Artificial neural network optimized by Bayesian-regularization algorithm to solve the convective flow of trihybrid nanofluid with LTNECs and thermal radiation

The aim of this study is to examine an artificial intelligence neural network based on the Intelligent Bayesian-Regularization optimizer to evaluate the effect of thermal radiation on the trihybrid nanofluid inside a stenotic artery with LTNECs (local thermal non-equilibrium conditions). The built n...

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Veröffentlicht in:Journal of Radiation Research and Applied Sciences Jg. 18; H. 4; S. 102046
Hauptverfasser: Abbas, Munawar, Medani, Mohamed, Elaissi, Samira, Adnan OTHMAN, Nashwan, Zayani, Hafedh Mahmoud, Shafique, Muhammad, Khan, Ilyas, Ben Khedher, Nidhal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2025
Schlagworte:
Activation energy
Convective conditions
Local thermal non-equilibrium conditions
Stenotic artery
Trihybrid nanofluid
Trihybrid nanofluid
Stenotic artery
Convective conditions
Activation energy
Local thermal non-equilibrium conditions
ISSN:1687-8507
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Zusammenfassung:The aim of this study is to examine an artificial intelligence neural network based on the Intelligent Bayesian-Regularization optimizer to evaluate the effect of thermal radiation on the trihybrid nanofluid inside a stenotic artery with LTNECs (local thermal non-equilibrium conditions). The built network used the log-sigmoid and radial basis activation functions. Artificial intelligence neural networks are trained on 80 % of the data, with the remaining 20 % being used for testing. Blood serves as both a base fluid and a trihybrid nanofluid that contains gold, silver, and titanium oxide nanoparticles. Simulating blood flow in damaged arteries can aid in the diagnosis and treatment of cardiovascular conditions. Additionally, this classic has the possible to enhance cancer treatments and targeted drug delivery, specifically hyperthermia therapy, and improve the design of biomedical devices that depend on heat and mass transmission in complex fluids. The appropriate similarity variable method has been used to convert PDEs into dimensionless ODEs. After that, the ODEs are solved both numerically and visually with MATLAB's built-in bvp4c solver. We chose the Bayesian-Regularization optimizer because it enhances generalization, decreases overfitting, and maintains consistent convergence even with sparse or noisy data, which is crucial for biological applications.
ISSN:1687-8507
DOI:10.1016/j.jrras.2025.102046