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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2020

01.04.2019 | Original Article

Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model

verfasst von: Muhammad Asif Zahoor Raja, Ammara Mehmood, Adeel Ahmad Khan, Aneela Zameer

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics.
Vorheriger Artikel Improved word-level handwritten Indic script identification by integrating small convolutional neural networks
Nächster Artikel Extended hesitant fuzzy linguistic term set with fuzzy confidence for solving group decision-making problems

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Metadaten
Titel
Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model
verfasst von
Muhammad Asif Zahoor Raja
Ammara Mehmood
Adeel Ahmad Khan
Aneela Zameer
Publikationsdatum
01.04.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04157-1

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