Applied Mathematics and Mechanics >
Physics-informed neural network approach to analyze the onset of oscillatory and stationary convections in chemically triggered Navier-Stokes-Voigt fluid layer heated and salted from below
Received date: 2025-03-20
Revised date: 2025-09-19
Online published: 2025-10-29
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The present work analyzes the linear and weakly nonlinear stability of double-diffusive convection (DDC) in a Navier-Stokes-Voigt (NSV) fluid, considering a chemical reaction and an internal heat source. The lower fluid layer is salted and heated. The quiescent state and dimensionless variables yield dimensionless parameters for the governing partial differential equations (PDEs). A two-dimensional scenario is investigated using the stream function. Stationary and oscillatory convection can be analyzed using the linear approach. The nonlinear equations are numerically solved using the Runge-Kutta Fehlberg (RKF-45) technique. Additionally, the physics-informed neural network (PINN) validates the mathematical outcomes. The Kelvin-Voigt parameter and the Prandtl number do not affect stationary convection. Thhe neutral stability diagrams show that the ratios of diffusivity, solute Rayleigh, and Kelvin-Voigt parameters stabilize oscillatory convection. However, internal heat and chemical reactions cause instability. The Kelvin-Voigt, internal heat, and chemical reaction parameters increase mass and heat transfer (MHT), while the solute Rayleigh number and the ratio of diffusivity decrease MHT.
B. S. SANJU , R. NAVEEN KUMAR , R. S. VARUN KUMAR , A. ABDULRAHMAN . Physics-informed neural network approach to analyze the onset of oscillatory and stationary convections in chemically triggered Navier-Stokes-Voigt fluid layer heated and salted from below[J]. Applied Mathematics and Mechanics, 2025 , 46(11) : 2199 -2220 . DOI: 10.1007/s10483-025-3316-7
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