Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Numerical simulation of MHD Carreau bio-magnetic dusty hybrid nano fluid flow over a rotating disk: machine learning approach. |
| Συγγραφείς: |
Karuppiah, Santhi, Govindaram, Anitha, Reddy Reddisekhar Reddy, Seethi, Jayapal, Jegan |
| Πηγή: |
International Journal of Computer Mathematics; Aug2026, Vol. 103 Issue 8, p1583-1611, 29p |
| Θεματικοί όροι: |
Nanofluids, Fluid flow, Fluid dynamics, Numerical solutions to boundary value problems, Heat flux, Heat transfer coefficient, Rotating disks, Computational fluid dynamics |
| Περίληψη: |
Machine learning is an effective method for addressing complicated fluid dynamics issues and evaluating enormous data sets. This work investigates the thermophysical behaviour of Ag-Au/blood hybrid nanofluids on a spinning disk utilizing a two-phase MHD Carreau model and a non-Fourier heat flux technique. The nonlinear governing equations for momentum, temperature, skin friction, and Nusselt number were numerically solved with the MATLAB bvp4c function. Tables and graphs show how important factors affect biomagnetic flow. Temperature increases with increased nanoparticle concentration and radiation, but velocity decreases with increasing porosity and magnetic field intensity. Under favourable conditions, the interaction between dust and fluid improves heat transmission by increasing skin friction and Nusselt number. Radiation increases the Nusselt number by around 19%, while particle contact decreases it by almost 12%. Machine learning integration enables predictive modelling and optimization of biomedical systems such as artificial organs, lab-on-a-chip devices, and medication delivery. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |