سامان محمدنبی

In this paper, a new model for estimation of the electrical conductivity of polymer carbon nanotube (CNT) nanocomposites based on the conventional power-law model and Halpin-Tsai formulation has been proposed. Halpin-Tsai model was originally presented to calculate the tensile modulus of composites, which can be modified for estimation of the electrical conductivity by replacing the electrical parameters. The nature of “b” exponent in power-law model is defined according to CNT dimensions, CNT electrical conductivity and the interphase thickness and also the impacts of these parameters on the “b” and the electrical conductivity of nanocomposite are taken into consideration. The developed model interprets that the electrical conductivity of polymer-CNT nanocomposite increases as the concentration, length and electrical conductivity of CNT and the interphase thickness increase. Furthermore, reduction in CNT diameter and waviness results in growth of nanocomposite electrical conductivity. In order to validate the developed model, nanocomposite samples with different volume fractions were produced by solid-state technique of the melt-blending method. The results of calculations and experimental procedure show good agreement.

As moisture condenses on the surface of the contaminated insulators, the electrical conductivity of the insulator surface increases, resulting in a leakage current on the surface and causing the insulators to fail. Condensation of moisture on the insulator surface occurs due to the mechanism of radiation cooling, mainly in the early mornings of winters. In this case, the temperature of the insulator surface is lower than the dew point. In this paper, a sample of real fine dust collected from the dust center and chemical analysis and measurement of electrical conductivity in the wet state were performed. By simulating a 230-kV contaminated composite insulator in COMSOL Multiphysics software, the magnitude of leakage current was obtained and verified by an experimental test. Also, using heat transfer governing relations, the temperature of the insulator surface during the leakage current flow, was determined and compared with the images captured by the thermo-vision camera during the test. The results show that the leakage current and the surface temperature of the insulator increase with increasing the conduction and thickness of the contamination layer and consequently the probability of insulator failure also increases. Experimental testing shows that simulation of insulators with finite elements-based commercial software can be a reliable method for pre-analysis of insulators.

In this research the optimization of piezoelectric fibers in a functionally graded cylindrical panel with PFRC layers as sensor and actuator under dynamic load and electrical excitation with various types of supports including: simple, clamped and combination of free and clamped supports is provided. The main goal is to obtain the volume fraction of piezoelectric fibers in a PFRC layer so that the radial displacement of this layer across the circumferential direction is equal to that of piezoelectric layer. For the optimization, Genetic algorithms were used and in each case, the algorithm parameters are obtained by using Parameter Tuning method. In order to saving time and reducing the use of memory, Artificial Neural Networks are trained and employed. In the end, the results for the stresses and displacements of the panel with piezoelectric layers and the one with PFRC layers are presented and compared. The effect of support conditions and on the optimization process and the obtained volume fractions are examined. Results show that with the introduction of clamped supports, increment for maximum Von mises stress in the structure and the volume fraction of piezoelectric fibers can be seen.