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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Numerical study of interacting vapor bubble trains in microchannel flow boiling finds frequency rises and growth falls with higher vapor-liquid volume ratio, while growth rises with heat flux or lower latent heat, plus upstream vaporization drives downstream expansion and periodic fluctuations in wa
Simulations demonstrate that interactions among multiple vapor bubbles in microchannels lead to smaller leading bubbles due to heat uptake by rear bubbles, modulated by volume ratio, Reynolds number, and wall thickness.
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.
citing papers explorer
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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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Dynamics of vapor bubble train in flow boiling heat transfer in microchannels
Numerical study of interacting vapor bubble trains in microchannel flow boiling finds frequency rises and growth falls with higher vapor-liquid volume ratio, while growth rises with heat flux or lower latent heat, plus upstream vaporization drives downstream expansion and periodic fluctuations in wa
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Interaction between vapor bubbles during flow boiling heat transfer in microchannels
Simulations demonstrate that interactions among multiple vapor bubbles in microchannels lead to smaller leading bubbles due to heat uptake by rear bubbles, modulated by volume ratio, Reynolds number, and wall thickness.
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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.