Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

A Novel No-Equilibrium Chaotic System with Multiwing Butterfly Attractors

Abstract: Discovering unknown features of no-equilibrium systems with hidden strange attractors is an attractive research topic. This paper presents a novel no-equilibrium chaotic system that is constructed by using a state feedback controller. Interestingly, the new system can exhibit multiwing butterfly attractors. Moreover, a new chaotic system with an infinite number of equilibrium points, which can generate multiscroll attractors, is also proposed by applying the introduced methodology.

An expert system for predicting longitudinal dispersion coefficient in natural streams by using ANFIS

Abstract: Longitudinal dispersion coefficient in rivers and natural streams usually is estimated by simple inaccurate empirical relations, because of the complexity of the phenomena. So, in this study using adaptive neuro-fuzzy inference system (ANFIS), which have the ability of perception and realization of phenomenon without need for mathematical governing equations, a new flexible tool is developed to predict the longitudinal dispersion coefficient. The process of training and testing of this new model is done using a set of available published filed data. Several statistical and graphical criterions are used to check the accuracy of the model. The dispersion coefficient values predicted by the ANFIS model satisfactorily compared with the measured data. The predicted values were also compared with those predicted using available empirical equations that have been suggested in the literature and it was found that the ANFIS model with R2=0.99 and RMSE=15.18 in training stage and R2=0.91 and RMSE=187.8 in testing stage is superior in predicting the dispersion coefficient than the best accurate empirical equation with R2=0.48 and RMSE=295.7. The presented methodology in this paper is a new approach in estimating dispersion coefficient in streams and can be combined with mathematical models of pollutant transfer or real-time updating of these models.

Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network

Abstract: Curve fitting and neural network modeling are suitable methods for modeling the complex relationship between various parameters in engineering problems In this study, at the first, a curved fitting was performed on experimental data related to nano-antifreeze containing carbon nanotubes, which led to the presentation of a two-variable correlation to predict its thermal conductivity After that, an artificial neural network was designed to evaluation of the effects of temperature and solid volume fraction on the thermal conductivity of nano-antifreeze For modeling, the volume fraction and temperature were applied as input variables By selecting 9 neurons for the hidden layer, the output of the neural network, which was thermal conductivity ratio, was obtained The results showed that the proposed equation has good accuracy for engineering applications However, comparative results showed that the neural network has a more accurate prediction than curve fitting for the thermal conductivity of the antifreeze containing multi walled carbon nanotubes (MWCNTs)