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Allah Bakhsh Yazdani Cherati

Allah Bakhsh Yazdani Cherati

Academic rank: Associate Professor
ORCID: 0000-0002-3352-5829
Education: PhD.
ScopusId: 57189309801
HIndex:
Faculty: Faculty of Mathematical Sciences
Address:
Phone: 01135302471

Research

Title
Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
Type
JournalPaper
Keywords
RK, RKM, GRKM, sixth-order, ordinary, partial, DEs, ODEs, PDEs, Taylor-series
Year
2024
Journal Open Engineering
DOI
Researchers Mohammed Sahib Mechee ، Murtadha Abduljawad Kadhim ، Allah Bakhsh Yazdani Cherati

Abstract

A general quasilinear sixth-order ordinary differential equation (ODE) is an important class of ODEs. The primary objective of this study is to establish a numerical method for solving a general class of quasilinear sixthorder partial differential equations (PDEs) and ODEs. However, the Runge–Kutta method (RKM) approach for solving special classes of ODEs has been generalized as an effort to solve the general class of ODEs. Nonlinear algebraic order condition (OCs) equations have been obtained up to the tenth order using the Taylor-series expansion methodology which is used to derive the novel generalized Runge–Kutta method (GRKM). In this study, a GRKM integrator has been derived for solving a general class of quasilinear sixth-order ODEs and then this method is modified subsequently to solve a class of PDEs. Accordingly, the proposed GRKM is modified to solve a quasilinear sixthorder PDE by converting it to a system of sixth-ODEs using the method of lines. Nine problems have been implemented to prove the efficiency and accuracy of the proposed method. Simulation results of these problems showed that the proposed numerical GRKM is an accurate and efficient method. In contrast, by comparing the proposed GRKM numerical approach with the classical RK method, the numerical results demonstrate that the direct integrator outperforms the indirect classical RK method in terms of algorithm complexity and function evaluations, proving that the numerical GRKM is efficient