Usage of computational method for hemodynamic analysis of intracranial aneurysm rupture risk in different geometrical aspects.

The importance of the parent vessel geometrical feature on the risk of cerebral aneurysm rupture is unavoidable. This study presents inclusive details on the hemodynamics of Internal carotid artery (ICA) aneurysms with different parent vessel mean diameters. Different aspects of blood hemodynamics are compared to find a reasonable connection between parent vessel mean diameter and significant hemodynamic factors of wall shear stress (WSS), oscillatory shear index (OSI), and pressure distribution. To access hemodynamic data, computational fluid dynamics is used to model the blood stream inside the cerebral aneurysms. A hemodynamic comparison of the selected cerebral aneurysm shows that the minimum WSS is reduced by about 71% as the parent vessel's mean diameter is increased from 3.18 to 4.48 mm.

Influence of parent vessel feature on the risk of internal carotid artery aneurysm rupture via computational method.

In this study, the role of sac section area and parent vessel diameter on the hemodynamic feature of the blood flow in selected internal carotid artery (ICA) aneurysms is comprehensively investigated. The changes of wall shear stress, pressure, and oscillatory shear index (OSI) of blood stream on the vessel for various aneurysms with coiling treatment. To attain hemodynamic factors, computational technique is used for the modeling of non-Newtonian transient blood flow inside the three different ICA aneurysms. Three different saccular models with various Parent vessel mean Diameter is investigated in this study. The achieved outcomes show that increasing the diameter of the parent vessel directly decreases the OSI value on the sac surface. In addition, the mean wall shear stress decreases with the increase of the parent vessel diameter.

Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks.

Separating carbon dioxide (CO2) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO2 capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO2 separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO2 capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO2/CH4 selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO2/CH4 selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO2/CH4 selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite.

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO2) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO2 permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO2 separation.