Derives a corrected boundary condition that enforces exact total surfactant mass conservation in nonlinear reduced models of soluble-surfactant-laden falling films, resolving an inconsistency in prior surface transport reductions.
Numerical Heat Transfer, Part A: Applications , volume =
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A physics-informed neural network merges sparse LBM data with Navier-Stokes equations to predict unsteady flows in fractal-rough microchannels at 150-200 times lower data cost.
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A conservation-consistent boundary condition for nonlinear models of soluble-surfactant-laden falling films
Derives a corrected boundary condition that enforces exact total surfactant mass conservation in nonlinear reduced models of soluble-surfactant-laden falling films, resolving an inconsistency in prior surface transport reductions.
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Amalgamation of Physics-Informed Neural Network and LBM for the Prediction of Unsteady Fluid Flows in Fractal-Rough Microchannels
A physics-informed neural network merges sparse LBM data with Navier-Stokes equations to predict unsteady flows in fractal-rough microchannels at 150-200 times lower data cost.