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SAM captures, stores, indexes, preserves, and distributes digital research material.Wed, 10 May 2017 06:11:35 GMT2017-05-10T06:11:35ZStochastic Jiles-Atherton model accounting for soft magnetic material properties variability
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.Residual-based a posteriori error estimation for stochastic magnetostatic problems
http://hdl.handle.net/10985/9557
Residual-based a posteriori error estimation for stochastic magnetostatic problems
MAC, Duy Hung; TANG, Z.; CLENET, Stéphane; CREUSE, E.
In this paper, we propose an a posteriori error estimator for the numerical approximation of a stochastic magnetostatic problem, whose solution depends on the spatial variable but also on a stochastic one. The spatial discretization is performed with finite elements and the stochastic one with a polynomial chaos expansion. As a consequence, the numerical error results from these two levels of discretization. In this paper, we propose an error estimator that takes into account these two sources of error, and which is evaluated from the residuals.
Tue, 01 Dec 2015 00:00:00 GMThttp://hdl.handle.net/10985/95572015-12-01T00:00:00ZMAC, Duy HungTANG, Z.CLENET, StéphaneCREUSE, E.In this paper, we propose an a posteriori error estimator for the numerical approximation of a stochastic magnetostatic problem, whose solution depends on the spatial variable but also on a stochastic one. The spatial discretization is performed with finite elements and the stochastic one with a polynomial chaos expansion. As a consequence, the numerical error results from these two levels of discretization. In this paper, we propose an error estimator that takes into account these two sources of error, and which is evaluated from the residuals.Proper Generalized Decomposition method applied to solve 3D Magneto Quasistatic Field Problems coupling with External Electric Circuits
http://hdl.handle.net/10985/9862
Proper Generalized Decomposition method applied to solve 3D Magneto Quasistatic Field Problems coupling with External Electric Circuits
HENNERON, Thomas; CLENET, Stéphane
In the domain of numerical computation, Proper Generalized Decomposition (PGD), which consists of approximating the solution by a truncated sum of separable functions, is more and more applied in mechanics and has shown its efficiency in terms of computation time and memory requirements. We propose to evaluate the PGD method in order to solve 3D quasi static field problems coupling with an external electric circuit. The numerical model, obtained from the PGD formulation, is used to study 3D examples. The results are compared to those obtained when solving the full original problem. It is shown in this paper that the computation time rate versus the number of time steps is very small compared to the one a classical time stepping method and can be very efficient to solve problems when small time steps are required.
Thu, 18 Dec 2014 00:00:00 GMThttp://hdl.handle.net/10985/98622014-12-18T00:00:00ZHENNERON, ThomasCLENET, StéphaneIn the domain of numerical computation, Proper Generalized Decomposition (PGD), which consists of approximating the solution by a truncated sum of separable functions, is more and more applied in mechanics and has shown its efficiency in terms of computation time and memory requirements. We propose to evaluate the PGD method in order to solve 3D quasi static field problems coupling with an external electric circuit. The numerical model, obtained from the PGD formulation, is used to study 3D examples. The results are compared to those obtained when solving the full original problem. It is shown in this paper that the computation time rate versus the number of time steps is very small compared to the one a classical time stepping method and can be very efficient to solve problems when small time steps are required.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.
Tue, 30 Jun 2015 00:00:00 GMThttp://hdl.handle.net/10985/98612015-06-30T00:00:00ZARBENZ, 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.Reduction of a Finite Element Parametric Model using Adaptive POD Methods – Application to uncertainty quantification
http://hdl.handle.net/10985/10554
Reduction of a Finite Element Parametric Model using Adaptive POD Methods – Application to uncertainty quantification
CLENET, Stéphane; HENNERON, Thomas; IDA, Nathan
Model Order Reduction (MOR) methods enable reduction of the computation time when dealing with parametrized numerical models. Among these methods, the Proper Orthogonal Decomposition (POD) method seems to be a good candidate because of its simplicity and its accuracy. In the literature, the offline/online approach is generally applied but is not always required especially if the
study focuses on the device without any coupling with others. In this paper, we propose a method to construct adaptively the reduced model while its utilization which limits the evaluations of the full model when appropriate. A stochastic magnetostatic example with 14 uncertain parameters is studied by applying the Monte Carlo simulation method to illustrate the proposed procedure. In that case, it appears that the complexity of this method does not depend on the number of input parameters and so is not affected by the curse of dimensionality.
Tue, 01 Dec 2015 00:00:00 GMThttp://hdl.handle.net/10985/105542015-12-01T00:00:00ZCLENET, StéphaneHENNERON, ThomasIDA, NathanModel Order Reduction (MOR) methods enable reduction of the computation time when dealing with parametrized numerical models. Among these methods, the Proper Orthogonal Decomposition (POD) method seems to be a good candidate because of its simplicity and its accuracy. In the literature, the offline/online approach is generally applied but is not always required especially if the
study focuses on the device without any coupling with others. In this paper, we propose a method to construct adaptively the reduced model while its utilization which limits the evaluations of the full model when appropriate. A stochastic magnetostatic example with 14 uncertain parameters is studied by applying the Monte Carlo simulation method to illustrate the proposed procedure. In that case, it appears that the complexity of this method does not depend on the number of input parameters and so is not affected by the curse of dimensionality.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.
Tue, 01 Dec 2015 00:00:00 GMThttp://hdl.handle.net/10985/105552015-12-01T00:00:00ZHENNERON, 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.Study of the Influence of the Fabrication Process Imperfections on the Performances of a Claw Pole Synchronous Machine Using a Stochastic Approach
http://hdl.handle.net/10985/10557
Study of the Influence of the Fabrication Process Imperfections on the Performances of a Claw Pole Synchronous Machine Using a Stochastic Approach
LIU, Sijun; MAC, Hung; CLENET, Stéphane; COOREVITS, Thierry; MIPO, Jean-Claude
In mass production, fabrication processes of electrical machines are not perfectly repeatable with time, leading to dispersions on the dimensions which are not equal to their nominal values. The issue is then to link the dispersions on the dimensions which are uncertain to the performances of electrical machines in order to evaluate their influence. To deal with uncertainty, there is a growing interest in
the stochastic approach, which consists in modelling the uncertain parameters with random variables. In fact, this approach enables to quantify the influence of the variability of the uncertain parameters on the variability of the quantities of interest. In this paper, a stochastic approach coupled with a 3D Finite Element model is used to study the influence of the fabrication process imperfections like the rotor eccentricity and the stator deformation on the performances of a claw pole synchronous machine.
Tue, 01 Dec 2015 00:00:00 GMThttp://hdl.handle.net/10985/105572015-12-01T00:00:00ZLIU, SijunMAC, HungCLENET, StéphaneCOOREVITS, ThierryMIPO, Jean-ClaudeIn mass production, fabrication processes of electrical machines are not perfectly repeatable with time, leading to dispersions on the dimensions which are not equal to their nominal values. The issue is then to link the dispersions on the dimensions which are uncertain to the performances of electrical machines in order to evaluate their influence. To deal with uncertainty, there is a growing interest in
the stochastic approach, which consists in modelling the uncertain parameters with random variables. In fact, this approach enables to quantify the influence of the variability of the uncertain parameters on the variability of the quantities of interest. In this paper, a stochastic approach coupled with a 3D Finite Element model is used to study the influence of the fabrication process imperfections like the rotor eccentricity and the stator deformation on the performances of a claw pole synchronous machine.Multirate coupling of controlled rectifier and non-linear finite element model based on Waveform Relaxation Method
http://hdl.handle.net/10985/10556
Multirate coupling of controlled rectifier and non-linear finite element model based on Waveform Relaxation Method
HENNERON, Thomas; CLENET, Stéphane; PIERQUIN, antoine; BRISSET, stephane
To study a multirate system, each subsystem can be solved by a dedicated sofware with respect to the physical problem and the time constant. Then, the problem is the coupling of the solutions of the subsystems. The Waveform Relaxation Method (WRM) seems to be an interesting solution for the coupling but until now it has been mainly applied on academic examples. In this paper, the WRM is applied to perform the coupling of a controlled rectifier and a non-linear finite element model of a transformer.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10985/105562016-01-01T00:00:00ZHENNERON, ThomasCLENET, StéphanePIERQUIN, antoineBRISSET, stephaneTo study a multirate system, each subsystem can be solved by a dedicated sofware with respect to the physical problem and the time constant. Then, the problem is the coupling of the solutions of the subsystems. The Waveform Relaxation Method (WRM) seems to be an interesting solution for the coupling but until now it has been mainly applied on academic examples. In this paper, the WRM is applied to perform the coupling of a controlled rectifier and a non-linear finite element model of a transformer.Stochastic post-processing calculation of iron losses – application to a PMSM
http://hdl.handle.net/10985/7255
Stochastic post-processing calculation of iron losses – application to a PMSM
FRATILA, Mircea; RAMAROTAFIKA, Rindra; BENABOU, Abdelkader; CLENET, Stéphane; TOUNZI, Abdelmounaouim
To take account of the uncertainties introduced on the magnetic properties during the manufacturing process, the present work aims to focus on the stochastic modelling of iron losses in electrical machine stators.
The investigated samples are composed of 28 slinky stators, coming from the same production chain. The stochastic modelling approach is first described. Thereafter, the Monte-Carlo sampling method is used to calculate, in post-processing, the iron loss density in a PMSM that is modelled by the finite element method.
The interest of such an approach is emphasized by calculating the main statistical characteristics associated to the losses variability, which are Gaussian distributed for A and O formulations.
The originality of the approach is due to the fact that the global influence of the manufacturing process (cutting, assembly, …) on magnetic properties of the considered samples is taken into account in the way of computing the iron losses.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/72552013-01-01T00:00:00ZFRATILA, MirceaRAMAROTAFIKA, RindraBENABOU, AbdelkaderCLENET, StéphaneTOUNZI, AbdelmounaouimTo take account of the uncertainties introduced on the magnetic properties during the manufacturing process, the present work aims to focus on the stochastic modelling of iron losses in electrical machine stators.
The investigated samples are composed of 28 slinky stators, coming from the same production chain. The stochastic modelling approach is first described. Thereafter, the Monte-Carlo sampling method is used to calculate, in post-processing, the iron loss density in a PMSM that is modelled by the finite element method.
The interest of such an approach is emphasized by calculating the main statistical characteristics associated to the losses variability, which are Gaussian distributed for A and O formulations.
The originality of the approach is due to the fact that the global influence of the manufacturing process (cutting, assembly, …) on magnetic properties of the considered samples is taken into account in the way of computing the iron losses.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
Mon, 01 Oct 2012 00:00:00 GMThttp://hdl.handle.net/10985/70752012-10-01T00:00:00ZRAMAROTAFIKA, 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 samples