Bayesian-based neural network scheme to analyze heat transfer enhancement in entropy optimized nanofluid flow: Application to solar energy

At modest nanoparticle concentration, Nanofluid show notable improvements in characteristics including the convective heat transfer coefficient, thermal conductivity that rises with nanoparticle volume percentage and diffusivity. Nanofluid offer significant potential for designing nanotechnology-based operational cooling systems for a variety of innovative applications. MHD radiative NF flow on a curvilinear permeable sheet under Darcy–Forchheimer porous resistance is examined in this work by using ANNs. Heat generation and thermal dissipation are included in the analysis, together with temperature-dependent thermal conductivity and variable mass diffusivity. Convective boundary conditions are incorporated. Brownian motion and thermophoretic effects are taken into account. This model also integrates Dufour and Soret effects, investigates binary chemical reactions and thermal radiation, and incorporates entropy generation by using ANNs. 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, concentration, velocity, pressure, thermal dissipation and entropy rate. The temperature gradient, velocity gradient, pressure gradient, concentration gradient and thermal dissipation rate are all represented graphically. In recent work, LBP-ANNs is used to discussed the solution behavior of MHD radiative NF flow on a curvilinear permeable surface with entropy generation impact. To deduced the solution behavior of , , , , and , the number of parameters including , , , , , 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 rapidly increases with growing differences of & . The profile declines as an increase in & . The profile rises when there is an increase in while it declines as an increase in . The profile declines with increasing difference of & . The profile tends to decrease when the values of increase while it increases with increasing difference of . The entropy generation profile rises up with rising values of and . The MSE consequences (testing, training, validation) for NF flow on a stretching curved 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 twelve scenarios of for NF flow on a stretching curved sheet. The R-squared value is equal to 1 for all data sets of for NF flow on a stretching curved sheet. The AE for NF flow on a stretching curved sheet is noted between for all twelve scenarios.