A design of neuro-computational approach for double‐diffusive natural convection nanofluid flow

Shoaib, Mohammed; Tabassum, Rafia; Nisar, Kottakkaran Sooppy; Raja, Muhammad Asif Zahoor; Fatima, Nahid; Al-Harbi, Nuha; Abdel‐Aty, Abdel‐Haleem

doi:10.1016/j.heliyon.2023.e14303

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…The purpose of this study was to present numerical solutions for the nanofluid model of heat/mass transfer using the deep neural network (DNN) along with the procedure of Bayesian regularization scheme (BRS), DNN-BRS, as described in [37][38][39][40][41]. The DNN process was delivered by taking eight and ten neurons into the first and second hidden layers as well as the log-sigmoid function.…”

Section: Neural Networkmentioning

confidence: 99%

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

2023

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This paper explores the numerical optimization of heat and mass transfer in the buoyancy-driven Al2O3-water nanofluid flow containing electrified Al2O3-nanoparticles adjacent to a symmetrically-vertical plane wall. The proposed model becomes a set of nonlinear problems through similarity transformations. The nonlinear problem is solved using the bvp4c method. The results of the proposed model concerning heat and mass transfer with nanoparticle electrification and buoyancy parameters are depicted in the Figures and Tables. It was revealed that the electrification of nanoparticles enhances the heat and mass transfer capabilities of the Al2O3 water nanoliquid. As a result, the electrification of nanoparticles could be an important mechanism to improve the transmission of heat and mass in the flow of Al2O3-water nanofluids. Furthermore, the numerical solutions of the nanofluid model of heat/mass transfer using the deep neural network (DNN) along with the procedure of Bayesian regularization scheme (BRS), DNN-BRS, was carried out. The DNN process is provided by taking eight and ten neurons in the first and second hidden layers along with the log-sigmoid function.

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Section: Neural Networkmentioning

confidence: 99%

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

2023

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“…In the manufacturing industry, solar thermal collectors are recognized as a highly straightforward and environmentally friendly way to convert UV irradiation to heat energy 2,3 . In such collectors, various fluids such as glycol and water are used to improvise their performance and power 4,5 . Recently, numerically and experimentally, several researchers have researched the use of NFs in many kinds of solar collectors 6,7 .…”

Section: Introductionmentioning

confidence: 99%

“…2,3 In such collectors, various fluids such as glycol and water are used to improvise their performance and power. 4,5 Recently, numerically and experimentally, several researchers have researched the use of NFs in many kinds of solar collectors. 6,7 Many researchers and engineers have recently shown a strong interest in the subject of two-dimensional axially symmetric fluid motion inside a quasi-inverted cone.…”

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confidence: 99%

A computational convection analysis of SiO₂/water and MoS₂‐SiO₂/water based fluidic system in inverted cone

Hussain

Ahmad

Raja

et al. 2023

Engineering Reports

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A complete shape factor investigation of water‐based mixture type hybrid nanofluid in a permeable boundary with the impact of magnetic field, thick dissemination, and warm radiation is presented in this article. A computational convection analysis of an inverted semi vertical cone with a porous surface in the form of /water nanofluid and /water hybrid nanofluid transport is developed. The system of differential equations is presented and solved numerically by the Lobatto IIIA method. The temperature distributions and fluid velocity are studied along with the coefficient of skin friction and the Nusselt number, taking into account the form of distinct nano‐particles. The flow problem's results are approximated by using several embedding variables. Tables and graphs are constructed for a variety of scenarios including maximum residual error, mesh points, and Nusselt numbers. We conclude that boundary film thickness reduces and the fluid flow is resisted by magnetic field presence. Fluid flow slows down as increases, and this reduction is more evident in nanofluids than in hybrid nanofluids. With an increment in , velocity drops. A detailed analysis of the proposed ordinary differential equations, boundary conditions, and numerical data of skin friction is given both in tabular and graphical forms. Additionally, it is observed that the fluid flow slows down more for the hybrid nanofluid than for the /water nanofluid. Moreover, it is clear that the temperature increase for the /water nanofluid is substantially greater. The authors deduce that the existence of a magnetic field resists fluid flow for hybrid nanofluid forms and decreases the thickness of the viscous boundary layer.

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“…Soft computing methods are important in a variety of applications [27][28][29]. Few research paradigms related to this neuro computing technique are given as cross nanofluid system [30], Darcy-Forchheimer flow of nanofluidic model [31], Carreau-nanoliquid model [32], Lane-Emden system [33,34], double diffusion convection floe models [35,36], pine wilt disease model [37], and COVID-19 models [38][39][40]. These computing paradigms are the motivational factor for researchers to exploit a reliable and accurate framework using the artificial intelligence infrastructure to solve the NRS-CNFS based on parametric investigations to observe the numerous physical…”

Section: Introductionmentioning

confidence: 99%

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

Shoaib,

Nisar,

Raja

et al. 2023

Z Angew Math Mech

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In the present article, the intelligence based numerical investigation by Levenberg–Marquardt algorithm with Back‐propagated Artificial Neural Networks (LMA–BANN) is exploited to analyze the nonlinear radiative stagnation point flow of cross nanofluid system (NRS‐CNFS) past a stretching surface. The designed NRS‐CNFS, initially represented by system of partial differential equations (PDEs), is converted into system of non‐linear ODEs through the applicability of mathematical conversion analysis. The desired reference solution in the form of a dataset for LMA–BANN is achieved from the Adam method for NRS‐CNFS's different scenarios by varying the magnetic parameter (Ha), Prandlt number (Pr), thermal radiation parameter(R), Eckert number (Ec), thermophoresis diffusion coefficients (Nt), and non‐dimensional activation energy (E). The obtained results are construed for the NRS‐CNFS through the performances of the testing, validation, and training. In addition, the comparison is provided using the LMA ‐BANN analysis, which is validated in terms of regression, fitness estimations, and histograms along with MSE performances. Hence, the article provides an impeccable innovative approach to problem‐solving by implementing a soft computation paradigm. It promotes the use of an effective and dependable alternative framework that is based on soft computing environments and emphasizes descriptive analysis to successfully handle the problems brought on by varied physical features.

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A design of neuro-computational approach for double‐diffusive natural convection nanofluid flow

Cited by 9 publications

References 49 publications

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

A computational convection analysis of SiO₂/water and MoS₂‐SiO₂/water based fluidic system in inverted cone

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

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A design of neuro-computational approach for double‐diffusive natural convection nanofluid flow

Cited by 9 publications

References 49 publications

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

Heat and Mass Transfer Gravity Driven Fluid Flow over a Symmetrically-Vertical Plane through Neural Networks

A computational convection analysis of SiO2/water and MoS2‐SiO2/water based fluidic system in inverted cone

A stagnation point flow of cross nanofluid flow: Levenberg Marquardt backpropagation computational approach

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A computational convection analysis of SiO₂/water and MoS₂‐SiO₂/water based fluidic system in inverted cone