@article{Cheng2025, 
author = {Kuiyu Cheng and Abdelraheem M. Aly and Nghia Nguyen Ho and Sang-Wook Lee and Andaç Batur Çolak and Weaam Alhejaili},
title = {Exothermic thermosolutal convection in a nanofluid-filled square cavity with a rotating Z-Fin: ISPH and AI integration},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {3},
pages = {5830-5858},
keywords = {Cattaneo–Christov heat flux, exothermic chemical reaction, ISPH method, magnetic field, rotating Z-shaped fin},
url = {https://www.sciopen.com/article/10.3934/math.2025268},
doi = {10.3934/math.2025268},
abstract = {This study explores the combined effects of exothermic chemical reactions and Cattaneo–Christov heat flux on thermosolutal convection within a nanofluid-filled square cavity containing a rotating Z-shaped fin. The incompressible smoothed particle hydrodynamics (ISPH) approach was employed, utilizing boundary particle renormalization to accurately model boundary conditions. An artificial neural network (ANN) model, trained on ISPH simulation data, predicted the average Nusselt number (             Nu              avg      ) and average Sherwood number (             Sh              avg      ) with high accuracy. A dataset comprising 56 data points was used, from which 40 data points were used for training, 8 for validation, and 8 for testing. The Z-shaped fin, centrally positioned, rotates at a fixed angular velocity, maintaining lower temperature and concentration levels, while the cavity's vertical walls exhibit elevated thermal and solutal conditions. Results indicate that the Z-shaped fin's geometry, exothermic reaction rates, and magnetic field strength significantly influence heat and mass transfer and fluid dynamics. For instance, increasing the Hartmann number (   Ha) from 0 to 50 decreased nanofluid velocity by 61.99%, while              Nu              avg       and              Sh              avg       were reduced by 16.87% and 11.81%, respectively. Additionally, increasing the nanoparticle volume fraction from 0 to 0.15 enhanced              Nu              avg       by 22.43% and              Sh              avg       by 116.3%. The ANN model, employing the Levenberg–Marquardt algorithm, achieved a coefficient of determination    R  =  0.99994 and a mean squared error    MSE  =  4.21  ×            10              −      6      , demonstrating its reliability in predicting thermal performance. These findings underscore the study's relevance to applications such as energy systems, refrigeration, and heat exchangers.}
}