Ternary Hybrid Nanofluid Flow Emerging on a Symmetrically Stretching Sheet Optimization with Machine Learning Prediction Scheme

Nanofluids holding three distinct sorts of nanosized particles suspended in base fluid possess excellent thermal performance. In light this novel use coolant applications, the current work dealt with optimal design and performance estimation a ternary hybrid nanofluid, based on modern machine learning prediction technique. The synthesis (Cu), (TiO2), (SiO2) nanoparticles water over symmetrically stretching sheet was scrutinized. flow is most noteworthy symmetry analysis for momentum boundary layers, due to implications heat transfer, applied various industries technological fields. governing equations were transformed dimension-free series ODEs, by handling similarity transformable variables, after which, ODEs treated scientifically, help Wolfram Language tool. precision estimates assessed comparison existing research. Moreover, natures physical phenomena forecast designing support vector algorithm an emphasis learning, which delivers robust efficient structure every application that infers influences. To validate proposed research, some statistical metrics taken error assessment between true anticipated values. It revealed presented approach best strategy predicting quantities. This investigation established nanofluid possesses performance, greater than nanofluid. optimization process new beneficial viewpoint production polymer sheets, glass fiber, petroleum, plastic films, exchangers, electronic devices. Hence, obtained results are recommended development industrial devices setups.