A Fuzzy-Based Computational Framework for MHD Sutterby Hybrid Nanofluid Flow with Bioconvection Induced by Microorganism and Homogeneous–Heterogeneous Chemical Reaction

This research work studied the magnetohydrodynamic (MHD) Sutterby hybrid nanofluid flow over a stretching sheet, incorporating thermal radiation along with homogeneous and heterogeneous chemical reactions (H-H), while the modified Buongiorno’s model utilizes significant scientific facts to represent the complex behavior of nanofluids. Cadmium selenide (CdSe) and chitosan 〖(C_6 H_11 〖NO〗_4)〗_(n )are used to generate hybrid nanoparticles that block greater solar thermal radiation. This could improve radiative shielding by 25 to 30% compared to typical single-phase sunscreen formulas. Utilized MATLAB's bvp5c solver to find out that raising the Deborah number makes the velocity go up by 22 to 28%, but increasing the magnetic parameter makes it go down by 20 to 35% because of Lorentz damping. Nonlinear radiation raises the temperature field by 18 to 24%, and greater Schmidt numbers lower concentration levels by roughly 30%. Bioconvection research shows that the Peclet number increases the density gradients of microorganisms by 12 to 18%, which makes it easier for germs to migrate up. However, higher bioconvective Lewis numbers make motile-cell diffusion 10 to 15% lower. This demonstrates the sensitivity of mass diffusion to nanoparticle interactions. In fuzzy set theory (FST), the derived triangular fuzzy numbers (TFNs) are employed to represent uncertainty in the volume percentage of nanoparticles within the range [0, 0.05, 0.1]. The fuzzy velocity profiles that come out of these calculations reveal an 8 to 12% variance from the clear answer. The fuzzy midpoint has the highest flow rate. These findings indicate that uncertainty in nanoparticle loading significantly influences heat and momentum transfer.