semnan university PressJournal of Modeling in Engineering2008-4854113520140220FINITE ELEMENT SIMULATIONS OF HEAT TRANSFER IN FRICTION STIR WELDING OF AL 6061FINITE ELEMENT SIMULATIONS OF HEAT TRANSFER IN FRICTION STIR WELDING OF AL 606119165410.22075/jme.2017.1654FAAbbas Honarbakhsh RaoufsemnanEhsan GharibshahiyansemnanJournal Article20170128Friction stir welding (FSW) is a process in the solid state in which heat is generated due to friction between welding tool and work piece. FSW has extensive effect on the microstructure, weld quality, and mechanical properties. The purpose of this investigation is to study and to predict the heat generated in Aluminum alloy plates welded by FSW method. A three dimensional model was developed by LS-Dyna software, using finite element method. An appropriate heating cycle has been proposed for aluminum 6061 alloy. The investigated parameters in this study were linear velocity and rotational velocity. Finally, results from numerical and experimental data was compared and verified.Friction stir welding (FSW) is a process in the solid state in which heat is generated due to friction between welding tool and work piece. FSW has extensive effect on the microstructure, weld quality, and mechanical properties. The purpose of this investigation is to study and to predict the heat generated in Aluminum alloy plates welded by FSW method. A three dimensional model was developed by LS-Dyna software, using finite element method. An appropriate heating cycle has been proposed for aluminum 6061 alloy. The investigated parameters in this study were linear velocity and rotational velocity. Finally, results from numerical and experimental data was compared and verified.https://modelling.semnan.ac.ir/article_1654_2cdc3756f08485c24dc7e97be950f64a.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220A COMPARATIVE ANALYSIS OF ARTIFICIAL BEE COLONY AND PARTICLE SWARM OPTIMIZATION SWARM INTELLIGENCE ALGORITHMS IN DESIGN A FRACTIONAL FUZZY PID CONTROLLER AND IMPLEMENTATION ON DC MOTORA COMPARATIVE ANALYSIS OF ARTIFICIAL BEE COLONY AND PARTICLE SWARM OPTIMIZATION SWARM INTELLIGENCE ALGORITHMS IN DESIGN A FRACTIONAL FUZZY PID CONTROLLER AND IMPLEMENTATION ON DC MOTOR1123165510.22075/jme.2017.1655FARohollah MaghsoudiuniversityYaghoob HeidariuniversityBehzad Moshiritehran universityJournal Article20170128In this article, has been studied implementation of fractional fuzzy PID controller on a DC motor. A fractional fuzzy PID controller is a conventional PID controller that includes two non-integer derivative and integral parameters (Î»,Âµ) in addition to kp,ki,kd could be perform against uncertainties as fuzzy logic. Design strategy contains five parameters. This research uses from artificial bee colony and particle swarm optimization for designing of controller parameters. Artificial bee colony in designing proposed FFPID has improved system robustness considerably comparison with designed controller based on PSO and conventional PID.In this article, has been studied implementation of fractional fuzzy PID controller on a DC motor. A fractional fuzzy PID controller is a conventional PID controller that includes two non-integer derivative and integral parameters (Î»,Âµ) in addition to kp,ki,kd could be perform against uncertainties as fuzzy logic. Design strategy contains five parameters. This research uses from artificial bee colony and particle swarm optimization for designing of controller parameters. Artificial bee colony in designing proposed FFPID has improved system robustness considerably comparison with designed controller based on PSO and conventional PID.https://modelling.semnan.ac.ir/article_1655_8e3bb9e7cce3011e081eea25ac6658ab.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220MATHEMATICAL MODELING FOR RAPID EXPANSION OF SUPERCRITICAL SOLUTION TO PRODUCE NANO AND MICRO NABUTEMON PARTICLESMATHEMATICAL MODELING FOR RAPID EXPANSION OF SUPERCRITICAL SOLUTION TO PRODUCE NANO AND MICRO NABUTEMON PARTICLES2538165610.22075/jme.2017.1656FAHadi Baserisemnan universityLotfollahi Lotfollahisemnan universityJournal Article20170128A new model is proposed for production of fine particles of Nabutemon by rapid expansion of supercritical solutions (RESS). A mathematical model is used for prediction of particle size and particle size distribution of RESS produced particles. In this model the effect of various operating parameters such as pressure and temperature of extraction and temperature and pressure of expansion on the characteristics of products were studied. The calculation results showed good agreement between the calculation results and the experimental data for Nabutemon particles.A new model is proposed for production of fine particles of Nabutemon by rapid expansion of supercritical solutions (RESS). A mathematical model is used for prediction of particle size and particle size distribution of RESS produced particles. In this model the effect of various operating parameters such as pressure and temperature of extraction and temperature and pressure of expansion on the characteristics of products were studied. The calculation results showed good agreement between the calculation results and the experimental data for Nabutemon particles.https://modelling.semnan.ac.ir/article_1656_a0a66b4e093d68699fda36cc3c8c7d0a.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220ELECTRICAL CIRCUIT MODELLING WITH PETRI NET BY USING OF CONTROL ARCSELECTRICAL CIRCUIT MODELLING WITH PETRI NET BY USING OF CONTROL ARCS3947165710.22075/jme.2017.1657FAAbbas Didebansemnan university0000-0001-6790-9567Meghdad Sabouri Radsemnan universityJournal Article20170128The continuous Petri net is a model in which the number of marks in the places are real numbers instead of integers. This kind of Petri Nets is useful for modeling of systems that have one flux variable. Thus, changes in variables such as electrical current, water flow, electrical power flow, etc can be modeled by continuous Petri nets. Petri nets with the elements in it are not responsive to modeling of systems that are based on two or more variables (Such as electronic circuit). In this paper a new method based on continuous Petri nets for modeling and analysis of electrical circuits is introduced. In this approach, the new types of arcs, called control arcs, which control the speed of Petri nets transition, are presented. By adding these arcs, the possibility of linear systems modeling is provided.The continuous Petri net is a model in which the number of marks in the places are real numbers instead of integers. This kind of Petri Nets is useful for modeling of systems that have one flux variable. Thus, changes in variables such as electrical current, water flow, electrical power flow, etc can be modeled by continuous Petri nets. Petri nets with the elements in it are not responsive to modeling of systems that are based on two or more variables (Such as electronic circuit). In this paper a new method based on continuous Petri nets for modeling and analysis of electrical circuits is introduced. In this approach, the new types of arcs, called control arcs, which control the speed of Petri nets transition, are presented. By adding these arcs, the possibility of linear systems modeling is provided.https://modelling.semnan.ac.ir/article_1657_bea95a45a6e1005dab6111b828c89cc8.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKSAPPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS4962165810.22075/jme.2017.1658FAAli HeidariDepartment of Civil Engineering, University of Shahrekord, Shahrekord, IranDavoud TavakoliPh.D. Student, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Lavizan,
IranPouyan FakharianM.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran0000-0003-4307-1944Journal Article20170128The general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neural network. Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency is calculated using ANSYS program and is defined as a goal function for neural network so that all outputs of the network can be compared to this function and the error can be calculated. Then using a set of inputs including dimensions or specifications of plate, a neural network is made. After the determination of algorithm and quantities of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the results show the performance of the neural network and that the time of frequency calculation is considerably reduced.The general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neural network. Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency is calculated using ANSYS program and is defined as a goal function for neural network so that all outputs of the network can be compared to this function and the error can be calculated. Then using a set of inputs including dimensions or specifications of plate, a neural network is made. After the determination of algorithm and quantities of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the results show the performance of the neural network and that the time of frequency calculation is considerably reduced.https://modelling.semnan.ac.ir/article_1658_da59110e7e04fb41acaa7cc247faee84.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220A NOVEL MODEL FOR DECISIONâMAKING ON OPEC PRODUCTION LEVEL BASED ON OIL PRICE PREDICTION AND GAME THEORYA NOVEL MODEL FOR DECISIONâMAKING ON OPEC PRODUCTION LEVEL BASED ON OIL PRICE PREDICTION AND GAME THEORY6376165910.22075/jme.2017.1659FAEhsan LotfiuniversityHamidreza Navidishahed university0000-0003-1072-8786Journal Article20170128In this paper, a novel hybrid model based on neural network and Game Theory is proposed to support the analyzers in oil market. In this model, first the neural network is utilized to learn the oil prices associated with OPEC production level and USA imports level. Then the learned neural network is applied by a game model. Finally the Nash equilibrium points of the game present the optimum decision which can be decided by OPEC. In experimental studies, the proposed model is applied to determine the best decision at March 2012. According to the results, the model can be used for OPEC decision-making and oil prices prediction.In this paper, a novel hybrid model based on neural network and Game Theory is proposed to support the analyzers in oil market. In this model, first the neural network is utilized to learn the oil prices associated with OPEC production level and USA imports level. Then the learned neural network is applied by a game model. Finally the Nash equilibrium points of the game present the optimum decision which can be decided by OPEC. In experimental studies, the proposed model is applied to determine the best decision at March 2012. According to the results, the model can be used for OPEC decision-making and oil prices prediction.https://modelling.semnan.ac.ir/article_1659_d155ef3bca11932a1fd030f9bd3b7a10.pdfsemnan university PressJournal of Modeling in Engineering2008-4854113520140220THE INFLUENCE OF GRAVITY ON A MICROFLUIDIC MIXED CONVECTION BY APPLYING LATTICE BOLTZMANN METHODTHE INFLUENCE OF GRAVITY ON A MICROFLUIDIC MIXED CONVECTION BY APPLYING LATTICE BOLTZMANN METHOD7794166010.22075/jme.2017.1660FAArash KarimipouruniversityMohammad AkbariuniversityDavood ToghraieuniversityJournal Article20170128In this article the effects of gravity on the mixed convection of a microflow is studied numerically by using lattice Boltzmann method (LBM). To do this, the hydrodynamic boundary condition equations should also be modified. The cold fluid enters to the microchannel and leaves it after cooling its hot walls. Calculations are provided for a wide range of Knudsen number (Kn). The results are presented as the isotherms and streamlines, the values of slip velocity and temperature jump and the local and global profiles of velocity, temperature and Nusselt number. It is observed that LBM is able to simulate the mixed convection in a microchannel appropriately. It is claimed that the effects of buoyancy forces are important for Kn0.05 they can be ignored. Moreover, the buoyancy forces make a rotational cell in the microchannel flow which generates the negative slip velocity at Kn=0.005.In this article the effects of gravity on the mixed convection of a microflow is studied numerically by using lattice Boltzmann method (LBM). To do this, the hydrodynamic boundary condition equations should also be modified. The cold fluid enters to the microchannel and leaves it after cooling its hot walls. Calculations are provided for a wide range of Knudsen number (Kn). The results are presented as the isotherms and streamlines, the values of slip velocity and temperature jump and the local and global profiles of velocity, temperature and Nusselt number. It is observed that LBM is able to simulate the mixed convection in a microchannel appropriately. It is claimed that the effects of buoyancy forces are important for Kn0.05 they can be ignored. Moreover, the buoyancy forces make a rotational cell in the microchannel flow which generates the negative slip velocity at Kn=0.005.https://modelling.semnan.ac.ir/article_1660_cd2cc59a5719d3a2cd8854a35349ea49.pdf