Cited by Machine learning estimation of heat and mass transfer attributes of thermal radiative Williamson nanofluid flow via nonlinear stretchable surface

Computational analysis of Yamada–Ota and Xue models for surface tension gradient impact on radiative 3D flow of trihybrid nanofluid with Soret–Dufour effects DOI

Sayer Obaid Alharbi,

Munawar Abbas,

Ahmed Babeker Elhag

и другие.

Microfluidics and Nanofluidics, Год журнала: 2024, Номер 29(2)

Опубликована: Дек. 23, 2024

Язык: Английский

Процитировано

Neural network-assisted analysis of MHD boundary layer flow and thermal radiation effects on SWCNT nanofluids with Maxwellian and non-Maxwellian models DOI

K. Jyothi, A. Sailakumari,

Ramachandra Reddy Vaddemani

и другие.

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(2)

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

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

Reima Daher Alsemiry, Sameh E. Ahmed, Mohamed R. Eid

и другие.

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

Процитировано

Machine learning estimation of heat and mass transfer attributes of thermal radiative Williamson nanofluid flow via nonlinear stretchable surface DOI

Syed M. Hussain,

Dilawar Nawaz,

Bushra Attique

и другие.

Journal of Radiation Research and Applied Sciences, Год журнала: 2025, Номер 18(3), С. 101581 - 101581

Опубликована: Май 12, 2025

Язык: Английский

Процитировано