Thermal Performance and Heat-mass Transfer in Ternary Hybrid Nanofluids with Straight and Corrugated Baffles: A Coupled FEM-ANN Study
DOI:
https://doi.org/10.15377/2409-5826.2026.13.3Keywords:
Corrugated baffles, Heat and mass transfer, Artificial neural network, Ternary hybrid nanofluid, Thermo-solutal natural convectionAbstract
Background: The development of advanced thermal systems needs efficient heat and mass transfer enhancement. This research study examines thermo-solutal natural convection in a closed rectangular enclosure which contains a ternary nanofluid and two types of baffles that include straight and corrugated designs to achieve better transport performance through physical and geometric changes.
Methods: The governing two-dimensional transport equations are solved numerically using the finite element method (FEM). A comprehensive parametric analysis is performed to investigate the effects of baffle length, corrugation amplitude, number of corrugations, Rayleigh number, Lewis number and nanoparticle volume fraction. In addition, a multilayer perception based artificial neural network (ANN) is developed and trained using finite element generated data to accurately predict heat and mass transfer characteristics across varying operating conditions, providing a fast and reliable surrogate model.
Significant findings: The results reveal that corrugated baffles significantly enhance flow disturbance, thermal mixing and overall transport efficiency compared to straight baffles, leading to higher Nusselt and Sherwood numbers. Increasing the nanoparticle volume fraction from ϕ=0 to 0.03 increase Nusselt number from 5.6376 to 12.108 corresponding to an enhancement of approximately 114.8% while the Sherwood number increase approximately 64.8%. The ANN surrogate exhibits high predictive accuracy. In the completely held-out edge-domain test, the average Nusselt number prediction achieves R2=0.99940, RMSE =9.341×10-3, and MAE =6.366×10-3, while the average Sherwood number prediction achieves R2=0.99196, RMSE =3.264×10-2, and MAE =2.068×10-2. These results demonstrate the effectiveness of the coupled FEM–ANN approach for analyzing and predicting heat and mass transfer in baffled ternary-hybrid-nanofluid systems.
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