An Analogue Program for Standing-Gradient Osmotic Flow

Diamond and Bossert (1) proposed a model of standing-gradient osmotic flow as a mechanism to explain the coupling of water and solute transport in epithelia. In their computations they used a digital program in F O R T R A N on an IBM 7094 to solve for the two dependent variables, C and v, where C was the osmolar concentration at any point, x along the channel and v was the linear velocity at t...

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Standing-Gradient Osmotic Flow

At the ultrastructural level, epithelia performing solute-linked water transport possess long, narrow channels open at one end and closed at the other, which may constitute the fluid transport route (e.g., lateral intercellular spaces, basal infoldings, intracellular canaliculi, and brush-border microvilli). Active solute transport into such folded structures would establish standing osmotic gr...

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Standing-Gradient Osmotic Flow A mechanism for coupling of water and solute transport in epithelia

At the ultrastructural level, epithelia performing solute-linked water transport possess long, narrow channels open at one end and closed at the other, which may constitute the fluid transport route (e.g., lateral intercellular spaces, basal infoldings, intracellular canaliculi, and brush-border microvilli). Active solute transport into such folded structures would establish standing osmotic gr...

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An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow

In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...

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An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow

In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...

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An Analogue Program for Standing-Gradient Osmotic Flow

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