Approximate analytic solutions for a two-dimensional mathematicalmodel of a packed-bed electrode using the Adomian decomposition method

Qin, Xiao-Ying; Sun, Yanping

doi:10.1016/j.amc.2009.05.012

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Using (19), (20), (23), (39), (40), and (42), we obtain 0 = 0.3 , 0 = −0.21952465, and 0 = −0.6 . The choice of polynomial degree in (17) and 18is important for solving the model.…”

Section: Computation Using Matlabmentioning

confidence: 99%

“…Adomian decomposition method (ADM), developed by Adomian [12], has been widely applied to solve various types of equations involving algebraic, differential, partial differential, integral, and integro-differential operations [12][13][14][15][16][17][18][19][20][21][22][23]. ADM is an efficient method for solving PDEs and systems thereof with various types of boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Analytic Solutions for the Two-Phase Stefan Problem Using the Adomian Decomposition Method

Qin

Yue-xing

Yin

2014

Journal of Applied Mathematics

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An Adomian decomposition method (ADM) is applied to solve a two-phase Stefan problem that describes the pure metal solidification process. In contrast to traditional analytical methods, ADM avoids complex mathematical derivations and does not require coordinate transformation for elimination of the unknown moving boundary. Based on polynomial approximations for some known and unknown boundary functions, approximate analytic solutions for the model with undetermined coefficients are obtained using ADM. Substitution of these expressions into other equations and boundary conditions of the model generates some function identities with the undetermined coefficients. By determining these coefficients, approximate analytic solutions for the model are obtained. A concrete example of the solution shows that this method can easily be implemented in MATLAB and has a fast convergence rate. This is an efficient method for finding approximate analytic solutions for the Stefan and the inverse Stefan problems.

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“…Using (19), (20), (23), (39), (40), and (42), we obtain 0 = 0.3 , 0 = −0.21952465, and 0 = −0.6 . The choice of polynomial degree in (17) and 18is important for solving the model.…”

Section: Computation Using Matlabmentioning

confidence: 99%