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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.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.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, Abdelmounaïm
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, AbdelmounaïmTo 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.Global Parameters Sensitivity Analysis and Development of a Two-Dimensional Real-Time Model of Proton-Exchange-Membrane Fuel Cells
http://hdl.handle.net/10985/13418
Global Parameters Sensitivity Analysis and Development of a Two-Dimensional Real-Time Model of Proton-Exchange-Membrane Fuel Cells
ZHOU, Daming; NGUYEN, Thu; BREAZ, Elena; ZHAO, Dongdong; CLENET, Stéphane; GAO, Fei
This paper presents a 2-D real-time modeling approach for a proton-exchange-membrane fuel cell (PEMFC). The proposed model covers multi-physical domains for both ﬂuidic and electrochemical features, which considers in particular the ﬂow ﬁeld geometric form of fuel cell. The characteristics of reactant gas convection in the serpentine gas pipeline and diﬀusion phenomenon through the gas diﬀusion layer (GDL) are thoroughly considered in ﬂuidic domain model. In addition, a three levels iterative solver is developed in order to accurately calculate the implicit spatial physical quantities distribution in electrochemical domain. Moreover, the proposed 2-D real-time modeling approach uses a numerical method to achieve a fast execution time, and can thus be further easily applied to any real-time control implementation or online diagnostic system. After experimental validation under diﬀerent fuel cell operating conditions, an iterative Least Angle Regression (LAR) method is used to eﬃciently and accurately perform the global parameters sensitivity analysis based on Sobol deﬁnition. The online analysis results give an insight into the inﬂuences of modeling parameters on fuel cell performance. The eﬀect of interactions between parameters’ sensitivities is especially investigated, which can provide useful in- formation for degradation understanding, parameters tuning, re-calibration of the parameters and online prognostic.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/134182018-01-01T00:00:00ZZHOU, DamingNGUYEN, ThuBREAZ, ElenaZHAO, DongdongCLENET, StéphaneGAO, FeiThis paper presents a 2-D real-time modeling approach for a proton-exchange-membrane fuel cell (PEMFC). The proposed model covers multi-physical domains for both ﬂuidic and electrochemical features, which considers in particular the ﬂow ﬁeld geometric form of fuel cell. The characteristics of reactant gas convection in the serpentine gas pipeline and diﬀusion phenomenon through the gas diﬀusion layer (GDL) are thoroughly considered in ﬂuidic domain model. In addition, a three levels iterative solver is developed in order to accurately calculate the implicit spatial physical quantities distribution in electrochemical domain. Moreover, the proposed 2-D real-time modeling approach uses a numerical method to achieve a fast execution time, and can thus be further easily applied to any real-time control implementation or online diagnostic system. After experimental validation under diﬀerent fuel cell operating conditions, an iterative Least Angle Regression (LAR) method is used to eﬃciently and accurately perform the global parameters sensitivity analysis based on Sobol deﬁnition. The online analysis results give an insight into the inﬂuences of modeling parameters on fuel cell performance. The eﬀect of interactions between parameters’ sensitivities is especially investigated, which can provide useful in- formation for degradation understanding, parameters tuning, re-calibration of the parameters and online prognostic.Temperature Dependence in the Jiles–Atherton Model for Non-Oriented Electrical Steels: An Engineering Approach
http://hdl.handle.net/10985/13419
Temperature Dependence in the Jiles–Atherton Model for Non-Oriented Electrical Steels: An Engineering Approach
HUSSAIN, Sajid; BENABOU, Abdelkader; CLENET, Stéphane; LOWTHER, David A.
High operating temperatures modify the magnetic behavior of ferromagnetic cores which may affect the performance of electrical machines. Therefore, a temperature-dependent material model is necessary to model the electrical machine behavior more accurately during the design process. Physics-inspired hysteresis models, such as the Jiles–Atherton (JA) model, seem to be promising candidates to incorporate temperature effects and can be embedded in finite element simulations. In this paper, we have identified the JA model parameters from measurements for a temperature range experienced by non oriented electrical steels in electrical machines during their operation. Based on the analysis, a parameter reduction has been performed. The proposed approach simplifies the identification procedures by reducing the number of model parameters and does not require any additional material information, such as the Curie temperature. The resulting temperature-dependent JA model is validated against measurements, and the results are in good agreement.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/134192018-01-01T00:00:00ZHUSSAIN, SajidBENABOU, AbdelkaderCLENET, StéphaneLOWTHER, David A.High operating temperatures modify the magnetic behavior of ferromagnetic cores which may affect the performance of electrical machines. Therefore, a temperature-dependent material model is necessary to model the electrical machine behavior more accurately during the design process. Physics-inspired hysteresis models, such as the Jiles–Atherton (JA) model, seem to be promising candidates to incorporate temperature effects and can be embedded in finite element simulations. In this paper, we have identified the JA model parameters from measurements for a temperature range experienced by non oriented electrical steels in electrical machines during their operation. Based on the analysis, a parameter reduction has been performed. The proposed approach simplifies the identification procedures by reducing the number of model parameters and does not require any additional material information, such as the Curie temperature. The resulting temperature-dependent JA model is validated against measurements, and the results are in good agreement.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.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/105572015-01-01T00:00:00ZLIU, 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.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.Solution 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 output