Analysis of heat transfer characteristics for tetra nanofluid flow based on entropy rate and non-linear radiations through Bayesian-based neural network scheme

Nanofluids are at the forefront of research on enhanced HT fluids, which has a significant impact. They are widely applied across heat exchangers, mechanical systems, chemical processing, and other industries that demand superior HT capabilities. The present study addresses a Sakiadis flow configuration utilizing the advanced tetra-NF , owing to its improved thermal characteristics relative to previous NF formulations. This formulation utilizes recently developed characteristics alongside appropriate transformation functions. In addition, the energy equation is modified to include nonlinear radiation, dissipation, and convective heating effects, making it more applicable to thermal systems. The PDEs are transformed into ODEs using similarity variables. LBP-ANNs are used to create and explain a framework for entropy generation analysis. ANNs are used to graphically analyze temperature, velocity and entropy rate. These gradients are all represented graphically. In recent work, LBP-ANNs is used to discussed the solution behavior of Sakiadis flow by using tetra-NF on a flat moving surface with entropy generation impact. To deduced the solution behavior of , and , the number of parameters likes; , , and varied. In each case, ANNs are used to display the graphical results of MSE, EH, TSF, FSF, RGN-A, solution evaluation, and AE findings. The profile rises up when there is an increase in . The profile tends to decline with increasing difference of , while it increases with increasing values of & . The entropy generation profile rises up with rising values of , while it declines with rising values of & . The MSE consequences (testing, training, validation) for tetra-NF flow on a flat moving sheet lies between The values of performance grids are lies between , while gradients values lie around by using ANNs. The EHA range recorded around for all seven scenarios of tetra-NF flow on a moving flat sheet. The R-squared value is equal to 1 for all data sets of tetra-NF flow on a moving flat sheet. The AE is noted between for all seven scenarios.

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