Abstract: Purpose - This paper aims to deal with the electromagnetic simulation of a microwave discharge excited by a surface wave in a large diameter (12 cm) cylindrical plasma reactor. It seeks to focus both on the optimization of the power coupling in the discharge and on the discharge homogeneity. Design/methodology/approach - The CST microwave studio 3D commercial code was used, which solves Maxwell equations using the finite integration technique. The power coupling is investigated by studying the influence of a short-circuit position, whereas the discharge homogeneity is investigated by studying the influence of the discharge diameter. Findings - A short-circuit position was found for which the power coupling is perfectly optimised (reflected power around 1 per cent), and it is shown that the 12 cm diameter cylindrical reactor is multi-mode at 2.45 GHz, with a dominant m=3 hexapolar mode. Research limitations/implications - The electromagnetic modelling of this reactor is a first step; now the plasma has to be taken into account. Research is in progress to develop a 2D fluid model of the plasma. Practical implications - The electromagnetic simulation of a plasma reactor turns out to be very useful for the optimization in terms of energy coupling and spatial homogeneity prediction. Originality/value - The results and a similar approach can be used for the conception of new plasma reactors. 38;copy; Emerald Group Publishing Limited.

Abstract: In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network, namely multilayer perceptron (MLP) neural network and finite element method (FEM) to solve the inverse problem of defect identification. The approach is used to identify unknown defects in metallic walls. The methodology used in this study consists in the simulation of a large number of defects in a metallic wall, using the finite element method. Both variations in with and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of MLP neural network model. Finally, the obtained neural network is used to identify a group of new defects, simulated by the finite element method, but not belonging to the original dataset. Noisy data, added to the probe measurements is used to enhance the robustness of the method. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Abstract: This paper presents an approach which is based on the use of radial basis function (RBF) neural network and finite element analysis to solve the inverse problem of defect identification. The approach is used to identify unknown defects in metallic walls. The methodology used in this study consists in the simulation of a large number of defects in a metallic wall, using the finite element method (FEM). Both variations in with and height of the defects are considered Then the obtained results are used to generate a set of vectors for the training of a RBF neural network. Finally, the obtained neural networks are used to identify a group of new defects, simulated by the FEM, bill not belonging to the original dataset. Performance of the RBF network was also compared with the most commonly used multilayer perceptron (MLP) network model. The reached results demonstrate the efficiency of the proposed approach, and that RBF network performs better than MLP network model. Copyright (c) 2007 Praise Worthy Prize S.r.l. All rights reserved.

Abstract: In this paper, a parameter identification using inverse problem methodology is proposed The magnetic permeability, which depends on the magnetic field and temperature, is a physical parameter which has to be taken into account in any electro thermal physical problem simulation. In order to shou, the validity of the proposed approach, the problem is usually treated as an optimization problem, where the conjugate gradient method is combined with the finite element analysis, to identify the relative magnetic permeability of the permanent magnets (PM) of a synchronous motor. Tikhonovâ€™s regularization method is then used to replace the original ill-posed or ill-conditioned problem with a well-posed or well-conditioned problem, able to provide a close approximation of the PM relative magnetic permeability. Copyright C 2007 Praise Worthy Prize S. r. l. - All rights reserved.

Abstract: In this article, an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely radial basis function (RBF) neural network and finite element method (FEM) to solve the inverse problem of parameter identification. The methodology used in this study consists in the simulation of a large number of variations of magnetic relative permeability and electric conductivity in a material under test by FEM. Then the obtained results are used to generate a set of vectors for the training of a RBF neural network. Finally, the obtained neural network is used to identify a electromagnetic parameters of a group of new materials that not belonging to the original dataset. Performance of the RBF neural network was also compared with the most commonly used multilayer perceptron network model, and the results show that RBF network performs better than multilayer perceptron network model.

Abstract: Purpose - The paper aims to estimate the thermal impact of temperature dependency of material characteristics on induction machines, for which a coupled electromagnetic thermal analysis is carried out. Design/methodology/ approach - Both electromagnetic and thermal fields are calculated using a weak coupled finite element analysis algorithm. The electromagnetic behavior of the induction motor is obtained by coupling the field equations to the voltage equations of the windings. The nonlinearity due to the saturation of the iron core and the temperature dependency of the electrical conductivity are taken into account. When the heat sources are evaluated the temperature distribution in the induction motor is obtained. In order to improve the accuracy of the formulation, thermal contact resistances, external and internal convection are considered. Findings - The results presented in this paper prove that the temperature dependency of electric material characteristics must be considered, to accurately simulate the behavior of the induction motors during the design stage. Originality/value - The presented field-circuit coupling completes the two-dimensional finite element analysis by introducing the possibility to take into account the three-dimensional part of the motor (R 60;sub 62;tte 60;/sub 62;, L 60;sub 62;tte 60;/sub 62;). Another advantage is the ability to include voltage sources. Consequently, a realistic approach for the electromagnetic and thermal behavior of the electrical machine is achieved. 38;copy; Emerald Group Publishing Limited.

Notes: Field testing;Circuit elements;Field-circuit coupling;Electric materials;

Abstract: Purpose - The modelling of the electromagnetic devices with moving objects such as launchers, electrical machines and actuators, necessitates considering the motion. This paper aims to examine this subject. Design/methodology/approach - This task can be performed by introducing the velocity term in the electromagnetic equation. However, the application of this method leads to a non-symmetrical finite element matrix. This numerical problem can be avoided either by the finite element meshing domain every displacement step or by using special techniques coupled to the finite element method like the moving bound, sliding surface and macro element (ME). The ME solution, based on an analytical model in the air-gap of the devices, is more solicited for its low cost and accuracy by comparison with the other one. This technique keeps unchanged the finite element topology during the simulation, where the motion is taken into account by modification of the ME formula’s every displacement. Findings - This paper sought to present a new formula of the ME which is called dynamic ME. This new formula keeps unchanged the finite element topology and the terms of the analytical stiffness matrix too during the movement simulation. Research limitations/implications - The developed model is limited to analyzing the 2D devices with moving objects in linear or non-linear case with saturation of the magnetic circuits. Extending the model to consider the 3D effects is the perspective of this work. Originality/value - The developed formula is more economical than the classical one

Notes: fast dynamic macro element;finite element 2D coupled model;electromagnetic launcher study;electromagnetic device modelling;nonsymmetrical finite element matrix;finite element meshing domain;moving bound;sliding surface;macro element;electromagnetic fields;

Abstract: An axially symmetric model for the calculation of eddy currents in 3D in inductively coupled rf plasma devices with metal cooling systems is presented. This model is combining an analytical approach of the induced currents in the metallic parts of the cooling system with a finite element analysis previously elaborated in the case of quartz confinement tube. Effect of temperature and motion on the electric conductivity is taken into account. The model is based on finite element method.

Notes: Inductively coupled radio frequency plasma torches;Metal cooling systems;

Abstract: The proposed work presents an economic and accurate model using a 2D combined analytical and Finite Element (FEM) solutions, to compute the eddy current, in the different parts of a 3D induction plasma device (Fig. 1). In such a device, 3 MHz frequency, the main problem is the representation of the skin effect characterizing the excitation coil and the metallic part of the plasma device cooling system. In some existing eddy current models, the thin skin depth is generally represented when using boundary element method and coupling it to the FEM to discretize the nonlinear domains. The resolution of the obtained algebraic system requires a large computer memory and a long CPU time. The characteristics of the proposed model are as follows: 1. using an x-y analytical solution in the cooling conducting regions (Fig. 2); 2. determining the Neumann boundary conditions, for the domains in which the FEM is required, from the solution obtained in 1; 3. deducing an equivalent electric conductivity that permits one to represent the 3D plasma device as an axisymmetrical system; 4. representing the excitation coil by a line conducting region; 5. finite element discretizing the plasma domain, in which the electric conductivity is strongly dependent on temperature.1 The full paper will present the details of the coupled analytical and numerical formulations and results for the proposed model validity

Abstract: A finite element analysis for the electromagnetic field in an rf plasma system with quartz confinement tube is presented. Effect of motion and heat on plasma electrical conductivity is taken into account by coupling heat, flow and electromagnetic equations within an iterative method.

Abstract: This paper describes a new methodology for using artificial neural networks (ANN) and finite element method (FEM) in an electromagnetic inverse problem (IP) of parameters identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the FEM. Both variations in relative magnetic permeability and electric conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of generalized radial basis function neural networks (RBFNN). Finally, the obtained neural network (NN) is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. The reached results demonstrate the efficiency of the proposed approach.

Abstract: In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network (FFNN) with one hidden layer, namely radial basis function (RBF) neural network and finite element method (FEM) to solve the electromagnetic inverse problem of parameter identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the FEM. Both variations in relative magnetic permeability and electrical conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of RBF neural network. Finally, the obtained neural network (NN) is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. Performance of the RBF network was also compared with the most commonly used multilayer perceptron (MLP) network model. The obtained results show that RBF network performs better than MLP network model. 38;copy;2007 IEEE.

Notes: RBF networks;International symposium;Parameter identifications;Material-under-test;Network modelling;Electromagnetic inverse problems;Electrical conductivity;RBF neural network;Neural network (NN);Finite element method FEM;Relative magnetic permeability;Radial basis function neural networks;Multi-Layer Perceptron;Data sets;New materials;Unknown parameters;Hidden layers;Radial basis function neural network;

Abstract: To calculate magnetic global force on bodies several approaches can be used with the finite element method (FEM). They are based on Maxwell’s stress tensor, or on the Lorentz formula or on the virtual work principle. The accuracy of the results for a given mesh is an important criterion for choosing between them. Description and comparison of these methods applied to the calculation of magnetic global forces on current carrying conductor is done.

Notes: electromagnetic forces;magnetic global force;finite element method;Maxwell stress tensor;Lorentz formula;virtual work principle;current carrying conductor;

Abstract: This paper presents an approach which is based on the use of artificial neural networks and finite element analysis to solve the inverse problem of defect identification. The approach is used to identify unknown defects in metallic walls. The methodology used in this study consists in the simulation of a large number of defects in a metallic wall, using the finite element method. Both variations in with and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of two neural network models: multilayer perceptron neural network (MLP) and radial basis functions (RBF). Finally, the obtained neural networks are used to classify a group of new defects, simulated by the finite element method, but not belonging to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject

Abstract: Modelling of the electromagnetic phenomena that occur in rf plasmas requires one to take into account the flow and temperature dependency of the electric conductivity of the plasma. The descriptive equations of the system are strongly coupled and non-linear. We previously proposed a finite element axially symmetric model for plasma devices with a quartz confinement tube (Mekideche and Feliachi, 1993). When the confinement is ensured by a segmented water-cooled metal tube two main difficulties appear: (1) the system is not axially symmetric and (2) there is a strong skin effect for the eddy currents in the confinement tube. In this work we present a quasitridimensional model of such a plasma device, combining an analytical method developed by Muhlbauer et al. (1991) for furnaces with cold crucible and finite element modelling. An induction plasma torch is mentioned

Notes: finite element eddy currents analysis;rf plasma devices;metal cooling system;electromagnetic phenomena;induction plasma torch;flow;temperature;electric conductivity;coupled nonlinear equations;finite element axially symmetric model;confinement;segmented water-cooled metal tube;skin effect;eddy currents;quasitridimensional model;

Abstract: Summary form only given. The proposed work presents an economic and accurate model using a 2D combined analytical and finite element (FEM) solutions, to compute the eddy current, in the different parts of a 3D induction plasma device. In such a device, 3 MHz frequency, the main problem is the representation of the skin effect characterizing the excitation coil and the metallic part of the plasma device cooling system. In some existing eddy current models, the thin skin depth is generally represented when using boundary element method and coupling it to the FEM to discretize the nonlinear domains. The resolution of the obtained algebraic system requires a large computer memory and a long CPU time. The characteristics of the proposed model are as follows: 1. using an 60;i 62;x 60;/i 62;- 60;i 62;y 60;/i 62; analytical solution in the cooling conducting regions; 2. determining the Neumann boundary conditions, for the domains in which the FEM is required, from the solution obtained in 1; 3. deducing an equivalent electric conductivity that permits one to represent the 3D plasma device as an axisymmetrical system; 4. representing the excitation coil by a line conducting region; 5. finite element discretizing the plasma domain, in which the electric conductivity is strongly dependent on temperature. The full paper will present the details of the coupled analytical and numerical formulations and results for the proposed model validity

Notes: finite element solutions;eddy current computation;3D induction plasma devices;skin effect;excitation coil;metallic part;plasma device cooling system;x-y analytical solution;Neumann boundary conditions;equivalent electric conductivity;axisymmetrical system;3 MHz;

Abstract: A finite element analysis for the electromagnetic field in an RF plasma system with a quartz confinement tube is presented. The effect of motion and heat on plasma electrical conductivity is taken into account by coupling heat, flow and electromagnetic equations within an iterative method