Effect of non-uniform heat rise/fall and porosity on MHD Williamson hybrid nanofluid flow over incessantly moving thin needle.

In this work, a novel enhanced model of the thermophysical characteristics of hybrid nanofluid is introduced. An innovative kind of fluid called hybrid nanofluid has been engineered to increase the heat transfer rate of heat and performance of thermal system. A growing trend in scientific and industrial applications pushed researchers to establish mathematical models for non-Newtonian fluids. A parametric study on theheat transfer and fluid flow of a Williamson hybrid nanofluid based on AA7075-AA7072/Methanol overincessantly moving thin needle under the porosity, Lorentz force, and non-uniform heat rise/fallis performed. Due to similarity variables, the partial differential equations governing the studied configuration undergo appropriate transformation to be converted into ordinary differential equations. The rigorous built-in numerical solver in bvp4c MATLAB has been employed to determine the numerical solutions of the established non-linear ordinary differential equations. It is worthy to note that velocity declines for both AA7075/Methanol nanofluid and AA7075- AA7072/Methanol hybrid nanofluid, but highervelocitymagnitudes occur for theAA7075/Methanol whilethe Williamson fluid parameters increased. It is alsoconcluded that as the porosity parameter isincreased, the flow intensity decreases gradually. It is worthy to note that for both non-uniform heat-rise and fall parameters, the temperature of the fluid gets stronger. Mounting valuesof needle thickness parameter leads to reduction in fluid speed and temperature. It is noticedthat as volume fractions of both types of nanoparticles are augmented then fluidvelocity and temperature amplify rapidly. A Comparison of current and published results is performed to ensure the validity of the established numerical model.

An innovative inertial extra-proximal gradient algorithm for solving convex optimization problems with application to image and signal processing.

This study introduces an innovative approach to address convex optimization problems, with a specific focus on applications in image and signal processing. The research aims to develop a self-adaptive extra proximal algorithm that incorporates an inertial term to effectively tackle challenges in convex optimization. The study's significance lies in its contribution to advancing optimization techniques in the realm of image deblurring and signal reconstruction. The proposed methodology involves creating a novel self-adaptive extra proximal algorithm, analyzing its convergence rigorously to ensure reliability and effectiveness. Numerical examples, including image deblurring and signal reconstruction tasks using only 10% of the original signal, illustrate the practical applicability and advantages of the algorithm. By introducing an inertial term within the extra proximal framework, the algorithm demonstrates potential for faster convergence and improved optimization outcomes, addressing real-world challenges of image enhancement and signal reconstruction. The algorithm's incorporation of an inertial term showcases its potential for faster convergence and improved optimization outcomes. This research significantly contributes to the field of optimization techniques, particularly in the context of image and signal processing applications.