Designing a solid–fluid interface layer and artificial neural network in a nanofluid flow due to rotating rough and porous disk

Journal of Thermal Analysis and Calorimetry, Journal Year: 2023, Volume and Issue: 149(2), P. 867 - 878

Published: Nov. 21, 2023

Language: Английский

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Impacts of thermophoretic deposition and thermal radiation on heat and mass transfer analysis of ternary nanofluid flow across a wedge

International Journal of Modelling and Simulation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: Jan. 2, 2024

Ternary nanofluids have attracted significant attention due to their improved thermal characteristics and adaptability for many applications. Nanoparticles interact with one another in complex ways a noticeable effect on heat transmission. Due this, promise several fields. Notably, investigating the behavior of ternary via wedge enables optimization transfer processes exchangers, cooling mechanisms, control systems. The current study examines effects radiation thermophoretic particle deposition hybrid nanofluid flow across wedge. governing equations are precisely modeled solved numerically using Runge Kutta Fehlberg's fourth-fifth order (RKF-45) approach shooting procedure. reduced ordinary differential (ODEs) similarity transformations. results efficiently shown visual depictions, facilitating precise understanding outcomes. outcomes show that increased parameter solid volume fraction decreases mass rate. Researchers may incorporate this comprehension effectively utilize enhance practical situations, as well development technology energy management.

Language: Английский

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Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 25, 2024

Abstract Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, lightweight, and quite miniature. Thus, this study investigates the influence of wavy fin structure subjected to convective effects with internal generation. The thermal distribution, considered steady condition one dimension, is described by unique implementation physics-informed neural network (PINN) as part machine-learning intelligent strategies for analyzing transfer fin. This novel research explores use PINNs examine effect nonlinearity temperature equation boundary conditions altering hyperparameters architecture. non-linear ordinary differential (ODE) involved reduced into dimensionless form utilizing non-dimensional variables simplify problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach implemented evaluate simplified equations numerically. To predict fin's properties, an advanced model created without using traditional data-driven approach, ability solve ODEs explicitly incorporating mean squared error-based loss function. obtained results divulge that increase conductivity variable upsurges distribution. In contrast, decrease profile caused due augmentation convective-conductive values.

Language: Английский

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Computational examination of heat and mass transfer of nanofluid flow across an inclined cylinder with endothermic/exothermic chemical reaction

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 57, P. 104336 - 104336

Published: April 1, 2024

The consequence of thermal performance and mass distribution endothermic/exothermic chemical reaction pollutant concentration on the nanoliquid stream via cylinder/plate in presence permeable media is explored present study. advancement efficient waste disposal pollution prevention techniques might result from studies fluid flow dispersion contaminants cylinders plates. Further, TiO2 nanoparticle offers improved conductivity, helps wide range technological industrial applications. governing partial differential equations (PDEs) problem are modelled converted to ordinary (ODEs) utilizing similarity variables. resultant ODEs numerically solved using Runge Kutta Fehlberg's fourth-fifth order (RKF-45) scheme. influence various non-dimensional parameters velocity, thermal, profiles illustrated with a graphical representation. comparison between cylinder plate geometry also displayed graphs. novel outcomes show that augmentation parameter reduces profile endothermic case elevates exothermic case. Elevating local external source leads rise profile. It around 10%–12% Cf, 4%–6% Nu 8%–12% Sh observed than plate. transfer rate for values solid fraction activation energy. In all modes, performs better geometry.

Language: Английский

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Implication of radiation on the thermal behavior of a partially wetted dovetail fin using an artificial neural network

Case Studies in Thermal Engineering, Journal Year: 2023, Volume and Issue: 51, P. 103552 - 103552

Published: Oct. 4, 2023

The simultaneous convection-radiation heat transfer of a partially wetted dovetail extended surface is investigated in this study. Also, the temperature variance behavior (DES) estimated through thermal models for wet and dry conditions using neural network with Levenberg-Marquardt scheme (NNLMS). corresponding governing energy equations fin are presented as set ordinary differential (ODE), which reduced to non-dimensional form dimensionless terms. Further, resulting coupled conductive, convective, radiative ODEs numerically solved utilizing Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) scheme. Using graphical illustrations, resultant solutions physically determined by considering effects various nondimensional variables on behavior. From outcomes, it established that conductivity parameter enhances distribution fin, an upsurge convection-conduction variable, ratio parameter, radiation–conduction, diminishes profile considered surface. modelled problem's NNLMS efficacy demonstrated achieving best convergence unique assessed quantified results. outcomes indicate strategy successfully resolves problem.

Language: Английский

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Evolutionary Computing for the Radiative–Convective Heat Transfer of a Wetted Wavy Fin Using a Genetic Algorithm-Based Neural Network

Biomimetics, Journal Year: 2023, Volume and Issue: 8(8), P. 574 - 574

Published: Dec. 1, 2023

Evolutionary algorithms are a large class of optimization techniques inspired by the ideas natural selection, and can be employed to address challenging problems. These iteratively evolve populations using crossover, which combines genetic information from two parent solutions, mutation, adds random changes. This iterative process tends produce effective solutions. Inspired this, current study presents results thermal variation on surface wetted wavy fin algorithm in context parameter estimation for artificial neural network models. The physical features convective radiative heat transfer during wet conditions also considered develop model. highly nonlinear governing ordinary differential equation proposed problem is transmuted into dimensionless equation. graphical outcomes aspects profile demonstrated specific non-dimensional variables. primary observation decrease temperature with rise parameters convective-conductive parameters. implemented offers powerful technique that effectively tune network, leading an enhanced predictive accuracy convergence numerically obtained solution.

Language: Английский

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Thermal radiation and soret/dufour effects on amplitude and oscillating frequency of darcian mixed convective heat and mass rate of nanofluid along porous plate

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 59, P. 104562 - 104562

Published: May 16, 2024

The influence of thermal radiations and Soret/Dufour significantly increases the mass heat transport in material science, geothermal process, chemical rectors exchangers due to temperature concentration variation nanoparticles. Main objective is enhance amplitude, oscillation frequency transfer using Soret Dufour characteristics Darcian radiating flow thermophoretic nanoparticles along vertical porous surface. dimensionless analysis performed develop convenient form governing thermodynamic nanofluid formulation. To programming algorithm FORTRAN language, primitive variable formulation used for both time-dependent steady models. numerical graphical outcomes type equations under boundary conditions are explored by implicit finite difference approach with Gaussian elimination scheme. Various pertinent parameters elaborate fluid velocity, surface concentrated nanoparticle volume fraction. Valuable novelty current research use results fluctuating shear stress, fluctuation heating rate rate. It found that parameter enhances velocity distribution maximum variations. noticed as radiation increases. transient stability prominent variations depicted amplitude stress enhances.

Language: Английский

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Nanoparticle aggregation kinematics and nanofluid flow in convectively heated outer stationary and inner stretched coaxial cylinders: Influenced by linear, nonlinear, and quadratic thermal radiation

Modern Physics Letters B, Journal Year: 2024, Volume and Issue: unknown

Published: May 18, 2024

The consequence of nanoparticle aggregation and convective boundary condition on the nanofluid stream past co-axial cylinder with radiation impact is investigated in present examination. influence linear, nonlinear, quadratic thermal flow analyzed. outer stays stable, while inner deforms horizontally axial direction, allowing fluid to flow. By using similarity variables, governing equations are transformed into ordinary differential (ODEs). Subsequently, Runge–Kutta–Fehlberg fourth-fifth order (RKF-45) method employed solve reduced ODEs. upshot several nondimensional terms temperature velocity profiles displayed graphical representation. comparison quadratic, nonlinear profile illustrated. upsurge curvature parameter increases profile. increase intensifies improves a rise values parameter. generates energy zone, which why field has improved. Biot number exhibits an increasing response layer thickness. linear shows better heat transfer compared radiation.

Language: Английский

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The significance of radiative heat and mass transfer through a vertical sheet with chemical reaction: Designing by artificial approach Levenberg-Marquardt

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 56, P. 104208 - 104208

Published: March 7, 2024

The present study examines the impact of incorporating soft computing algorithms during neural network training on evaluation and prediction performance artificial networks. research centers a magneto hydrodynamic flow model (RHMT-VSCR) that depicts movement rotating fluid along vertical sheet in permeable medium. Furthermore, considers heat source-sink interactions, thermal radiation, reactive species conjunction with effects Hall current enhancement energy solute profiles. Because induced magnetic field has negligible Reynolds number, it is disregarded. A set governing nonlinear PDEs converted to system ODEs by applying an appropriate postulate similarity variables order analyze system. multilayer perceptron utilizes supervised Levenberg–Marquardt Backpropagation algorithm (LMLA-BPNN) ascertain ideal quantity neurons be included hidden layer networks intended for modeling purposes. bvp4c numerical approach utilized establish continuous mapping, which produces datasets are purposes authentication, training, testing. range i.e., 10−2−10−8 absolute error reference target data demonstrates optimal accuracy LMLA-BPNN After acquiring knowledge applied approximate solutions wide scenarios through manipulation physical constraints, including suction/injection quantities, parameter, porosity influence characteristics flow, energy, concentration. To assess precision proposed method, statistical graphs based regression, mean squared analysis graphs, histogram employed. results demonstrate Levenberg-Marquardt mappings' derivation, convergence, stability were effectively validated.

Language: Английский

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Numerical paradigm to explore the chemically reacting Williamson nanofluid flow with the influence of bioconvection effects using neural networks

Numerical Heat Transfer Part A Applications, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: April 17, 2024

This research focuses on using a type of advanced computer program called artificial neural networks (ANNs) the Levenberg–Marquardt backpropagation technique (LMBPT-NNs) to stimulate behavior bioconvective effects heat and mass transport in non-Newtonian chemically Williamson nanofluid through stretched surface (BEHMT-NNCRWNF-SS). flow refers specially engineered fluids with nanoparticles dispersed within them moving when subjected external forces. Understanding their is crucial effectively use various applications, especially field transfer fluid dynamics. Before being solved numerically, couple governing partial differential equations are transformed into ordinary suitable similarity functions. The finite difference method (FDM) (Lobatto IIIA) applied for solution nonlinear nanofluidic system by selecting different collocation points. These points play role approximating ensuring accurate results. To address fluidic problems, it's important appropriate results FDM create reference dataset LMBPT-NNs. Statistical analysis should then be performed training, testing, validation datasets obtain optimal scheme issues.. precision LMBPT-NNs checked ANN tools like mean square error (MSE), regression analysis, curve fitness, histogram error.

Language: Английский

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