Enhancing thermal transport in chemically reacting nanoparticles using the energy source and Cattaneo-Christov heat flux model

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Developing an effective heat exchange solvent remains one of the biggest hurdles facing industries today, as conventional fluids are unsatisfactory for effective heating and cooling. The purpose of this work is to analyze the heat transmission and flow behaviors of hybrid nanofluids based on Single-Walled Carbon Nanotubes and Multi-Walled Carbon Nanotubes in the context of thermal radiation on a porous surface and the Cattaneo–Christov heat flux model with Thomson and Troian boundary conditions. This investigation utilizes a unique computational framework that combines Morlet Wavelet Neural Networks with Hybrid Cuckoo Search Algorithm. This advanced stochastic computational framework can effectively handle various nonlinear models and produce accurate results. This scheme’s accurate and consistent convergence is established by analyzing its findings with a numerical approach, and statistical metrics for performance are utilized to validate it further. The recommended method exhibits exceptional accuracy and precision, showcasing the hybrid nanofluid’s remarkable heat transmission attributes and thermal conductivity. The Mean squared error values range from 10−01 to 10−05. The Fitness values fall within the interval 100-10−06, whereas the range of Error in Nash Sutcliffe efficiency lies between 10−02 and 10−−08. The important and intriguing feature of this remarkable work is that, for all parameters examined, the heat transfer rate rises with minimal measurement of errors, consistent with the core objective of applying nanofluids to nanotechnology for their prospective implications.