A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A 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...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Muhammad Fawad Khan, Muhammad Sulaiman, Carlos Andrés Tavera Romero, Ali Alkhathlan
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
Q
Accès en ligne:https://doaj.org/article/861c1753ae7c44ec88da40be5baef66a
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Description
Résumé:A 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msub><mi>E</mi><mi>s</mi></msub><mo>)</mo></mrow></semantics></math></inline-formula>, the electrical Reynolds number <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msub><mi>R</mi><msub><mi>e</mi><mi>E</mi></msub></msub><mo>)</mo></mrow></semantics></math></inline-formula>, and electrical slip number <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msub><mi>E</mi><mrow><mi>s</mi><mi>l</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula>, 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.