Akademik Veri Yönetim Sistemi
Computers in Biology and Medicine, cilt.207, 2026 (SCI-Expanded, Scopus)
This work introduces a machine learning enhanced finite element model for the study of blood flow in a stretching artery with the presence of sulfonated polystyrene nanoparticles (NSPS). The analysis deals with the coupling of the blood dynamics with nanoscale additives, which show significant promise for biomedical applications, such as drug delivery, oncological therapy, and targeted treatment based on magnetohydrodynamic mechanisms. The influence of nondimensional parameters (magnetic field strength, porosity, chemical reaction rate, and viscous dissipation) is studied to validate the usefulness of NSPS particles in clinical applications. Governing equations are solved by the finite element method, and numerical simulations are performed in the Python programming language to determine velocity, temperature, and concentration field. A multilayer perceptron regression surrogate is then trained on the finite element data and gives a predictive accuracy of 97.3% . on the numerical reference. This hybrid FEM-ML-based framework provides a fast computational tool for parameter space exploration, which helps to reduce the dependency on repeated, computationally expensive, finite element simulations. Numerical experimentation proves that the improvement of the magnetic parameter results in an approximate 18% . reduction in axial velocity, while improved porosity increases the drug penetration depth by approximately 11% . For hyperthermia-based treatments, localized heating could be used to release the drug from NSPS, whereas in pH-sensitive scenarios, the acidic tumor environment triggers automatic drug release. This approach ensures high localized action of the drugs, with the least amount of side effects on the healthy tissues.