Stochastic-based deep neural network for analysis of bioconvection flow and thermal Radiation: Applications of nanofluid in industrial cooling systems
Munawar Abbas, Bouthaina Dammak, Hafedh Mahmoud Zayani, Adnan Burhan Rajab, Sami Dhahbi, Ainul Akmar Mokhtar, Hilmi Hussin, Nidhal Ben Khedher
Abstract
The suggested model is used in biomedical engineering, industrial cooling, and environmental sciences. Developing tailored treatments and efficient drug delivery systems is made possible by biomedical researchers' comprehension of bioconvection in oxytactic bacterial nanofluid. Additionally, this knowledge enhances the efficacy of heat transmission in industrial processes through the integration of microfluidic devices with cooling systems. This study developed a unique deep neural network to inspect the significance of heat radiation and local thermal non-equilibrium circumstances on the flow of a nanofluid over a sheet via thermo-bioconvection in the occurrence of oxytactic microbes. In this work, a simple mathematical model is used to analyse heat transmission features without local thermal equilibrium constraints. The conventional local thermal non-equilibrium effect method produces two distinct fundamental thermal gradients for both liquid and solid phases. The accurateness of the generated approach is verified by comparing it with the reference solution obtained from the bvp4c solver. As the activation energy parameter increases, the concentration profile improves.