Prof. Dr. abdolreza MEMARI

University: islamic azad university mahshahr branch
Department: Department of petroleum
Research Fields: petroleume engineering

Publications

1 Buckling Analysis of a Five-walled CNT with Nonlocal Theory

Authors: abdolreza MEMARI, Alireza Bozorgian, Navid Majdi Nasab

Abstract:

A continuum model is presented to study vdW interaction on buckling analysis of multi-walled walled carbon nanotube. In previous studies, only the vdW interaction between adjacent two layers was considered and the vdW interaction between the other two layers was neglected. The results show that the vdW interaction cofficients are dependent on the change of interlayer spacing and the radii of tubes. With increase of radii the vdW coefficients approach a constant value. The numerical results show that the effect of vdW interaction on the critical strain for a doublewalled CNT is negligible when the radius is large enough for the both the cases of before and after buckling.

Keywords: buckling, Carbon Nanotube, multi-walled carbon nanotube, van der Waals interaction, Critical Strain, Prebuckling Pressure

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Abstracts

1 Prediction Fluid Properties of Iranian Oil Field with Using of Radial Based Neural Network

Authors: abdolreza MEMARI

Abstract:

In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: Neural Network, viscosity, Iranian crude oil, radial based, roller ball method, KHAN model

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