Integrating artificial intelligence in investigating magneto-bioconvection flow of oxytactic microorganisms and nano-enhanced phase change material in H-type cavity
Scientists have integrated artificial intelligence techniques to numerically explore the magneto-bioconvection flow of nano-enhanced phase change materials in an H-type cavity. This research focuses on the suspension of nano-enhanced phase change materials and a host fluid containing oxytactic microorganisms, aiming to improve thermal characteristics and minimize energy consumption. The governing system was reduced to dimensionless form to analyze the impact of various parameters, including porosity, Darcy numbers, and Rayleigh numbers. Results indicate that increasing the cavity aspect ratio enhances bioconvection flow and phase change material behavior. Conversely, raising the Hartmann number from 10 to 100 resulted in a 1.67% decrease in average Nusselt numbers and a 0.247% increase in Sherwood numbers at a specific angle. Six different artificial neural network models were tested to estimate these critical parameters using an AI approach. The findings confirm that the developed networks can predict each parameter with high accuracy, offering a robust tool for future engineering and medical science applications involving bioconvection flows. The primary takeaway is that artificial neural networks successfully predicted critical fluid dynamics parameters with high accuracy in this study. This advancement holds significant potential for optimizing thermal systems where energy efficiency and precise flow control are essential. However, the reliance on numerical simulation suggests that physical validation remains necessary before widespread industrial adoption. Further research is required to determine how these findings translate to real-world engineering environments beyond the controlled H-type cavity model.
Published: June 3, 2026 at 01:35 PM
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Content
Scientists have integrated artificial intelligence techniques to numerically explore the magneto-bioconvection flow of nano-enhanced phase change materials in an H-type cavity. This research focuses on the suspension of nano-enhanced phase change materials and a host fluid containing oxytactic microorganisms, aiming to improve thermal characteristics and minimize energy consumption.
The governing system was reduced to dimensionless form to analyze the impact of various parameters, including porosity, Darcy numbers, and Rayleigh numbers. Results indicate that increasing the cavity aspect ratio enhances bioconvection flow and phase change material behavior. Conversely, raising the Hartmann number from 10 to 100 resulted in a 1.67% decrease in average Nusselt numbers and a 0.247% increase in Sherwood numbers at a specific angle.
Six different artificial neural network models were tested to estimate these critical parameters using an AI approach. The findings confirm that the developed networks can predict each parameter with high accuracy, offering a robust tool for future engineering and medical science applications involving bioconvection flows.
Key Insights
The primary takeaway is that artificial neural networks successfully predicted critical fluid dynamics parameters with high accuracy in this study.
This advancement holds significant potential for optimizing thermal systems where energy efficiency and precise flow control are essential.
However, the reliance on numerical simulation suggests that physical validation remains necessary before widespread industrial adoption.
Further research is required to determine how these findings translate to real-world engineering environments beyond the controlled H-type cavity model.
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