Neural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activation
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy's model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide (MoS2) and copper (Cu)-silicon dioxide (SiO2), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter R-d, both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number Br leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, surface injection parameter (s<0), Eckert number Ec and radiation parameter R-d and with a decrement in suction parameter (s>0) and Prandtl number Pr, there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction phi(2), magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and a reduction in thermal buoyancy parameter lambda, strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter M, inverse permeability parameter epsilon, suction parameter s and higher values of thermal buoyancy parameter lambda. There is a significant decrement also computed in Nusselt number with a greater radiation parameter R-d. The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.
Açıklama
Anahtar Kelimeler
Bayesian Regularization, Bejan Number, Boundary Layers, Brinkman Number, Cu-SiO2/engine Oilhybrid Nanofluid, Coating, Entropy Generation, Hybrid Magnetic Nanofluids, Neural Network, Thermodynamic Optimization, Wall Mass Flux, MATLAB BVP4C, Radiative Heat Flux
Kaynak
Numerical Heat Transfer Part A-Applications
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
Sayı
Künye
Ferdows, M., Ahmed, M., Bhuiyan, M. A., Bég, O. A., Çolak, A. B., & Leonard, H. J. (2024). Neural Network And Thermodynamic Optimization Of Magnetized Hybrid Nanofluid Dissipative Radiative Convective Flow With Energy Activation. Numerical Heat Transfer, Part A: Applications, 1-35.
