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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Thu, 23 Sep 2021 00:30:19 GMT2021-09-23T00:30:19ZError 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.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.A posteriori error estimation for stochastic static problems
http://hdl.handle.net/10985/8319
A posteriori error estimation for stochastic static problems
MAC, Hung; CLENET, Stéphane
To solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the random variables can be approximated in a finite dimension functional space - a truncated polynomial chaos expansion. The error between the exact solution and the approximated one depends not only on the spatial mesh but also on the discretization along the stochastic dimension. In this paper, we propose an a posteriori estimation of the error due to the discretization along the stochastic dimension.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/83192014-01-01T00:00:00ZMAC, HungCLENET, StéphaneTo solve stochastic static field problems, a discretization by the Finite Element Method can be used. A system of equations is obtained with the unknowns (scalar potential at nodes for example) being random variables. To solve this stochastic system, the random variables can be approximated in a finite dimension functional space - a truncated polynomial chaos expansion. The error between the exact solution and the approximated one depends not only on the spatial mesh but also on the discretization along the stochastic dimension. In this paper, we propose an a posteriori estimation of the error due to the discretization along the stochastic dimension.Uncertainty quantification and sensitivity analysis in electrical machines with stochastically varying machine parameters
http://hdl.handle.net/10985/9556
Uncertainty quantification and sensitivity analysis in electrical machines with stochastically varying machine parameters
OFFERMANN, Peter; MAC, Hung; NGUYEN, Thu Trang; CLENET, Stéphane; DE GERSEM, Herbert; HAMEYER, Kay
Electrical machines that are produced in mass production suffer from stochastic deviations introduced during the production process. These variations can cause undesired and unanticipated side-effects. Until now, only worst case analysis and Monte-Carlo simulation have been used to predict such stochastic effects and reduce their influence on the machine behavior. However, these methods have proven to be either inaccurate or very slow. This paper presents the application of a polynomialchaos meta-modeling at the example of stochastically varying stator deformations in a permanent-magnet synchronous machine. The applied methodology allows a faster or more accurate uncertainty propagation with the benefit of a zero-cost calculation of sensitivity indices, eventually enabling an easier creation of stochastic insensitive, hence robust designs.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/95562015-01-01T00:00:00ZOFFERMANN, PeterMAC, HungNGUYEN, Thu TrangCLENET, StéphaneDE GERSEM, HerbertHAMEYER, KayElectrical machines that are produced in mass production suffer from stochastic deviations introduced during the production process. These variations can cause undesired and unanticipated side-effects. Until now, only worst case analysis and Monte-Carlo simulation have been used to predict such stochastic effects and reduce their influence on the machine behavior. However, these methods have proven to be either inaccurate or very slow. This paper presents the application of a polynomialchaos meta-modeling at the example of stochastically varying stator deformations in a permanent-magnet synchronous machine. The applied methodology allows a faster or more accurate uncertainty propagation with the benefit of a zero-cost calculation of sensitivity indices, eventually enabling an easier creation of stochastic insensitive, hence robust designs.Influence of uncertainties on the B(H) curves on the flux linkage of a turboalternator
http://hdl.handle.net/10985/7482
Influence of uncertainties on the B(H) curves on the flux linkage of a turboalternator
MAC, Hung; CLENET, Stéphane; BEDDEK, Karim; KORECKI, Julien; MOREAU, Olivier; CHEVALLIER, Loic; THOMAS, Pierre
In this paper, we analyze the influence of the uncertainties on the behavior constitutive laws of ferromagnetic materials on the behavior of a turboalternator. A simple stochastic model of anhysteretic nonlinear B(H) curve is proposed for the ferromagnetic yokes of the stator and the rotor. The B(H) curve is defined by five random parameters. We quantify the influence of the variability of these five parameters on the flux linkage of one phase of the stator winding depending on the excitation current I. The influence of each parameter is analyzed via the Sobol indices. With this analysis, we can determine the most influential parameters for each state of magnetization (according to the level of I) and investigate where the characterization process of the B(H) curve should focus to improve the accuracy of the computed flux linkage.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/74822013-01-01T00:00:00ZMAC, HungCLENET, StéphaneBEDDEK, KarimKORECKI, JulienMOREAU, OlivierCHEVALLIER, LoicTHOMAS, PierreIn this paper, we analyze the influence of the uncertainties on the behavior constitutive laws of ferromagnetic materials on the behavior of a turboalternator. A simple stochastic model of anhysteretic nonlinear B(H) curve is proposed for the ferromagnetic yokes of the stator and the rotor. The B(H) curve is defined by five random parameters. We quantify the influence of the variability of these five parameters on the flux linkage of one phase of the stator winding depending on the excitation current I. The influence of each parameter is analyzed via the Sobol indices. With this analysis, we can determine the most influential parameters for each state of magnetization (according to the level of I) and investigate where the characterization process of the B(H) curve should focus to improve the accuracy of the computed flux linkage.