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.
Highlights
Fluid dynamics applies to a large number of fields such as traffic engineering, weather prediction, aerospace, and crowed dynamics [1,2,3,4]
Problem 1: Dynamics of Falkner–Skan boundary layer system (FSS) Based on the Variation of Stream-Wise Pressure Gradient α
We considered the celebrated nonlinear dynamic differential equation, known as the Falkner–Skan system, that arises in fluid dynamics for boundary-layer flow with the streamwise pressure gradient transfer of mass over a dynamic wall
Summary
Fluid dynamics applies to a large number of fields such as traffic engineering, weather prediction, aerospace, and crowed dynamics [1,2,3,4]. Fluid dynamics can apply to more complex scenarios, such as in astrophysical problems, including plasma and solar physics. J. González-Avilés et al present a study about ideal MHD code to study the solar atmosphere and Jet formation in solar atmosphere due to magnetic reconnection [6]. The fluid dynamic behavior depends on the information of velocity, density, temperature, and pressure in terms of space and time. The role of a mathematician is vital to clear the blurred image of fluid dynamics by describing the application of science-based fluid dynamics through mathematical modeling.
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