Irreversibility analysis of the flow of a radiative Casson–Williamson hybrid ferrofluid over a moving cylinder

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Год журнала: 2025, Номер 105(3)

Опубликована: Март 1, 2025

Abstract Entropy generation refers to the energy losses and irreversibilities in dynamics of non‐linear flow heat transfer process. Its applications span a wide range, including exchangers, turbomachinery, chemical reactors, microfluidic devices, many others. In this study, we analyze irreversibility deionized water‐based Casson–Williamson hybrid ferrofluid flowing over continuously moving cylinder. A magnetic field with non‐changing intensity is exerted transversely direction which fluid streams. The bivariate spectral local linearization method on multiple overlapping single grids used solve resulting equations. results are validated by comparing them existing studies under certain limiting conditions. Graphical presented show effects parametric values, such as volumetric fraction () ferrous oxide aluminum oxide, Casson Williamson parameters thermal radiation parameter velocity, temperature distribution, entropy generation, Bejan numbers. Findings indicate that fractions increase magnitude generated system.

Язык: Английский

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Dynamical Analysis of Response in a Micropolar Thermodiffusive Medium With Two Temperatures and Variable Thermal Conductivity Based on Eringen's Nonlocal Elasticity

Heat Transfer, Год журнала: 2025, Номер unknown

Опубликована: Март 25, 2025

ABSTRACT The present study enlightens the analysis of transient disturbances in a nonlocal micropolar thermodiffusive medium with two temperatures and variable thermal conductivity on account mechanical load. theoretical model is established framework Eringen's elasticity theory Green–Lindsay theory. By addressing scientific engineering domains, mathematical holds potential to stimulate practical innovations design optimization advanced materials devices tailored for real‐world applications. analytical solution procured by employing normal mode displacement components, stresses, temperatures, concentration space–time domain. A numerical simulation magnesium crystal material carried out using MATLAB software investigate impacts various parameters thermophysical quantities, outcomes are illustrated graphically. graphical results demonstrate that micropolarity diffusivity have significant effects physical fields. Temperature fields increasingly influenced conductivity, which signifies importance this parameter. comparative two‐temperature one‐temperature generalized thermoelasticity presents difference magnitudes quantities constituting model. reveal all distributions restricted bounded region, exhibiting finite speed thermoelastic signals. Some specific cases been derived from particularly noteworthy. To best authors' knowledge, no research emphasizing dynamic response microstructured has conducted, significantly defines novelty conducted research.

Язык: Английский

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Effect of Nanoparticle Size on the Stability and Efficiency of Silver Nanofluids in Solar Thermal Systems

Russian Journal of General Chemistry, Год журнала: 2025, Номер 95(3), С. 722 - 731

Опубликована: Март 1, 2025

Язык: Английский

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Density-driven flow in chamfered enclosures with obstacles under regular and irregular inner temperature distributions: A hybrid finite element method and neural network approach

Separation Science and Technology, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Март 26, 2025

Язык: Английский

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ANN-Based Prediction and RSM Optimization of Radiative Heat Transfer in Couple Stress Nanofluids with Thermodiffusion Effects

Processes, Год журнала: 2025, Номер 13(4), С. 1055 - 1055

Опубликована: Апрель 1, 2025

This research investigates the impact of second-order slip conditions, Stefan flow, and convective boundary constraints on stagnation-point flow couple stress nanofluids over a solid sphere. The nanofluid density is expressed as nonlinear function temperature, while diffusion-thermo effect, chemical reaction, thermal radiation are incorporated through linear models. governing equations transformed using appropriate non-similar transformations solved numerically via finite difference method (FDM). Key physical parameters, including heat transfer rate, analyzed in relation to Dufour number, velocity, parameters an artificial neural network (ANN) framework. Furthermore, response surface methodology (RSM) employed optimize skin friction, transfer, mass by considering influence radiation, slip, reaction rate. Results indicate that velocity enhances behavior reducing temperature concentration distributions. Additionally, increase number leads higher profiles, ultimately lowering overall ANN-based predictive model exhibits high accuracy with minimal errors, offering robust tool for analyzing optimizing transport characteristics nanofluids.

Язык: Английский

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Boundary Layer Convective Flow in a Divergent Channel With Melting Heat Transfer and Mass Suction/Injection: An Analysis Using ANN and Numerical Methods

Heat Transfer, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

ABSTRACT This study investigates magnetohydrodynamic fluids in converging and diverging channels, with a focus on melting heat transfer effects. The investigation utilizes combination of numerical techniques, specifically the finite element Galerkin method, artificial neural network (ANN) modeling to examine fluid flow behavior thermal patterns various channel configurations. Numerical explanations are provided for effect these factors temperature, velocity, local skin friction, Nusselt number distributions. Upon careful analysis graphical presentation, acquired data provide significant understanding how different physical parameters affect properties. contrast between current past findings reveals good agreement. have important applications engineering fields where specific control is essential, including plastic sheet extrusion, electronic device cooling, metal casting. Additionally, research employs ANN enhance prediction characteristics, demonstrating strong correlation predicted theoretical results. Accurate regulation transmission crucial technical areas, such as casting, cooling.

Язык: Английский

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Computational investigation on non‐Fourier heat transfer in polymeric liquid with multi‐nanoscale and microscale structures in Forchheimer porous medium using Galerkin finite element method

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Год журнала: 2025, Номер 105(5)

Опубликована: Май 1, 2025

Abstract Several physical phenomena associated with fluid–solid interaction occur in the presence of granular, micro, nanoscale, and crystal structures. Heat transfer fluids containing such microbodies cannot be studied using conventional rheological stress–strain relations. Eringen developed micropolar theory for rheology to investigate related fields flow behavior. In contrast classical law heat conduction, polymeric liquids display thermal relaxation time conduction. To avoid any discrepancy, a generalized non‐Fourier is used studying transfer. A theoretical approach via flux theories conservation laws. The Galerkin finite element method implemented visualize record simulations study mixed convection exhibiting couple stresses, microinertia, spin gradients, viscosity effects, wall vorticity rate has highest value comparison mono di nanofluids. shear stress ternary nanofluids on surface sheet noted relative hybrid Moreover, Newtonian fluid less than that fluid. phenomenon strong ‐micropolar fluids. nanofluid bears maximum retarding force from porous medium case weaker

Язык: Английский

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Effects of thermal radiation on MHD bioconvection flow of non-Newtonian fluids using linear regression based machine learning and artificial neural networks

International Journal of Numerical Methods for Heat &amp Fluid Flow, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Purpose This paper aims to investigate the effects of thermal radiation on magnetohydrodynamics (MHD) bioconvection nonlinear complex structure flow non-Newtonian fluids such as Casson, Williamson and Sisko fluids. Design/methodology/approach The coupled fundamental equations governing steady, incompressible combined with Casson–Williamson–Sisko over an exponential sheet are reduced ordinary differential using appropriate transformations. Open-source platforms Google Colab Python used. Results, performance, accuracy correlation examined neural networking, Levenberg-Marquardt, machine learning, artificial intelligence (AI) algorithms linear regression. Findings Numerical graphical results presented observe impact physical parameters. prospect AI tools, particularly increases developed fluid dynamics models. Besides, further scope learning in hybrid nature is also presented. It concluded that Levenberg-Marquardt algorithm most suitable for simulation boundary layer high accuracy, smooth regression curves minimum rate error. observed range 10 −8 mean squared error shows good fit model. noted by increasing Casson fluids’ parameters, velocity profile decreases. Both concentration motile density decrease values Schmidt Peclet numbers. Originality/value existing literature lacks a comparative analysis networks predicting AI-based approaches, MHD literature. effort devoted fill said gap.

Язык: Английский

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A comparative performance analysis of machine learning techniques for optimizing heat transfer between parallel disks

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Год журнала: 2025, Номер unknown

Опубликована: Май 17, 2025

This research rigorously investigates the heat transfer dynamics between parallel disks through a combined experimental and machine learning methodology, focusing on flux, Reynolds number, gap ratio. Conducting 100 experiments across varied numbers, fluxes, ratios, study identifies optimal parameter values where Nusselt number maximizes, attributing this to enhanced convective transfer. An artificial neural network (ANN) model, refined using Teaching-Learning-Based Optimization (TLBO) JAYA algorithms, accurately predicts confirming findings providing robust tool for optimizing systems in applications like gas turbines exchangers. The underscores critical importance of precise flow control, offering significant advancements design optimization engineering involving disks. Results demonstrate highest average 48.132 at ratio 20.38, flux 444.30 W/m 2 , 99.99, validating reliability proposed models.

Язык: Английский

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Developing regenerable levodopa/ε-polylysine magnetic nanoparticles for rapid selective adsorption of anionic dyes

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

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