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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 22 May 2022 01:04:35 GMT2022-05-22T01:04:35ZApplication 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.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.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.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.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.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 outputProper 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.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/98622014-01-01T00:00:00ZHENNERON, 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.Proper Generalized Decomposition Applied on a Rotating Electrical Machine
http://hdl.handle.net/10985/12734
Proper Generalized Decomposition Applied on a Rotating Electrical Machine
MONTIER, Laurent; HENNERON, Thomas; CLENET, Stéphane; GOURSAUD, Benjamin
The Proper Generalized Decomposition (PGD) is a model order reduction method which allows to reduce the computational time of a numerical problem by seeking for a separated representation of the solution. The PGD has been already applied to study an electrical machine but at standstill without accounting the motion of the rotor. In this paper, we propose a method to account for the rotation in the PGD approach in order to build an efficient metamodel of an electrical machine. Then, the machine metamodel will be coupled to its electrical and mechanical environment in order to obtain accurate results with an acceptable computational time on a full simulation.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/127342018-01-01T00:00:00ZMONTIER, LaurentHENNERON, ThomasCLENET, StéphaneGOURSAUD, BenjaminThe Proper Generalized Decomposition (PGD) is a model order reduction method which allows to reduce the computational time of a numerical problem by seeking for a separated representation of the solution. The PGD has been already applied to study an electrical machine but at standstill without accounting the motion of the rotor. In this paper, we propose a method to account for the rotation in the PGD approach in order to build an efficient metamodel of an electrical machine. Then, the machine metamodel will be coupled to its electrical and mechanical environment in order to obtain accurate results with an acceptable computational time on a full simulation.Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
http://hdl.handle.net/10985/12997
Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
PIERQUIN, Antoine; HENNERON, Thomas; CLENET, Stéphane
Among the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its efficiency to solve magnetostatic and magneto-quasistatic problems in the time domain. However, the POD is intrusive in the sense that it requires the extraction of the matrix system of the full model to build the reduced model. To avoid this extraction, nonintrusive approaches like the Data Driven (DD) methods enable to approximate the reduced model without the access to the full matrix system. In this article, the DD-POD method is applied to build a low dimensional system to solve a magnetostatic problem coupled with electric circuit equations.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/129972018-01-01T00:00:00ZPIERQUIN, AntoineHENNERON, ThomasCLENET, StéphaneAmong the model order reduction techniques, the Proper Orthogonal Decomposition (POD) has shown its efficiency to solve magnetostatic and magneto-quasistatic problems in the time domain. However, the POD is intrusive in the sense that it requires the extraction of the matrix system of the full model to build the reduced model. To avoid this extraction, nonintrusive approaches like the Data Driven (DD) methods enable to approximate the reduced model without the access to the full matrix system. In this article, the DD-POD method is applied to build a low dimensional system to solve a magnetostatic problem coupled with electric circuit equations.Comparative study of methods for optimization of electromagnetic devices with uncertainty
http://hdl.handle.net/10985/13420
Comparative study of methods for optimization of electromagnetic devices with uncertainty
DENG, Siyang; BRISSET, Stéphane; CLENET, Stéphane
This paper compares different probabilistic optimization methods dealing with uncertainties. Reliability-Based Design Optimization is presented as well as various approaches to calculate the probability of failure. They are compared in terms of precision and number of evaluations on mathematical and electromagnetic design problems to highlight the most effective methods.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/134202018-01-01T00:00:00ZDENG, SiyangBRISSET, StéphaneCLENET, StéphaneThis paper compares different probabilistic optimization methods dealing with uncertainties. Reliability-Based Design Optimization is presented as well as various approaches to calculate the probability of failure. They are compared in terms of precision and number of evaluations on mathematical and electromagnetic design problems to highlight the most effective methods.