A fast method based on variable time steps for 2D nonlinear time-fractional generalized Benjamin–Bona–Mahony–Burgers equation: Error and stability analysis
IF 3.1 3区 计算机科学Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0
Abstract
This paper presents the stability and convergence analysis of a hybrid scheme that combines the Galerkin-spectral method with a nonuniform time-stepping temporal discretization. The scheme is used to solve the two-dimensional nonlinear time-fractional generalized Benjamin–Bona–Mahony–Burgers equation with admissible regularities. It is widely accepted that fractional differential equations often exhibit singularity near the initial time, which makes uniform time-stepping methods unsuitable to approximate their solution. We convert the problem to an equivalent integral form and then solve it using a reliable time-stepping method with nonuniform time steps. We have developed an unconditionally stable discretization technique that approximates Riemann–Liouville fractional integrals with third-order convergence. Furthermore, we have used the spectral Galerkin method based on the Legendre polynomials for spatial discretization of the mentioned nonlinear problem in two dimensions. Also, we have proved the unconditional stability and convergence of the full-discretization scheme. Using the proposed method, we have conducted several numerical simulations which fully confirmed the theoretical results.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
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