Novel Bayesian distributed adaptive neural structure for titanium and aluminium alloy nanofluidic model with gyrotactic microorganisms

Abstract

The purpose of this research study is to evaluate the solutions of Titanium and Aluminum alloy nanofluidic model with gyrotactic microorganisms (TAA-NFMGM) by applying stupendous knacks of Bayesian distributed adaptive neural structure (BDANS). The use of nanofluids containing gyrotactic microorganisms in combination with titanium and aluminum alloys have potential applications in diverse fields, including engineering, bioremediation, biomedical devices, biotechnology, and research depending on different factors like design of the system and use of certain microorganism. A dataset for BDANS is generated for the eight different events with Adam numerical technique for TAA-NFMGM by varying Prandtl number (Pr), mixed convection parameter (λ), microorganisms concentration parameter (Ω), bioconvection peclet number (Pe), and bioconvection Lewis number (Lb). The reference data set generated with Adam numerical technique is utilized for numerical calculation of different parameters on TAA-NFMGM by employing the artificial intelligence based BDANS. The accuracy and justification of the performance of BDANS is efficaciously substantiated through negligible level of MSE around 10–10 to 10–13, calculation of regression metrics and distribution of instances of error on histograms with values 10–04 to 10–09 near to reference line.