A qualitative analysis of the artificial neural network model and numerical solution for the nanofluid flow through an exponentially stretched surface DOI Creative Commons
Asad Ullah, Hongxing Yao, Waseem Waseem

et al.

Frontiers in Physics, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 17, 2024

This article aims to analyze the two-dimensional (2D) nanofluid (Ag/C 2 H 6 O ) flow past an exponentially stretched sheet. The magnetic field impact, heat source/sink, and convection in thermal profile are taken into account. complexity of problem is reduced by introducing a dimensionless group functions. model transformed system first-order ordinary differential equations (ODEs). further analyzed with artificial neural network (ANN), which trained using Levenberg–Marquardt algorithm. whole dataset sub divided three parts: training ( 70% ), validation id="m2">15% testing id="m3">15% ). impact nonlinear source/sink parameter, volume fraction nanoparticles, Prandtl number displayed through graphs. source, fraction, cause increase its larger values. parameter causes decline both momentum boundary layers higher analysis shows that energy enhanced values silver nanoparticles source. For each case study, residual error (RE), regression line, results presented. performance proposed methodology numerically tabulated for nanoparticle shown Table 3 , where minimum absolute (AE) id="m4">5.3373e−11 at id="m5">ϕ=0.05 . Based on this, we recommend id="m6">ϕ=0.05 better performance. AEs ANN bvp4c computed state variables Tables id="m7">M=5,10 15. These tables show overall validate present study. We have also validated mean squared graphically, accuracy proven.

Language: Английский

Darcy-Forchheimer MHD micropolar water based hybrid nanofluid flow, heat and mass transfer features past on stretching/shrinking surface with slip and radiation effects DOI Creative Commons
M. Asif Memon, J. Kavikumar,

Hazoor Bux Lanjwani

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102534 - 102534

Published: July 11, 2024

Hybrid nanofluids (HNF) play a vital role in enhancing the heat transfer characteristics of all types traditional fluids, both industrial and experimental applications. In this regard, laminar two-dimensional (2D) boundary layer magnetohydrodynamic (MHD) Darcy-Forchheimer flow, transfer, mass transfers Cu–MoS2/micropolar water-based hybrid nanofluid have been considered over stretching/shrinking surface. The thermal radiation partial slip effects are porous medium. governing differential equations (PDEs) transformed into ordinary (ODEs) using appropriate similarity transformations. numerically solved shooting method Maple software, dual solutions obtained for different ranges applied parameters. physical parameters nanoparticle volume fractions on velocity, microrotation, temperature, concentration profiles along with skin friction, couple stress, Nusselt Sherwood numbers examined. main findings study show that velocity decrease an increase suction, number, slip, magnetic micromaterial parameters, while oppositely, it increases fractions. Moreover, field, fractions, temperature profiles, Prandtl decreases it. An stress coefficient, but number variation suction.

Language: Английский

Citations

22
Diversified characteristic of carbon nanotube nanoparticles on the entropy minimization for the flow of hybrid nanofluid through a convectively heated surface DOI
Rupa Baithalu, Titilayo M. Agbaje, S. R. Mishra

et al.

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Journal Year: 2024, Volume and Issue: 104(9)

Published: July 19, 2024

Abstract An analysis of entropy is essential to determine the heat transfer efficiency characteristics nanofluids in different applications. Implementation carbon nanotubes (CNTs) that combined effect “single‐wall nanotube” (SWCNT) and “multi‐wall (MWCNT) water shows their effective properties enhancing transport phenomena. In general, these are useful industrial processes for better shape product proposed as a coolant, cancer therapy, solar radiation, etc. Based on special characteristics, current investigation analyses flow water‐based CNT cross‐hybrid nanofluid past convectively heated surface. The characteristic enriches by insertion dissipative heat, thermal external source/sink. appropriate choice similarity rules transforming governing designed problem non‐dimensional form further, “ spectral quasi‐linearization method (SQLM) ” imposed solve set equations. After getting result, process irreversibility due various factors obtained, is, presented briefly. physical significance deployed graphically described discussion section. However, validation with earlier result projected show good correlation.

Language: Английский

Citations

5
Review on velocity slip with thermal features of irregular heat transport enhancement of hybrid nanofluids DOI Creative Commons
Mudassar Qamar,

Masood Khan,

Muhammad Yasir

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103606 - 103606

Published: Dec. 1, 2024

Language: Английский

Citations

5
The Artificial Neural Network Optimization for Thermally Magnetized Williamson Fluid Flow over a Porous Surface DOI

P. Priyadharshini,

V. Karpagam

International Journal of Applied and Computational Mathematics, Journal Year: 2025, Volume and Issue: 11(2)

Published: March 15, 2025

Language: Английский

Citations

0
Applications of Radiated Tri Hybrid Nanoparticles (TiO2-CuO-SiO2) on Thermal Performance of Engine Oil (SAE10W-30): Case Study for HNF and MNF DOI Creative Commons
Walid Aich,

Sami Ullah Khan,

Muhammad Ishaq

et al.

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 106144 - 106144

Published: April 1, 2025

Language: Английский

Citations

0
Thermal evaluation of radiated ternary hybrid nanoparticles with quadratic thermal constraints: Advances to solar energy and industrial heat management DOI
Ahmed Mir, Sami Ullah Khan, Nermeen Abdullah

et al.

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(2), P. 101536 - 101536

Published: April 21, 2025

Language: Английский

Citations

0
Entropy Analysis of Hall-Effect-Driven Ti−CoFe2O4/ Engine Oil-Based Hybrid Nanofluid Flow Between Spinning Porous Disks with Thermal Convective Boundaries DOI Creative Commons

Sk Enamul,

Surender Ontela

JCIS Open, Journal Year: 2025, Volume and Issue: unknown, P. 100134 - 100134

Published: March 1, 2025

Language: Английский

Citations

0
Optimization of convective heat transfer and thermal storage in ternary hybrid nanomaterials using machine learning-driven exogenous neural networks with radiation effects DOI

Li Yongxin,

Muhammad Habib Ullah Khan,

Waqar Azeem Khan

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 120, P. 116395 - 116395

Published: April 4, 2025

Language: Английский

Citations

0
A recurrent neural network approach for magneto‐hydro‐dynamic flow of second‐grade fluid with dissipation effect DOI
Aamra Urooj, Muhammad Kamran, Muhammad Asif Zahoor Raja

et al.

ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Abstract Artificial neural networks (ANNs) with feedback loops known as recurrent (RNNs) are appropriate for handling temporal dependencies. The accuracy of the results in computational fluid dynamics (CFD) has gradually improved integration artificial intelligence (AI) CFD. This research article aims to decipher magneto‐hydro‐dynamic flow second‐grade dissipation effect (MHD‐FSGF‐DE) using Levenberg–Marquardt backpropagation (LMB) based on RNNs (LMB‐RNNs). dataset is produced by cutting‐edge homotopy analysis method variation different parameters including parameter β , Marangoni M a Hartmann number and Prandtl Pr. trained points maximize outcome's provide comprehensive knowledge long‐term correlations between input output data points. state‐of‐the‐art LMB‐RNNs approach validated performance graphs, error histograms, training estate analyses, regression plots, correlation plots. profiles physical properties like velocity, temperature, concentration against Pr graphically shown further highlight how these affect properties. After 1000 iterations, mean squared (MSE) 10 −10 observed value coefficient R 1 endorsing worth LMB‐RNNs. velocity upsurges increasing while declines . outcomes comparable previously published findings. core findings this study have potential applications various fields polymer processing cooling electronic devices, specifically areas coolant system design optimization.

Language: Английский

Citations

2
A qualitative analysis of the artificial neural network model and numerical solution for the nanofluid flow through an exponentially stretched surface DOI Creative Commons
Asad Ullah, Hongxing Yao, Waseem Waseem

et al.

Frontiers in Physics, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 17, 2024

This article aims to analyze the two-dimensional (2D) nanofluid (Ag/C 2 H 6 O ) flow past an exponentially stretched sheet. The magnetic field impact, heat source/sink, and convection in thermal profile are taken into account. complexity of problem is reduced by introducing a dimensionless group functions. model transformed system first-order ordinary differential equations (ODEs). further analyzed with artificial neural network (ANN), which trained using Levenberg–Marquardt algorithm. whole dataset sub divided three parts: training ( 70% ), validation id="m2">15% testing id="m3">15% ). impact nonlinear source/sink parameter, volume fraction nanoparticles, Prandtl number displayed through graphs. source, fraction, cause increase its larger values. parameter causes decline both momentum boundary layers higher analysis shows that energy enhanced values silver nanoparticles source. For each case study, residual error (RE), regression line, results presented. performance proposed methodology numerically tabulated for nanoparticle shown Table 3 , where minimum absolute (AE) id="m4">5.3373e−11 at id="m5">ϕ=0.05 . Based on this, we recommend id="m6">ϕ=0.05 better performance. AEs ANN bvp4c computed state variables Tables id="m7">M=5,10 15. These tables show overall validate present study. We have also validated mean squared graphically, accuracy proven.

Language: Английский

Citations

1