Abstract
This article focuses on the stagnation point flow of hybrid nanofluid towards a flat plate. The cases when the buoyancy forces and the flow are in the opposite direction and the same direction are discussed. The effect of radiation and suction is also taken into account. The similarity transformations are used to convert the partial differential equations into nonlinear ordinary differential equations. These equations are computed numerically via the bvp4c function in MATLAB software. A comparison with the previously published articles is carried out, where an outstanding agreement is observed. The dual solutions exist in the case of opposing flow (λ<0) and the suction parameter S>0.6688. Meanwhile, only unique solutions exist in the case of assisting flow (λ>0). The existence of dual solutions leads to stability analysis. From the analysis, the first solution is confirmed as a stable solution. Furthermore, the heat transmission rate increases, while the skin friction coefficient decreases as the radiation rate increases. An increase in the radiation rate from 0 (no radiation) to 1.0 increases the heat transmission rate by 5.01% for water, 4.96% for nanofluid, and 4.80% for hybrid nanofluid. Finally, it is worth mentioning that the present study yields new and original results. This study has also not been done by other researchers, indicating its novelty.
Highlights
Over the last few decades, the topic of nanofluid has attracted a vast number of researchers due to its contribution to industries
The analysis of mixed convection stagnation point flow in a hybrid nanofluid past a permeable flat surface has been investigated under the influence of radiation
The heat transfer rate intensified with the radiation effect
Summary
Over the last few decades, the topic of nanofluid has attracted a vast number of researchers due to its contribution to industries. A new kind of fluid, namely hybrid nanofluid, is introduced by suspending two different nanoparticles in the regular fluid. By combining these nanoparticles, their chemical and physical properties will simultaneously combine and lead those properties in a homogeneous state [2,3]. Esfe et al [14] studied the thermal properties of Mg/O water nanofluid using artificial neural networks (ANNs). Fuxi et al [15] investigated the thermal characteristics of water-EG/MWCNT-Al2O3 hybrid nanofluid using a feed-forward neural network. Esfe and Toghraie [17] applied an optimal feed-forward artificial neural network model and new empirical correlation to predict the viscosity of Al2O3-engine oil nanofluid. It is found that ANN estimated laboratory data more accurately than correlation output and ANN output
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