Thermal Analysis of Conductive-Convective-Radiative Heat Exchangers With Temperature Dependent Thermal Conductivity

Khan, Naveed Ahmad; Sulaiman, Muhammad; Bakar, Maharani A.

doi:10.1109/access.2021.3117839

Cited by 18 publications

(11 citation statements)

References 76 publications

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“…However, stochastic metaheuristic techniques developed through artificial intelligence algorithms have not been explored and exploited for solving nonlinear models of nanofluids. Recently, the strength of stochastic techniques based on artificial neural networks (ANNs) using bio-and nature-inspired computing paradigms has been extensively applied to study the approximate solutions of stiff nonlinear problems, such as the saturation of oil and water during the secondary oil recovery process [24], the bath of a wire during coating with Oldroyd 8-constant fluid [25], the rolling motion of ships in random beam seas [26], the study of 3-D Prandtl nanofluid flow over a convectively heated sheet [27], nonlinear problems arising in heat transfer [28,29], thermal radiation and Hall effects on the boundary layer flow of a nanofluid [30] and the Lorenz chaotic attractor (LCA) and double-scroll attractor (DSA) in secure communication systems [31]. Some salient features of the designed schemes are as follows:…”

Section: Introductionmentioning

confidence: 99%

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

et al. 2022

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This study investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces. The basic continuity and momentum equations were considered along with the Buongiorno model to formulate the governing mathematical model of the problem. Furthermore, the intelligent computational strength of artificial neural networks (ANNs) was utilized to construct the approximate solution for the problem. The unsupervised objective functions of the governing equations in terms of mean square error were optimized by hybridizing the global search ability of an arithmetic optimization algorithm (AOA) with the local search capability of an interior point algorithm (IPA). The proposed ANN-AOA-IPA technique was implemented to study the effect of variations in the thermophoretic parameter (Nt), Hartmann number (Ha), Brownian (Nb) and radiation (Rd) motion parameters, Eckert number (Ec), Reynolds number (Re) and Schmidt number (Sc) on the velocity profile, thermal profile, Nusselt number and skin friction coefficient of the nanofluid. The results obtained by the designed metaheuristic algorithm were compared with the numerical solutions obtained by the Runge–Kutta method of order 4 (RK-4) and machine learning algorithms based on a nonlinear autoregressive network with exogenous inputs (NARX) and backpropagated Levenberg–Marquardt algorithm. The mean percentage errors in approximate solutions obtained by ANN-AOA-IPA are around 10−6 to 10−7. The graphical analysis illustrates that the velocity, temperature, and concentration profiles of the nanofluid increase with an increase in the suction parameter, Eckert number and Schmidt number, respectively. Solutions and the results of performance indicators such as mean absolute deviation, Theil’s inequality coefficient and error in Nash–Sutcliffe efficiency further validate the proposed algorithm’s utility and efficiency.

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Section: Introductionmentioning

confidence: 99%

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

et al. 2022

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“…N.A Khan [28] used Legendre neural networks (LeNN) and optimized the model of countercurrent imbibition phenomena during the secondary oil recovery process by using the nature-inspired whale optimization algorithm and the Nelder-Mead algorithm. Some recent applications of stochastic computational techniques or learning algorithms based on artificial neural networks (ANN's) with novel metaheuristic and heuristic techniques includes the solution of wire coating dynamics with Oldroyd 8-constant fluid [29,30], nonlinear problems arising in heat transfer [31,32], absorption of CO2 into solution of phenyl glycidyl ether (PGE) [33], mathematical models of CBSC over wireless channels [34] and electrohydrodynamic (EHD) flow in a circular cylindrical conduit [35]. Recent developments in stochastic algorithms for such problems motivated authors to explore and exploit machine learning algorithms and use Legendre neural networks to develop an alternative, accurate, and reliable framework to solve nonlinear multi-singular initial or boundary value problems representing drainage problems.…”

Section: Introductionmentioning

confidence: 99%

An Optimistic Solver for the Mathematical Model of the Flow of Johnson Segalman Fluid on the Surface of an Infinitely Long Vertical Cylinder

et al. 2021

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In this paper, a novel soft computing technique is designed to analyze the mathematical model of the steady thin film flow of Johnson–Segalman fluid on the surface of an infinitely long vertical cylinder used in the drainage system by using artificial neural networks (ANNs). The approximate series solutions are constructed by Legendre polynomials and a Legendre polynomial-based artificial neural networks architecture (LNN) to approximate solutions for drainage problems. The training of designed neurons in an LNN structure is carried out by a hybridizing generalized normal distribution optimization (GNDO) algorithm and sequential quadratic programming (SQP). To investigate the capabilities of the proposed LNN-GNDO-SQP algorithm, the effect of variations in various non-Newtonian parameters like Stokes number (St), Weissenberg number (We), slip parameters (a), and the ratio of viscosities (ϕ) on velocity profiles of the of steady thin film flow of non-Newtonian Johnson–Segalman fluid are investigated. The results establish that the velocity profile is directly affected by increasing Stokes and Weissenberg numbers while the ratio of viscosities and slip parameter inversely affects the fluid’s velocity profile. To validate the proposed technique’s efficiency, solutions and absolute errors are compared with reference solutions calculated by RK-4 (ode45) and the Genetic algorithm-Active set algorithm (GA-ASA). To study the stability, efficiency and accuracy of the LNN-GNDO-SQP algorithm, extensive graphical and statistical analyses are conducted based on absolute errors, mean, median, standard deviation, mean absolute deviation, Theil’s inequality coefficient (TIC), and error in Nash Sutcliffe efficiency (ENSE). Statistics of the performance indicators are approaching zero, which dictates the proposed algorithm’s worth and reliability.

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“…Such stochastic computing techniques use artificial neural networks to model approximate solutions. These numerical solvers have wide applications in various fields including petroleum engineering [ 23 ], wireless communication [ 24 ], heat transfer [ 25 , 26 , 27 ], fuzzy systems [ 28 ], plasma system [ 29 ], civil engineering [ 30 , 31 ], wire coating dynamics [ 32 ] and Diabetic retinopathy classification [ 33 ]. The techniques mentioned earlier inspire the authors to explore and incorporate the soft computing architectures as an alternative, precise and feasible way for solving the mathematical model of micro-disk biosensors.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Analysis of Reaction–Diffusion Equations Modeling the Michaelis–Menten Kinetics in a Micro-Disk Biosensor

et al. 2021

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In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis–Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction–diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg–Marquardt training (LMT) algorithm. NNs–LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as “pdex4”. Furthermore, the data set is validated by the processing of the NNs–LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.

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Thermal Analysis of Conductive-Convective-Radiative Heat Exchangers With Temperature Dependent Thermal Conductivity

Abstract: The authors acknowledge the financial support provided by the Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT.

Cited by 18 publications

References 76 publications

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

An Optimistic Solver for the Mathematical Model of the Flow of Johnson Segalman Fluid on the Surface of an Infinitely Long Vertical Cylinder

Mathematical Analysis of Reaction–Diffusion Equations Modeling the Michaelis–Menten Kinetics in a Micro-Disk Biosensor

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