SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Tue, 28 Jan 2020 00:45:44 GMT2020-01-28T00:45:44ZSolution of Large Stochastic Finite Element Problems – Application to ECT-NDT
http://hdl.handle.net/10985/7317
Solution of Large Stochastic Finite Element Problems – Application to ECT-NDT
BEDDEK, Karim; CLENET, Stéphane; MOREAU, Olivier; LE MENACH, Yvonnick
This paper describes an efficient bloc iterative solver for the Spectral Stochastic Finite Element Method (SSFEM). The SSFEM was widely used to quantify the effect of input data uncertainties on the outputs of finite element models. The bloc iterative solver allows reducing computational cost of the SSFEM. The method is applied on an industrial Non Destructive Testing (NDT) problem. The numerical performances of the method are compared with those of the Non-Intrusive Spectral Projection (NISP).
Version éditeur disponible à cette adresse : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514633
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/73172013-01-01T00:00:00ZBEDDEK, KarimCLENET, StéphaneMOREAU, OlivierLE MENACH, YvonnickThis paper describes an efficient bloc iterative solver for the Spectral Stochastic Finite Element Method (SSFEM). The SSFEM was widely used to quantify the effect of input data uncertainties on the outputs of finite element models. The bloc iterative solver allows reducing computational cost of the SSFEM. The method is applied on an industrial Non Destructive Testing (NDT) problem. The numerical performances of the method are compared with those of the Non-Intrusive Spectral Projection (NISP).Experimental characterization of the iron losses variability in stators of electrical machines
http://hdl.handle.net/10985/7115
Experimental characterization of the iron losses variability in stators of electrical machines
RAMAROTAFIKA, Rindra; BENABOU, Abdelkader; MIPO, Jean-Claude; CLENET, Stéphane
Manufacturing processes may introduce a significant variability on the magnetic properties of claw pole generator stators. The present work deals with the analysis of two groups of stator samples. The first group is composed of 28 slinky stators (SS) and the second group is composed of 5 stators, manufactured using laser cut stacked laminations (SL). Both groups are made from the same lamination grade and with the same geometrical dimensions. Characterization was carried out for several levels of excitation field at 50Hz. A noticeable variability has been observed on the iron losses for SS samples, whereas it appears to be not significant for SL samples. The loss separation technique has then been investigated for the SS samples. Results show that the variability of static losses is more important than the one of dynamic losses.
Version éditeur disponible à l'adresse suivante : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6172417
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10985/71152012-01-01T00:00:00ZRAMAROTAFIKA, RindraBENABOU, AbdelkaderMIPO, Jean-ClaudeCLENET, StéphaneManufacturing processes may introduce a significant variability on the magnetic properties of claw pole generator stators. The present work deals with the analysis of two groups of stator samples. The first group is composed of 28 slinky stators (SS) and the second group is composed of 5 stators, manufactured using laser cut stacked laminations (SL). Both groups are made from the same lamination grade and with the same geometrical dimensions. Characterization was carried out for several levels of excitation field at 50Hz. A noticeable variability has been observed on the iron losses for SS samples, whereas it appears to be not significant for SL samples. The loss separation technique has then been investigated for the SS samples. Results show that the variability of static losses is more important than the one of dynamic losses.Stochastic Non Destructive Testing simulation: sensitivity analysis applied to material properties in clogging of nuclear power plant steam generators
http://hdl.handle.net/10985/7116
Stochastic Non Destructive Testing simulation: sensitivity analysis applied to material properties in clogging of nuclear power plant steam generators
MOREAU, Olivier; BEDDEK, Karim; CLENET, Stéphane; LE MENACH, Yvonnick
A Non destructive Testing (NDT) procedure is currently used to estimate the clogging of tube support plates in French nuclear power plant steam generators. A stochastic approach has been applied to Finite Element electromagnetic field simulation to evaluate the impact of material properties uncertainties on the monitoring signal. The Polynomial Chaos Expansion method makes it possible to easily derive the Sobol decomposition which measures how much the variability of each input parameter affects the model output
La version éditeur de cette publication est disponible à l'adresse suivante : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514684
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/71162013-01-01T00:00:00ZMOREAU, OlivierBEDDEK, KarimCLENET, StéphaneLE MENACH, YvonnickA Non destructive Testing (NDT) procedure is currently used to estimate the clogging of tube support plates in French nuclear power plant steam generators. A stochastic approach has been applied to Finite Element electromagnetic field simulation to evaluate the impact of material properties uncertainties on the monitoring signal. The Polynomial Chaos Expansion method makes it possible to easily derive the Sobol decomposition which measures how much the variability of each input parameter affects the model outputSolution of Static Field Problems With Random Domains
http://hdl.handle.net/10985/7275
Solution of Static Field Problems With Random Domains
MAC, Duy Hung; CLENET, Stéphane; MIPO, Jean-Claude; MOREAU, Olivier
A method to solve stochastic partial differential equations on random domains consists in using a one-to-one random mapping function which transforms the random domain into a deterministic domain. With this method, the randomness is then borne by the constitutive relationship of the material. In this paper, this method is applied in electrokinetics in the case of scalar potential and vector potential formulations. An example is treated and the proposed method is compared to a nonintrusive method (NIM) based on the remeshing of the random domains.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10985/72752010-01-01T00:00:00ZMAC, Duy HungCLENET, StéphaneMIPO, Jean-ClaudeMOREAU, OlivierA method to solve stochastic partial differential equations on random domains consists in using a one-to-one random mapping function which transforms the random domain into a deterministic domain. With this method, the randomness is then borne by the constitutive relationship of the material. In this paper, this method is applied in electrokinetics in the case of scalar potential and vector potential formulations. An example is treated and the proposed method is compared to a nonintrusive method (NIM) based on the remeshing of the random domains.Balanced Proper Orthogonal Decomposition Applied to Magnetoquasistatic Problems Through a Stabilization Methodology
http://hdl.handle.net/10985/11755
Balanced Proper Orthogonal Decomposition Applied to Magnetoquasistatic Problems Through a Stabilization Methodology
MONTIER, Laurent; HENNERON, Thomas; GOURSAUD, Benjamin; CLENET, Stéphane
Model Order Reduction (MOR) methods are applied in different areas of physics in order to reduce the computational time of large scale systems. It has been an active field of research for many years, in mechanics especially, but it is quite recent for magnetoquasistatic problems. Although the most famous method, the Proper Orthogonal Decomposition (POD) has been applied for modelling many electromagnetic devices, this method can lack accuracy for low order magnitude output quantities, like flux associated with a probe in regions where the field is low. However, the Balanced Proper Orthogonal Decomposition (BPOD) is a MOR method which takes into account these output quantities in its reduced model to render them accurately. Even if the BPOD may lead to unstable reduced systems, this can be overcome by a stabilization procedure. Therefore, the POD and stabilized BPOD will be compared on a 3D linear magnetoquasistatic field problem.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/117552017-01-01T00:00:00ZMONTIER, LaurentHENNERON, ThomasGOURSAUD, BenjaminCLENET, StéphaneModel Order Reduction (MOR) methods are applied in different areas of physics in order to reduce the computational time of large scale systems. It has been an active field of research for many years, in mechanics especially, but it is quite recent for magnetoquasistatic problems. Although the most famous method, the Proper Orthogonal Decomposition (POD) has been applied for modelling many electromagnetic devices, this method can lack accuracy for low order magnitude output quantities, like flux associated with a probe in regions where the field is low. However, the Balanced Proper Orthogonal Decomposition (BPOD) is a MOR method which takes into account these output quantities in its reduced model to render them accurately. Even if the BPOD may lead to unstable reduced systems, this can be overcome by a stabilization procedure. Therefore, the POD and stabilized BPOD will be compared on a 3D linear magnetoquasistatic field problem.Error Estimation for Model Order Reduction of Finite Element Parametric Problems
http://hdl.handle.net/10985/11034
Error Estimation for Model Order Reduction of Finite Element Parametric Problems
CLENET, Stéphane; HENNERON, Thomas
To solve a parametric model in computational electromagnetics, the Finite Element method is often used. To reduce the computational time and the memory requirement, the Finite Element method can be combined with Model Order Reduction Technic like the Proper Orthogonal Decomposition (POD) and the (Discrete) Empirical Interpolation ((D)EI) Methods. These three numerical methods introduce errors of discretisation, reduction and interpolation respectively. The solution of the parametric model will be efficient if the three errors are of the same order and so they need to be evaluated and compared. In this paper, we propose an aposteriori error estimator based on the verification of the constitutive law which estimates the three different errors. An example of application in magnetostatics with 11 parameters is treated where it is shown how the error estimator can be used to control and to improve the accuracy of the solution of the reduced model.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10985/110342016-01-01T00:00:00ZCLENET, StéphaneHENNERON, ThomasTo solve a parametric model in computational electromagnetics, the Finite Element method is often used. To reduce the computational time and the memory requirement, the Finite Element method can be combined with Model Order Reduction Technic like the Proper Orthogonal Decomposition (POD) and the (Discrete) Empirical Interpolation ((D)EI) Methods. These three numerical methods introduce errors of discretisation, reduction and interpolation respectively. The solution of the parametric model will be efficient if the three errors are of the same order and so they need to be evaluated and compared. In this paper, we propose an aposteriori error estimator based on the verification of the constitutive law which estimates the three different errors. An example of application in magnetostatics with 11 parameters is treated where it is shown how the error estimator can be used to control and to improve the accuracy of the solution of the reduced model.Stochastic 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.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.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.Model 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.