Random Flow Model Research Articles

Assessment of the best flow model to characterize diffuse correlation spectroscopy data acquired directly on the brain.

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique capable of monitoring tissue perfusion. The normalized temporal intensity autocorrelation function generated by DCS is typically characterized by assuming that the movement of erythrocytes can be modeled as a Brownian diffusion-like process instead of by the expected random flow model. Recently, a hybrid model, referred to as the hydrodynamic diffusion model, was proposed, which combines the random and Brownian flow models. The purpose of this study was to investigate the best model to describe autocorrelation functions acquired directly on the brain in order to avoid confounding effects of extracerebral tissues. Data were acquired from 11 pigs during normocapnia and hypocapnia, and flow changes were verified by computed tomography perfusion (CTP). The hydrodynamic diffusion model was found to provide the best fit to the autocorrelation functions; however, no significant difference for relative flow changes measured by the Brownian and hydrodynamic diffusion models was observed.

Design and Implementation of Drainage Network Extraction Algorithm Based on Binary Linear Regression

The flow directions of the extracted drainage network are more random based on random flow model. Binary linear regression method is applied to calculate the residual value at every point, and then the normalized residuals are used to prune reasonably as the variables. The terrains are distinguished automatically according to the normalized residuals, and the local catchment area threshold is determined based on each terrain feature. When there are two terrains in the experimental area, source density is large on mountainous terrain, and it is small on flat terrain. Eventually, the extracted result is consistent with the actual drainage network.

Application of the load flow and random flow models for the analysis of power transmission networks

In this paper, the classical load flow model and the random flow model are considered for analyzing the performance of power transmission networks. The analysis concerns both the system performance and the importance of the different system elements; this latter is computed by power flow and random walk betweenness centrality measures. A network system from the literature is analyzed, representing a simple electrical power transmission network. The results obtained highlight the differences between the LF “global approach” to flow dispatch and the RF local approach of randomized node-to-node load transfer. Furthermore, computationally the LF model is less consuming than the RF model but problems of convergence may arise in the LF calculation.

Parametrization for mesoscale ocean transport through random flow models

AbstractWe describe a mathematical approach based on homogenization theory toward representing the effects of mesoscale coherent structures on large‐scale transport in the ocean. We demonstrate the approach on a deterministic and a random model flow. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Stochastic model of flow through stratified soils : J Geotech Engng Div ASCE, V106, NGT6, June 1980, P593–610

The flow through Stratified soils has been analyzed using probability theory. The stratification is represented by a stochastic model composed of two Markov chains. Studies were made on two varved clays and a glacial outwash deposit. Seepage in stratified soils is analyzed by a random flow model. The stochastic model for soil stratification is incorporated in the flow model. Simulation techniques were used to obtain the statistical properties of the permeability of the varved clay and outwash deposits. The mean, variance and autocorrelation functions of permeability were computed and compared with experimental results from a varved clay.

Stochastic Model of Flow Through Stratified Soils

The flow through Stratified soils has been analyzed using probability theory. The stratification is represented by a stochastic model composed of two Markov chains. Studies were made on two varved clays and a glacial outwash deposit. Seepage in stratified soils is analyzed by a random flow model. The stochastic model for soil stratification is incorporated in the flow model. Simulation techniques were used to obtain the statistical properties of the permeability of the varved clay and outwash deposits. The mean, variance and autocorrelation functions of permeability were computed and compared with experimental results from a varved clay.

An investigation of the distribution of fluid velocities in fibrous filters

A substantial fraction of the total flow through commercial fibrous filters passes through areas with few fibres. This results in differences in velocity within the filter bed and values of average flow resistance substantially lower than that predicted from theories based on model filters. The experimental results presented are compatible with a simple random flow model.