Falkner–Skan Flow with Stream-Wise Pressure Gradient and Transfer of Mass over a Dynamic Wall

Abstract

In this work, an important model in fluid dynamics is analyzed by a new hybrid neurocomputing algorithm. We have considered the Falkner–Skan (FS) with the stream-wise pressure gradient transfer of mass over a dynamic wall. To analyze the boundary flow of the FS model, we have utilized the global search characteristic of a recently developed heuristic, the Sine Cosine Algorithm (SCA), and the local search characteristic of Sequential Quadratic Programming (SQP). Artificial neural network (ANN) architecture is utilized to construct a series solution of the mathematical model. We have called our technique the ANN-SCA-SQP algorithm. The dynamic of the FS system is observed by varying stream-wise pressure gradient mass transfer and dynamic wall. To validate the effectiveness of ANN-SCA-SQP algorithm, our solutions are compared with state-of-the-art reference solutions. We have repeated a hundred experiments to establish the robustness of our approach. Our experimental outcome validates the superiority of the ANN-SCA-SQP algorithm.

Numerical analysis of gyrotactic microorganisms in MHD radiative Eyring–Powell nanofluid across a static/moving wedge with Soret and Dufour effects

“…Recently, Atif et al [30] analyzed the time-dependent flow of heat and mass transport on tangent hyperbolic fluid over a wedge surface. Khan et al [31] examined the flow of Falkner-Skan with stream-wise pressure gradient due to dynamic wall.…”

“…Khan et al. [31] examined the flow of Falkner–Skan with stream‐wise pressure gradient due to dynamic wall.…”

Numerical Computation for Gyrotactic Microorganisms in MHD Radiative Eyring–Powell Nanomaterial Flow by a Static/Moving Wedge with Darcy–Forchheimer Relation

“…Tangent hyperbolic nanofluid with time dependent flow past a wedge plane was recently evaluated by Atif et al [ 30 ]. Khan et al [ 31 ] evaluated a dynamic wall-induced pressure gradient in the Falkner–Skan flow.…”