhao. A remark on the invariant energy quadratization (IEQ) method for preserving the original energy dissipation laws[J]. Electronic Research Archive, 2022, 30(2): 701-714. doi: 10.3934/era.2022037
In this letter, we revisit the invariant energy quadratization (IEQ) method and provide a new perspective on its ability to preserve the original energy dissipation laws. The IEQ method has been widely used to design energy stable numerical schemes for phase-field or gradient flow models. Although there are many merits of the IEQ method, one major disadvantage is that the IEQ method usually respects a modified energy law, where the modified energy is expressed in the auxiliary variables. Still, the dissipation laws in terms of the original energy are not guaranteed by the IEQ method. Using the widely-used Cahn-Hilliard equation as an example, we demonstrate that the Runge-Kutta IEQ method indeed can preserve the original energy dissipation laws for certain situations up to arbitrary high-order accuracy. Interested readers are encouraged to extend this idea to more general cases and apply it to other thermodynamically consistent models.
[1] | I. Steinbach, Phase-field models in materials science, Modelling Simul. Mater. Sci. Eng., 17 (2009), 073001. https://doi.org/10.1088/0965-0393/17/7/073001 doi: 10.1088/0965-0393/17/7/073001 |
[2] | D. M. Anderson, G. B. McFadden, A. A. Wheeler, Diffuse-interface methods in fluid mechanics, Annu. Rev. Fluid Mech., 30 (1998), 139–165. https://doi.org/10.1146/annurev.fluid.30.1.139 doi: 10.1146/annurev.fluid.30.1.139 |
[3] | K. Elder, M. Grant, Modeling elastic and plastic deformations in nonequilibrium processing using phase field crystals, Phys. Rev. E, 70 (2004), 051605. https://doi.org/10.1103/PhysRevE.70.051605 doi: 10.1103/PhysRevE.70.051605 |
[4] | Z. Guo, F. Yu, P. Lin, S. M. Wise, J. Lowengrub, A diffuse domain method for two-phase flows with large density ratio in complex geometries, J. Fluid Mech., 907 (2021), A38. https://doi.org/10.1017/jfm.2020.790 doi: 10.1017/jfm.2020.790 |
[5] | C. Liu, J. Shen, X. Yang, Dynamics of defect motion in nematic liquid crystal flow: modeling and numerical simulation, Commun. Comput. Phys., 2 (2007), 1184–1198. |
[6] | L. Onsager, Reciprocal relations in irreversible processes. i, Phys. Rev., 37 (1931), 405. https://doi.org/10.1103/PhysRev.37.405 doi: 10.1103/PhysRev.37.405 |
[7] | L. Onsager, Reciprocal relations in irreversible processes. ii, Phys. Rev., 38 (1931), 2265. https://doi.org/10.1103/PhysRev.38.2265 doi: 10.1103/PhysRev.38.2265 |
[8] | X. Yang, J. Li, G. Forest, Q. Wang, Hydrodynamic theories for flows of active liquid crystals and the generalized Onsager principle, Entropy, 18 (2016), 202. https://doi.org/10.3390/e18060202 doi: 10.3390/e18060202 |
[9] | D. J. Eyre, Unconditionally gradient stable time marching the Cahn-Hilliard equation, MRS Online Proc. Libr. (OPL), 529 (1998). https://doi.org/10.1557/PROC-529-39 doi: 10.1557/PROC-529-39 |
[10] | C. Wang, X. Wang, S. M. Wise, Unconditionally stable schemes for equations of thin film epitaxy, Discrete Contin. Dyn. Syst., 28 (2010), 405. https://doi.org/10.3934/dcds.2010.28.405 doi: 10.3934/dcds.2010.28.405 |
[11] | F. Guillén-González, G. Tierra, On linear schemes for a Cahn-Hilliard diffuse interface model, J. Comput. Phys., 234 (2013), 140–171. https://doi.org/10.1016/j.jcp.2012.09.020 doi: 10.1016/j.jcp.2012.09.020 |
[12] | J. Shin, H. Lee, J. Lee, Unconditionally stable methods for gradient flow using convex splitting Runge-Kutta scheme, J. Comput. Phys., 347 (2017), 367–381. https://doi.org/10.1016/j.jcp.2017.07.006 doi: 10.1016/j.jcp.2017.07.006 |
[13] | L. Ju, X. Li, Z. Qiao, H. Zhang, Energy stability and error estimates of exponential time differencing schemes for the epitaxial growth model without slope selection, Math. Comput., 87 (2018), 1859–1885. https://doi.org/10.1090/mcom/3262 doi: 10.1090/mcom/3262 |
[14] | J. Shen, J. Xu, J. Yang, The scalar auxiliary variable (SAV) approach for gradient flows, J. Comput. Phys., 353 (2018), 407–416. https://doi.org/10.1016/j.jcp.2017.10.021 doi: 10.1016/j.jcp.2017.10.021 |
[15] | X. Yang, J. Zhao, Q. Wang, Numerical approximations for the molecular beam epitaxial growth model based on the invariant energy quadratization method, J. Comput. Phys., 333 (2017), 104–127. https://doi.org/10.1016/j.jcp.2016.12.025 doi: 10.1016/j.jcp.2016.12.025 |
[16] | L. Chen, Z. Zhang, J. Zhao, Numerical approximations of phase field models using a general class of linear time-integration schemes, Commun. Comput. Phys., 30 (2021), 1290–1322. https://doi.org/10.4208/cicp.OA-2020-0244 doi: 10.4208/cicp.OA-2020-0244 |
[17] | Y. Gong, J. Zhao, Energy-stable Runge-Kutta schemes for gradient flow models using the energy quadratization approach, Appl. Math. Lett., 94 (2019), 224–231. https://doi.org/10.1016/j.aml.2019.02.002 doi: 10.1016/j.aml.2019.02.002 |
[18] | J. Zhao, A revisit of the energy quadratization method with a relaxation technique, Appl. Math. Lett., 120 (2021), 107331. https://doi.org/10.1016/j.aml.2021.107331 doi: 10.1016/j.aml.2021.107331 |
[19] | M. Jiang, Z. Zhang, J. Zhao, Improving the accuracy and consistency of the scalar auxiliary variable (SAV) method with relaxation, J. Comput. Phys., 456 (2022), 110954. https://doi.org/10.1016/j.jcp.2022.110954 doi: 10.1016/j.jcp.2022.110954 |
[20] | Y. Chen, Y. Gong, Q. Hong, C. Wang, A novel class of energy-preserving Runge-Kutta methods for the Korteweg-de Vries equation, preprint, arXiv: 2108.12097v2. |
[21] | B. K. Tapley, Numerical integration of ODEs while preserving all polynomial first integrals, preprint, arXiv: 2108.06548. |
[22] | Y. Gong, J. Zhao, Q. Wang, Arbitrarily high-order unconditionally energy stable schemes for thermodynamically consistent gradient flow models, SIAM J. Sci. Comput., 42 (2020), B135–B156. https://doi.org/10.1137/18M1213579 doi: 10.1137/18M1213579 |
[23] | Y. Gong, J. Zhao, Q. Wang, Arbitrarily high-order unconditionally energy stable SAV schemes for gradient flow models, Comput. Phys. Commun., 249 (2020), 107033. https://doi.org/10.1016/j.cpc.2019.107033 doi: 10.1016/j.cpc.2019.107033 |
[24] | Y. Gong, J. Zhao, Q. Wang, Arbitrarily high-order linear energy stable schemes for gradient flow models, J. Comput. Phys., 419 (2020), 109610. https://doi.org/10.1016/j.jcp.2020.109610 doi: 10.1016/j.jcp.2020.109610 |
[25] | L. Chen, J. Zhao, X. Yang, Regularized linear schemes for the molecular beam epitaxy model with slope selection, Appl. Numer. Math., 128 (2018), 138–156. https://doi.org/10.1016/j.apnum.2018.02.004 doi: 10.1016/j.apnum.2018.02.004 |
[26] | E. Hairer, C. Lubich, G. Wanner, Geometric numerical integration: structure-preserving algorithms for ordinary differential equations, Springer Sci. & Bus. Media, 2006. |