Browsing by Author "Khan, Muhammad Fawad"
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Item A hybrid metaheuristic based on neurocomputing for analysis of unipolar electrohydrodynamic pump flow(2021-11) Khan, Muhammad Fawad; Sulaiman, Muhammad; Tavera Romero, Carlos Andrés; Alkhathlan, AliA unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es ), the electrical Reynolds number (ReE ), and electrical slip number (Esl ), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.Item Falkner–skan flow with stream-wise pressure gradient and transfer of mass over a dynamic wall(2021-11) Khan, Muhammad Fawad; Sulaiman, Muhammad; Tavera Romero, Carlos Andrés; Alkhathlan, AliIn this work, an important model in fluid dynamics is analyzed by a new hybrid neurocom-puting 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.