Thermal convective and flow dynamics of blood trihybrid nanofluid inside a stenosis artery for cardiovascular disease treatment using artificial intelligence approach.
5 affiliations
- 1Department of Electrical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
- 2Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
- 3Department of Physical and Natural Sciences, University of The Gambia, Serekunda, Gambia. [email protected].
- 4Department of Computer Engineering, Biruni University, Istanbul, 34010, Turkey.
- 5Mathematics Research Center, Near East University, Nicosia, 99138, Cyprus.
Abstract
This article briefly discusses the influence of heat radiation and heat source on the convective flow of a blood-based trihybrid nanofluid inside a stenotic artery with chemical reaction. It can assist in investigating and predicting blood flow dynamics and heat transmission in constricted arteries for thermal therapy, targeted drug delivery, and cardiovascular disease treatment. By replicating the intricate interactions of many nanoparticles with blood, the model also aids in the design and optimization of biomedical devices like stents and artificial organs. The AI technique also makes it possible to make quick, precise, and patient-specific predictions, which helps doctors plan treatments and enhances the efficacy of cardiovascular procedures. The artificial intelligent neural network (AINN) is one sophisticated machine learning approach. The difficulty led to the system of PDEs (partial differential equations), which are transformed into ODEs (ordinary differential equations) via dimensionless similarity. The dataset was filtered using the Bvp4c method before being incorporated into the Bayesian-regularization algorithm (BRA). Regression-based statistical analysis, error histograms, absolute error, and MSE (mean squared error) are used to measure the determination, verification, convergence, and stability of Bayesian-regularized neural network mappings for this hybrid model. The thermal field increases as upsurge the values of thermal Biot number.Clinical trial: This study is not a clinical trial.
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BibTeX
@article{mrabet2026thermal,
title = {{Thermal convective and flow dynamics of blood trihybrid nanofluid inside a stenosis artery for cardiovascular disease treatment using artificial intelligence approach.}},
author = {Brahim Mrabet and Joffin J Ponnore and Rumla Jawo and Yakup Yildirim},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-05019-1},
url = {https://europepmc.org/article/MED/42909740},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Thermal convective and flow dynamics of blood trihybrid nanofluid inside a stenosis artery for cardiovascular disease treatment using artificial intelligence approach.
AU - Brahim Mrabet
AU - Joffin J Ponnore
AU - Rumla Jawo
AU - Yakup Yildirim
PY - 2026
DA - 2026/10/23
JO - Discover nano
DO - 10.1186/s11671-026-05019-1
UR - https://europepmc.org/article/MED/42909740
AB - This article briefly discusses the influence of heat radiation and heat source on the convective flow of a blood-based trihybrid nanofluid inside a stenotic artery with chemical reaction. It can assist in investigating and predicting blood flow dynamics and heat transmission in constricted arteries for thermal therapy, targeted drug delivery, and cardiovascular disease treatment. By replicating the intricate interactions of many nanoparticles with blood, the model also aids in the design and optimization of biomedical devices like stents and artificial organs. The AI technique also makes it possible to make quick, precise, and patient-specific predictions, which helps doctors plan treatments and enhances the efficacy of cardiovascular procedures. The artificial intelligent neural network (AINN) is one sophisticated machine learning approach. The difficulty led to the system of PDEs (partial differential equations), which are transformed into ODEs (ordinary differential equations) via dimensionless similarity. The dataset was filtered using the Bvp4c method before being incorporated into the Bayesian-regularization algorithm (BRA). Regression-based statistical analysis, error histograms, absolute error, and MSE (mean squared error) are used to measure the determination, verification, convergence, and stability of Bayesian-regularized neural network mappings for this hybrid model. The thermal field increases as upsurge the values of thermal Biot number.Clinical trial: This study is not a clinical trial.
SN - 2731-9229
ER -