Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Physics-informed neural network and numerical analysis of Maxwell nanofluid flow over a stretching surface with induced magnetic field and variable properties. |
| Συγγραφείς: |
Ali, Mohsin, Yaseen, Lubaba, Abbas, Nadeem, Hassan, Ahmad, Shatanawi, Wasfi |
| Πηγή: |
Canadian Journal of Physics; 7/8/2026, Vol. 104, p1-12, 12p |
| Θεματικοί όροι: |
Numerical analysis, Artificial neural networks, Viscosity, Surfaces (Physics), Magnetic flux, Fluid flow, Numerical solutions to boundary value problems, Thermal conductivity |
| Περίληψη: |
The present analysis investigated the stagnation dynamic of Maxwell fluid over a stretching surface, including the characteristics of variable thermal conductivity, variable viscosity, and induced magnetic field. The flow is examined under both prescribed surface temperature and concentration and prescribed surface heat and mass flux conditions. The study takes into account a system of highly nonlinear partial differential equations governing the behavior of considered flow. The inspection of these equations requires conversion into ordinary differential equations via suitable similarity transformations. These complex equations are solved by applying the bvp4c scheme in MATLAB, and a neural network-based analysis is implemented to enhance the prediction and generalization of key physical quantities. Results for Nusselt number and Sherwood number are analyzed concerning pertinent flow parameters. Profiles of velocity, temperature, and concentration are plotted. The numerical comparisons confirmed excellent agreement with previously published work validating the model. The study reveals that increasing Deborah number leads to a notable reduction in the fluid velocity near the surface. The integrated neural network framework provides additional insight into the nonlinear dynamics of Maxwell fluid flow under an induced magnetic field. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |