Thermal transport of radially magnetized peristalsis of non-Newtonian nanofluid through an asymmetric curved channel DOI
J. Iqbal,

M. Gul,

F. M. Abbasi

et al.

Numerical Heat Transfer Part B Fundamentals, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: June 18, 2024

Present examination explores the heat and mass transfer phenomena for magnetohydrodynamics (MHD) peristaltic transport of diethylene glycol (DEG)-based Cross nanofluid through an asymmetric curved channel. The thermal characteristics are established assessment Buongiorno nano-liquid model, which allows to identify intriguing features thermophoretic Brownian diffusion coefficients. Further, velocity slip conditions enforced on walls. influences radiation, radius-dependent magnetic field viscous dissipation also taken into consideration. governing equations simplified by employing lubrication theory ("biological estimate creeping transportation phenomenon"), resulting system is tackled numerically. Impacts different flow parameters nanofluid's velocity, nanomaterials concentration profile, transfer, streamlines, temperature nanofluid, stresses at wall analyzed via graphs tables. findings this investigation report that enhances against Hartmann Brinkman numbers, whereas it declines radiation parameter. distribution profile decreases motion while increases thermophoresis a development in stresses, rates boundary seen better values number. Additionally, higher parameter show increasing behavior near walls effects MHD with magnesium aluminate nanoparticles suspended DEG base fluid-based conduit have many uses industry, organic compounds, biomedical engineering, commercial productions, such as brake fluid, tobacco, polyester resins, certain dyes, printing ink, polyurethanes, glue, antifreeze, nitrocellulose, oils, cigarettes, plasticizers, so forth. DEG-based nanofluids used human medications, including acetaminophen sulfanilamide, can result incidents poisoning, some been fatal, either intentionally or unintentionally.

Language: Английский

Analysis of nonlinear complex heat transfer MHD flow of Jeffrey nanofluid over an exponentially stretching sheet via three phase artificial intelligence and Machine Learning techniques DOI
A. Zeeshan,

Nouman Khalid,

R. Ellahi

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 189, P. 115600 - 115600

Published: Oct. 7, 2024

Language: Английский

Citations

38
Advanced intelligent computing ANN for momentum, thermal, and concentration boundary layers in plasma electro hydrodynamics burgers fluid DOI
Muhammad Imran Khan,

Refka Ghodhbani,

T.A. Taha

et al.

International Communications in Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 159, P. 108195 - 108195

Published: Oct. 23, 2024

Language: Английский

Citations

16
Intelligent Computing Technique to Analyze the Two-Phase Flow of Dusty Trihybrid Nanofluid with Cattaneo-Christov Heat Flux Model Using Levenberg-Marquardt Neural-Networks DOI Creative Commons
Cyrus Raza Mirza, Munawar Abbas, Sahar Ahmed Idris

et al.

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105891 - 105891

Published: Feb. 1, 2025

Language: Английский

Citations

7
Artificial neural network analysis of MHD Maxwell nanofluid flow over a porous medium in presence of Joule heating and nonlinear radiation effects DOI
Muhammad Idrees Afridi,

Bandar Almohsen,

Shazia Habib

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 192, P. 116072 - 116072

Published: Jan. 31, 2025

Language: Английский

Citations

2
Analysis of thermal transport and wall stresses for MHD peristaltic motion of Reiner‐Philippoff fluid with viscous dissipation and mixed convection effects DOI

Mohammed Alkinidri,

J. Iqbal, F. M. Abbasi

et al.

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Journal Year: 2025, Volume and Issue: 105(1)

Published: Jan. 1, 2025

Abstract The present study examines the thermal characteristics and stresses at boundary for peristaltic motion of Reiner‐Philippoff fluid through a symmetric channel. (R‐P) model is widely recognized its ability to provide comprehensive representation unique properties exhibited by non‐Newtonian fluids. One notable aspect that initiates this nonlinear relationship between velocity gradient shear stress. Moreover, captures implicit connection deformation rate Additionally, R‐P exhibits distinct characteristics, acting as dilatant , exhibiting pseudoplastic behavior behaving Newtonian when . Governing equations are mathematically modeled under consideration mixed convection, viscous dissipation, magnetic field, Joule heating effects. Long wavelength small Reynolds number approximations used simplify system. To compute numerical solution simplified system, BVP4c technique employed via MATLAB. influences key parameters on flow physically visualized graphs. A detailed analysis heat transfer dilatant, pseudoplastic, fluids also provided. assessments wall presented tables. Outcomes reveal temperature profile decreases due parameter Bingham number. findings case indicate both enhanced Grashof Hartmann numbers lead an increase in profile. Tabular results improved developing values Brinkman numbers, while exhibit opposite behavior. decrease with greater Furthermore, more effective improving reducing compared

Language: Английский

Citations

1
Bayesian regularization-based intelligent computing for peristaltic propulsion of curvature-dependent channel walls DOI
J. Iqbal, Yasir Akbar, Mohammad Mahtab Alam

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(2)

Published: Feb. 1, 2025

This study investigates the numerical analysis of curvature-dependent symmetric channel walls filled with porous media, focusing on various flow characteristics using Artificial Neural Networks optimized Levenberg–Marquardt Backpropagation Scheme (ANNs-BLMS). The explores Electrically Conducting Peristaltic Propulsion Carreau–Yasuda Ternary Hybrid Nanofluids (ECPPCY-THNFs) propagating through sinusoidal wave trains within a curved conduit. To streamline analysis, governing equations have been simplified under specific assumptions lubrication theory. are solved Adam and three-stage Lobatto IIIa formula techniques to generate dataset spanning walls, covering four cases nine scenarios ECPPCY-THNFs. encompasses ECPPCY-THNFs, step size 0.02. As result, domain is divided into 131 grid points for velocity temperature profiles 71 rates heat transfer analysis. three parts: 10% training, testing, 80% validation. apply proposed methodology, constructed by varying Hartmann number, rate, Darcy curvature parameter, radiation parameter. Subsequently, an artificial intelligence-based algorithm employed derive solution expressions fields analyze dataset. results presented detailed tabular graphical illustrations. Heat performed model, findings validated multiple techniques, including error histograms, regression plots, mean square (MSE), time series autocorrelation, state transition. A comparative between two methods Intelligence (AI)-generated predictions also undertaken. obtained AI-based ANN-BLMS framework confirm reliability accuracy methodology in effectively solving demonstrate that parameter has considerable effect mechanical thermal aspects flow, therefore, it must be incorporated modeling flows channels. Additionally, rate 7.5 critical value, representing minimum required sustain fluid channel. When below this increase decrease profile. However, when exceeds profile shows opposite trend. Furthermore, ternary hybrid nanofluids show concave-up shapes (Θ) values greater than concave-down less 7.5. highest lowest velocities occur near center Θ>7.5 Θ<7.5, respectively. Moreover, coefficient determination values, used as performance indicators, found unity (1.000) ANN model. MSE histogram 2.8467 × 10−11 −3.05 10−7,

Language: Английский

Citations

0
Cross-coupling in hydrodynamic phase-field models for nonisothermal binary fluids DOI Creative Commons
Shouwen Sun, Jun Li, Qi Wang

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 195, P. 116286 - 116286

Published: March 22, 2025

Language: Английский

Citations

0
Thermal transport and irreversibility analysis for radially magnetized Carreau–Yasuda nanofluids flow over polished surfaces DOI

Mohammed Alkinidri,

J. Iqbal, F. M. Abbasi

et al.

Modern Physics Letters B, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

A major challenge in modern industry is the need for efficient heat transfer fluids, as conventional fluids often do not provide necessary efficiency heating and cooling processes. Nanofluids, seen future of are developed by dispersing nanoparticles into base fluids. Owing to their unique dynamic thermophysical properties, these innovative nanofluids have a broad spectrum applications nanotechnology advanced systems. Based on nanofluids, flow composed graphene suspended lubricant oil with studied here. Flow induced curved stretching sheet. The surface sheet considered be polished uniformly, which hence facilitates velocity slip. novelty this study lies utilizing rheological properties dispersed oil-based fluid, empirically investigated Bakak et al. (2021). Their findings suggest that nanofluid exhibits non-Newtonian behavior, experimental data closely aligning Carreau–Yasuda model. radially varying magnetic field influences generating Lorentz force Ohmic heating. In view experimentally reported results, depicted using Furthermore, configuration influenced thermal radiation, source, viscous dissipation, convective at boundary. Irreversibility analysis carried out propose ways energy optimization. Boundary layer approximations utilized model partial differential equations (PDEs) governing system. By applying non-similarity transformation, original PDEs converted dimensionless nonlinear PDEs. local approach, truncated second order, reducing them ordinary (ODEs) can more easily solved. resulting system tackled through BVP4c algorithm MATLAB. Influences pertinent parameters profile, drag force, isotherms, nanofluid’s temperature, streamlines, entropy generation number, rates, Bejan number analyzed graphs tables. Findings indicate temperature distribution improves higher values Biot Weissenberg number. Additionally, decreases increasing Also, it noted velocity. demonstrates direct relationship profile. magnitude coefficient curvature parameter volume fraction nanoparticles. Heat rises elevated parameter, Eckert source radiation but an increase

Language: Английский

Citations

0
Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects DOI
T.A. Taha, Sohaib Abdal, Liaqat Ali

et al.

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 66, P. 102071 - 102071

Published: April 25, 2025

Language: Английский

Citations

0
A cross-fluid heat transfer analysis using neural networks over porous rotating disk DOI
Fahim Ullah,

Muhammad Bilal Ashraf

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(9)

Published: Sept. 1, 2024

This research explores the complex interaction of incompressible cross-fluid flow, heat, and mass transfer characteristics on a porous rotating disk. The study employs sophisticated mathematical methods, including similarity transformations, to convert governing partial differential equations into nonlinear ordinary equations. These are then solved using numerical method, fourth-class boundary value problem. We employ an Artificial Neural Networks algorithm with backpropagation Levenberg–Marquardt Scheme analyze heat mechanism quantitatively. Our results provide accurate values for Nusselt number, Sherwood skin friction coefficient. examination addresses this system's fluid mechanics transport phenomena potential applications in engineering industrial processes.

Language: Английский

Citations

3