Hybridized Projected Differential Transform Method For collisional-breakage equation

TL;DR

This paper introduces a hybrid method combining Elzaki transform and projected differential transform to solve the nonlinear collisional breakage equation, improving accuracy and convergence without discretization.

Contribution

It presents a novel hybrid analytical technique for solving nonlinear collisional breakage equations, enhancing solution accuracy and convergence over existing methods.

Findings

Accurate solutions for number density functions and moments.

Effective for both linear and nonlinear equations.

Maintains high precision over long time periods.

Abstract

The non-linear collision induced fragmentation plays a crucial role in modeling several engineering and physical problems. In contrast to linear breakage, it has not been thoroughly investigated in the existing literature. This study introduces an innovative method that leverages the Elzaki integral transform as a preparatory step to enhance the accuracy and convergence of domain decomposition, used alongside the projected differential transform method to obtain closed-form or series approximations of solutions for the collisional breakage equation (CBE). A significant advantages of this technique is its capability to directly address both linear and nonlinear differential equations without the need for discretization or linearization. The mathematical framework is reinforced by a thorough convergence analysis, applying fixed point theory within an adequately defined Banach space. Additionally, error estimates for the approximated solutions are derived, offering more profound insights into the accuracy and dependability of the proposed method. The validity of this approach is demonstrated by comparing the obtained results with exact or finite volume approximated solutions considering several physical examples. Interestingly, the proposed algorithm yields accurate approximations for the number density functions as well as moments with fewer terms and maintains higher precision over extended time periods.