Neural Network Modeling of CuO/Au Hybrid Nanofluid Thermal Performance with Slip Effects for Advanced Process Applications

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 516 - 516

Published: Feb. 12, 2025

This study explores transient magnetohydrodynamic (MHD) heat and mass transfer in the flow of a hybrid nanofluid over stretching surface, considering both steady unsteady scenarios. The investigation incorporates chemical reactions, slip boundary conditions, effects thermal radiation. nanofluid, composed copper oxide (CuO) gold (Au) nanoparticles water-based fluid, demonstrates enhanced performance compared with base fluids. Key findings reveal that higher nanoparticle concentrations significantly improve transfer, highlighting potential nanofluids advanced management applications. Additionally, machine learning models effectively predict characteristics high accuracy (R2 = 0.99), showcasing their effectiveness complementing traditional numerical methods. These contribute to understanding complex systems highlight utility emerging computational tools for analysis.

Language: Английский

Machine learning-based predictive modeling and optimization: Artificial neural network-genetic algorithm vs. response surface methodology for black soldier fly (Hermetia illucens) farm waste fermentation.

Biochemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 109685 - 109685

Published: Feb. 1, 2025

Language: Английский