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http://hdl.handle.net/10985/7296
Stochastic Jiles-Atherton model accounting for soft magnetic material properties variability
RAMAROTAFIKA, Rindra; BENABOU, Abdelkader; CLENET, Stéphane
Industrial processing (cutting, assembly…) of steel laminations can lead to significant modifications in their magnetic properties. Moreover, the repeatability of these modifications is not usually verified because of the tool wear or, more intrinsically, to the manufacturing process itself. When investigating the iron losses, it is generally observed that the hysteresis losses contribution are more likely to be affected. In the present work, twenty eight (28) samples of slinky stator (SS) are investigated, at a frequency of 5Hz and 1.5T. A stochastic model is then developed, using the Jiles-Atherton model together with a statistical approach to account for the variability of the hysteresis loops of the considered samples.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/72962013-01-01T00:00:00ZRAMAROTAFIKA, RindraBENABOU, AbdelkaderCLENET, StéphaneIndustrial processing (cutting, assembly…) of steel laminations can lead to significant modifications in their magnetic properties. Moreover, the repeatability of these modifications is not usually verified because of the tool wear or, more intrinsically, to the manufacturing process itself. When investigating the iron losses, it is generally observed that the hysteresis losses contribution are more likely to be affected. In the present work, twenty eight (28) samples of slinky stator (SS) are investigated, at a frequency of 5Hz and 1.5T. A stochastic model is then developed, using the Jiles-Atherton model together with a statistical approach to account for the variability of the hysteresis loops of the considered samples.Characterization of the local Electrical Properties of Electrical Machine Parts with non-Trivial Geometry
http://hdl.handle.net/10985/9861
Characterization of the local Electrical Properties of Electrical Machine Parts with non-Trivial Geometry
ARBENZ, Laure; BENABOU, Abdelkader; CLENET, Stéphane; MIPO, Jean-Claude; FAVEROLLE, Pierre
In electrical machines, knowing the electrical conductivity is of importance for the eddy current calculation, especially when massive iron parts are involved. Generally the conductivity is measured on samples of raw materials with simple geometries. Indeed, a simple geometry is suitable for applying an analytical approach to deduce the electrical conductivity from the measured electrical quantities. Nevertheless, when a non destructive measurement is required, the measurement of the electrical conductivity can become rather difficult on parts with complex geometry. To that end, with the help of the Finite Element Modeling approach (FEM), a strategy is developed to characterize the local electrical properties of parts with a non-trivial geometry.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/98612015-01-01T00:00:00ZARBENZ, LaureBENABOU, AbdelkaderCLENET, StéphaneMIPO, Jean-ClaudeFAVEROLLE, PierreIn electrical machines, knowing the electrical conductivity is of importance for the eddy current calculation, especially when massive iron parts are involved. Generally the conductivity is measured on samples of raw materials with simple geometries. Indeed, a simple geometry is suitable for applying an analytical approach to deduce the electrical conductivity from the measured electrical quantities. Nevertheless, when a non destructive measurement is required, the measurement of the electrical conductivity can become rather difficult on parts with complex geometry. To that end, with the help of the Finite Element Modeling approach (FEM), a strategy is developed to characterize the local electrical properties of parts with a non-trivial geometry.Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines
http://hdl.handle.net/10985/7075
Stochastic Modeling of Soft Magnetic Properties of Electrical Steels: Application to Stators of Electrical Machines
RAMAROTAFIKA, Rindra; BENABOU, Abdelkader; CLENET, Stéphane
To take account of the uncertainties introduced on the soft magnetic materials properties (magnetic behavior law, iron losses) during the manufacturing process, the present work deals with the stochastic modeling of the magnetic behavior law B-H and iron losses of claw pole stator generator. Twenty eight (28) samples of slinky stator (SS) coming from the same production chain have been investigated. The used approaches are similar to those used in mechanics. The accuracy of existing anhysteretic models has been tested first using cross validation techniques. The well known iron loss separation model has been implemented to take into account the variability of the losses. Then, the Multivariate Gaussian distribution is chosen to model the variability and dependencies between identified parameters, for both behavior law and iron loss models. The developed stochastic models allow predicting a 98% confidence interval for the considered samples
La version éditeur de cet article est disponible à l'adresse suivante : 10.1109/TMAG.2012.2201734
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10985/70752012-01-01T00:00:00ZRAMAROTAFIKA, RindraBENABOU, AbdelkaderCLENET, StéphaneTo take account of the uncertainties introduced on the soft magnetic materials properties (magnetic behavior law, iron losses) during the manufacturing process, the present work deals with the stochastic modeling of the magnetic behavior law B-H and iron losses of claw pole stator generator. Twenty eight (28) samples of slinky stator (SS) coming from the same production chain have been investigated. The used approaches are similar to those used in mechanics. The accuracy of existing anhysteretic models has been tested first using cross validation techniques. The well known iron loss separation model has been implemented to take into account the variability of the losses. Then, the Multivariate Gaussian distribution is chosen to model the variability and dependencies between identified parameters, for both behavior law and iron loss models. The developed stochastic models allow predicting a 98% confidence interval for the considered samplesModel Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods
http://hdl.handle.net/10985/7816
Model Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods
HENNERON, Thomas; CLENET, Stéphane
In the domain of numerical computation, Model Order Reduction approaches are more and more frequently applied in mechanics and have shown their efficiency in terms of reduction of computation time and memory storage requirements. One of these approaches, the Proper Orthogonal Decomposition (POD), can be very efficient in solving linear problems but encounters limitations in the non-linear case. In this paper, the Discret Empirical Interpolation Method coupled with the POD method is presented. This is an interesting alternative to reduce large-scale systems deriving from the discretization of non-linear magnetostatic problems coupled with an external electrical circuit.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/78162014-01-01T00:00:00ZHENNERON, ThomasCLENET, StéphaneIn the domain of numerical computation, Model Order Reduction approaches are more and more frequently applied in mechanics and have shown their efficiency in terms of reduction of computation time and memory storage requirements. One of these approaches, the Proper Orthogonal Decomposition (POD), can be very efficient in solving linear problems but encounters limitations in the non-linear case. In this paper, the Discret Empirical Interpolation Method coupled with the POD method is presented. This is an interesting alternative to reduce large-scale systems deriving from the discretization of non-linear magnetostatic problems coupled with an external electrical circuit.Non Linear Proper Generalized Decomposition method applied to the magnetic simulation of a SMC microstructure
http://hdl.handle.net/10985/7815
Non Linear Proper Generalized Decomposition method applied to the magnetic simulation of a SMC microstructure
HENNERON, Thomas; BENABOU, Abdelkader; CLENET, Stéphane
Improvement of the magnetic performances of Soft Magnetic Composites (SMC) materials requires to link the microstructures to the macroscopic magnetic behavior law. This can be achieved with the FE method using the geometry reconstruction from images of the microstructure. Nevertheless, it can lead to large computational times. In that context, the Proper Generalized Decomposition (PGD), that is an approximation method originally developed in mechanics, and based on a finite sum of separable functions, can be of interest in our case. In this work, we propose to apply the PGD method to the SMC microstructure magnetic simulation. A non-linear magnetostatic problem with the scalar potential formulation is then solved.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10985/78152012-01-01T00:00:00ZHENNERON, ThomasBENABOU, AbdelkaderCLENET, StéphaneImprovement of the magnetic performances of Soft Magnetic Composites (SMC) materials requires to link the microstructures to the macroscopic magnetic behavior law. This can be achieved with the FE method using the geometry reconstruction from images of the microstructure. Nevertheless, it can lead to large computational times. In that context, the Proper Generalized Decomposition (PGD), that is an approximation method originally developed in mechanics, and based on a finite sum of separable functions, can be of interest in our case. In this work, we propose to apply the PGD method to the SMC microstructure magnetic simulation. A non-linear magnetostatic problem with the scalar potential formulation is then solved.Application of the PGD and DEIM to solve a 3D Non-Linear Magnetostatic Problem coupled with the Circuit Equations
http://hdl.handle.net/10985/10555
Application of the PGD and DEIM to solve a 3D Non-Linear Magnetostatic Problem coupled with the Circuit Equations
HENNERON, Thomas; CLENET, Stéphane
Among the model order reduction techniques, the Proper Generalized Decomposition (PGD) has shown its efficiency to solve static and quasistatic problems in the time domain. However, the introduction of nonlinearity due to ferromagnetic materials for example has never been addressed. In this paper, the PGD technique combined with the Discrete Empirical Interpolation Method (DEIM) is applied to solve a non-linear problem in magnetostatic coupled with the circuit equations. To evaluate the reduction technique, the transient state of a three phase transformer at no load is studied using the full Finite Element model and the PGD_DEIM model.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/105552015-01-01T00:00:00ZHENNERON, ThomasCLENET, StéphaneAmong the model order reduction techniques, the Proper Generalized Decomposition (PGD) has shown its efficiency to solve static and quasistatic problems in the time domain. However, the introduction of nonlinearity due to ferromagnetic materials for example has never been addressed. In this paper, the PGD technique combined with the Discrete Empirical Interpolation Method (DEIM) is applied to solve a non-linear problem in magnetostatic coupled with the circuit equations. To evaluate the reduction technique, the transient state of a three phase transformer at no load is studied using the full Finite Element model and the PGD_DEIM model.Modelling of a hysteresis motor using the Jiles-Atherton model
http://hdl.handle.net/10985/10139
Modelling of a hysteresis motor using the Jiles-Atherton model
BENABOU, Abdelkader; CLENET, Stéphane; BOUAZIZ, Lounas
In this paper, we present a model of a hysteresis motor based on Maxwell's equations coupled with the Jiles-Atherton (J-A) hysteresis model solved by the finite element method. The aim of this work is to validate such a model by comparison with the experimental results (electromagnetic torque, voltage, current). We also present an analysis of this motor when imposing current or voltage in the 2D vector potential formulation.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/101392015-01-01T00:00:00ZBENABOU, AbdelkaderCLENET, StéphaneBOUAZIZ, LounasIn this paper, we present a model of a hysteresis motor based on Maxwell's equations coupled with the Jiles-Atherton (J-A) hysteresis model solved by the finite element method. The aim of this work is to validate such a model by comparison with the experimental results (electromagnetic torque, voltage, current). We also present an analysis of this motor when imposing current or voltage in the 2D vector potential formulation.Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems
http://hdl.handle.net/10985/7814
Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems
PIERQUIN, Antoine; BRISSET, Stéphane; HENNERON, Thomas; CLENET, Stéphane
We present an optimization problem that requires to model a multirate system, composed of subsystems with different time constants. We use waveform relaxation method in order to simulate such a system. But computation time can be penalizing in an optimization context. Thus we apply output space mapping which uses several models of the system to accelerate optimization. Waveform relaxation method is one of the models used in output space mapping.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/78142014-01-01T00:00:00ZPIERQUIN, AntoineBRISSET, StéphaneHENNERON, ThomasCLENET, StéphaneWe present an optimization problem that requires to model a multirate system, composed of subsystems with different time constants. We use waveform relaxation method in order to simulate such a system. But computation time can be penalizing in an optimization context. Thus we apply output space mapping which uses several models of the system to accelerate optimization. Waveform relaxation method is one of the models used in output space mapping.Investigations on the performances of the electrical generator of a rim-driven marine current turbine”
http://hdl.handle.net/10985/9257
Investigations on the performances of the electrical generator of a rim-driven marine current turbine”
DROUEN, Laurent; CHARPENTIER, Jean-Frederic; SEMAIL, Eric; CLENET, Stéphane
In this paper, the electrical generator of a rim-driven horizontal-axis current turbine is modeled in detail. Its main characteristics and performances are evaluated (efficiency, mass, cost, etc). This generator is of permanent magnet direct-driven synchronous type and is connected to a variable speed power electronics drive. It is then compared to a more traditional technology (a pod generator) in terms of mass and cost for a common set of specification. In addition, due to the specific geometry of the machine, the use of low-cost ferrite magnets is investigated in place of NdFeB magnets.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10985/92572008-01-01T00:00:00ZDROUEN, LaurentCHARPENTIER, Jean-FredericSEMAIL, EricCLENET, StéphaneIn this paper, the electrical generator of a rim-driven horizontal-axis current turbine is modeled in detail. Its main characteristics and performances are evaluated (efficiency, mass, cost, etc). This generator is of permanent magnet direct-driven synchronous type and is connected to a variable speed power electronics drive. It is then compared to a more traditional technology (a pod generator) in terms of mass and cost for a common set of specification. In addition, due to the specific geometry of the machine, the use of low-cost ferrite magnets is investigated in place of NdFeB magnets.Error estimation of a proper orthogonal decomposition reduced model of a permanent magnet synchronous machine
http://hdl.handle.net/10985/9264
Error estimation of a proper orthogonal decomposition reduced model of a permanent magnet synchronous machine
HENNERON, Thomas; MAC, Hung; CLENET, Stéphane
Model order reduction methods, like the proper orthogonal decomposition (POD), enable to reduce dramatically the size of a finite element (FE) model. The price to pay is a loss of accuracy compared with the original FE model that should be of course controlled. In this study, the authors apply an error estimator based on the verification of the constitutive relationship to compare the reduced model accuracy with the full model accuracy when POD is applied. This estimator is tested on an example of a permanent magnet synchronous machine.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/92642015-01-01T00:00:00ZHENNERON, ThomasMAC, HungCLENET, StéphaneModel order reduction methods, like the proper orthogonal decomposition (POD), enable to reduce dramatically the size of a finite element (FE) model. The price to pay is a loss of accuracy compared with the original FE model that should be of course controlled. In this study, the authors apply an error estimator based on the verification of the constitutive relationship to compare the reduced model accuracy with the full model accuracy when POD is applied. This estimator is tested on an example of a permanent magnet synchronous machine.