Inquiry of MHD bioconvective non-newtonian nanofluid flow over a moving wedge using HPM

Krishna, S. Gopi; Shanmugapriya, M.

doi:10.1016/j.matpr.2020.10.028

Cited by 11 publications

(1 citation statement)

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“…S. Gupta [30] investigated the three-dimensional flow of an Oldroyd-B nanofluid over a bidirectional stretching sheet using hybridization of DTM and the Padé approximation. Hatami [31,32] studied nanofluid flow and heat transfer when subjected to a magnetic field and flowing between parallel plates. An MHD nanofluid squeezing flow analysis under the influence of slip boundary conditions was investigated by Sobamowo et al [33] using a variation of parameter method (VPM).…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer Analysis of Nanofluid Flow in a Rotating System with Magnetic Field Using an Intelligent Strength Stochastic-Driven Approach

et al. 2022

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This paper investigates the heat transfer of two-phase nanofluid flow between horizontal plates in a rotating system with a magnetic field and external forces. The basic continuity and momentum equations are considered to formulate the governing mathematical model of the problem. Furthermore, certain similarity transformations are used to reduce a governing system of non-linear partial differential equations (PDEs) into a non-linear system of ordinary differential equations. Moreover, an efficient stochastic technique based on feed-forward neural networks (FFNNs) with a back-propagated Levenberg–Marquardt (BLM) algorithm is developed to examine the effect of variations in various parameters on velocity, gravitational acceleration, temperature, and concentration profiles of the nanofluid. To validate the accuracy, efficiency, and computational complexity of the FFNN–BLM algorithm, different performance functions are defined based on mean absolute deviations (MAD), error in Nash–Sutcliffe efficiency (ENSE), and Theil’s inequality coefficient (TIC). The approximate solutions achieved by the proposed technique are validated by comparing with the least square method (LSM), machine learning algorithms such as NARX-LM, and numerical solutions by the Runge–Kutta–Fehlberg method (RKFM). The results demonstrate that the mean percentage error in our solutions and values of ENSE, TIC, and MAD is almost zero, showing the design algorithm’s robustness and correctness.

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