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SAM collecte, emmagasine, indexe, archive, et diffuse du matériel de recherche en format numérique.Sun, 27 May 2018 07:57:16 GMT2018-05-27T07:57:16ZModel-Order Reduction of Magnetoquasi-Static Problems Based on POD and Arnoldi-Based Krylov Methods
http://hdl.handle.net/10985/9558
PIERQUIN, Antoine; HENNERON, Thomas; CLENET, Stéphane; BRISSET, Stephane
The proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigated in order to reduce a finite-element model of a quasi-static problem. Both methods are compared on an academic example in terms of computation time and precision.
Sun, 01 Mar 2015 00:00:00 GMThttp://hdl.handle.net/10985/95582015-03-01T00:00:00ZPIERQUIN, AntoineHENNERON, ThomasCLENET, StéphaneBRISSET, StephaneThe proper orthogonal decomposition method and Arnoldi-based Krylov projection method are investigated in order to reduce a finite-element model of a quasi-static problem. Both methods are compared on an academic example in terms of computation time and precision.Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems
http://hdl.handle.net/10985/7814
PIERQUIN, Antoine; BRISSET, Stéphane; HENNERON, Thomas; CLENET, Stéphane
IEEE transactions on Magnetics
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.
Sat, 01 Feb 2014 00:00:00 GMThttp://hdl.handle.net/10985/78142014-02-01T00:00:00ZPIERQUIN, 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.Model Order Reduction of Electrical Machines with Multiple Inputs
http://hdl.handle.net/10985/11834
FARZAM FAR, Mernhaz; BELAHCEN, Anouar; RASILO, Pavo; CLENET, Stéphane; PIERQUIN, Antoine
Transactions on Industrial Applications
In this paper, proper orthogonal decomposition method is employed to build a reduced-order model from a high-order nonlinear permanent magnet synchronous machine
model with multiple inputs. Three parameters are selected as the multiple inputs of the machine. These parameters are terminal current, angle of the terminal current, and rotation angle. To produce the lower-rank system, snapshots or instantaneous system states are projected onto a set of orthonormal basis functions with small dimension. The reduced model is then validated by comparing the vector potential, flux
density distribution, and torque results of the original model, which indicates the capability of using the proper orthogonal decomposition method in the multi-variable input problems. The developed methodology can be used for fast simulations of
the machine.
Wed, 01 Mar 2017 00:00:00 GMThttp://hdl.handle.net/10985/118342017-03-01T00:00:00ZFARZAM FAR, MernhazBELAHCEN, AnouarRASILO, PavoCLENET, StéphanePIERQUIN, AntoineIn this paper, proper orthogonal decomposition method is employed to build a reduced-order model from a high-order nonlinear permanent magnet synchronous machine
model with multiple inputs. Three parameters are selected as the multiple inputs of the machine. These parameters are terminal current, angle of the terminal current, and rotation angle. To produce the lower-rank system, snapshots or instantaneous system states are projected onto a set of orthonormal basis functions with small dimension. The reduced model is then validated by comparing the vector potential, flux
density distribution, and torque results of the original model, which indicates the capability of using the proper orthogonal decomposition method in the multi-variable input problems. The developed methodology can be used for fast simulations of
the machine.Comparison of DEIM and BPIM to Speed up a POD-based Nonlinear Magnetostatic Model
http://hdl.handle.net/10985/11757
HENNERON, Thomas; MONTIER, Laurent; PIERQUIN, Antoine; CLENET, Stephane
Transactions on Magnetics
Proper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite Element (FE) problems, and thus the computational time associated with. When considering a nonlinear behavior law of the ferromagnetic materials, the POD is
not so efficient due to the high computational cost associated to the nonlinear entries of the full FE model. Then, the POD approach must be combined with an interpolation method to efficiently deal with the nonlinear terms, and thus obtaining an efficient reduced model. An interpolation method consists in computing a small number of nonlinear entries and interpolating the other terms. Different methods have been presented to select the set of nonlinear entries to be calculated. Then, the (Discrete) Empirical Interpolation method ((D)EIM) and the Best Points Interpolation Method (BPIM) have been developed. In this article, we propose to compare two reduced models based on the POD-(D)EIM and on the POD-BPIM in the case of nonlinear magnetostatics coupled with electric equation.
Fri, 27 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/117572017-01-27T00:00:00ZHENNERON, ThomasMONTIER, LaurentPIERQUIN, AntoineCLENET, StephaneProper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite Element (FE) problems, and thus the computational time associated with. When considering a nonlinear behavior law of the ferromagnetic materials, the POD is
not so efficient due to the high computational cost associated to the nonlinear entries of the full FE model. Then, the POD approach must be combined with an interpolation method to efficiently deal with the nonlinear terms, and thus obtaining an efficient reduced model. An interpolation method consists in computing a small number of nonlinear entries and interpolating the other terms. Different methods have been presented to select the set of nonlinear entries to be calculated. Then, the (Discrete) Empirical Interpolation method ((D)EIM) and the Best Points Interpolation Method (BPIM) have been developed. In this article, we propose to compare two reduced models based on the POD-(D)EIM and on the POD-BPIM in the case of nonlinear magnetostatics coupled with electric equation.Structure Preserving Model Reduction of Low Frequency Electromagnetic Problem based on POD and DEIM
http://hdl.handle.net/10985/11758
MONTIER, Laurent; PIERQUIN, Antoine; HENNERON, Thomas; CLÉNET, Stéphane
Transactions on Magnetics
The Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Method (DEIM) can be used to reduce the computation time of the solution of a Finite Element (FE) model. However, it can lead to numerical instabilities. To increase
the robustness, the POD_DEIM model must be constructed by preserving the structure of the full FE model. In this article, the structure preserving is applied for different potential formulations used to solve electromagnetic problems.
Thu, 02 Feb 2017 00:00:00 GMThttp://hdl.handle.net/10985/117582017-02-02T00:00:00ZMONTIER, LaurentPIERQUIN, AntoineHENNERON, ThomasCLÉNET, StéphaneThe Proper Orthogonal Decomposition (POD) combined with the (Discrete) Empirical Interpolation Method (DEIM) can be used to reduce the computation time of the solution of a Finite Element (FE) model. However, it can lead to numerical instabilities. To increase
the robustness, the POD_DEIM model must be constructed by preserving the structure of the full FE model. In this article, the structure preserving is applied for different potential formulations used to solve electromagnetic problems.Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
http://hdl.handle.net/10985/12497
PIERQUIN, Antoine; HENNERON, Thomas; CLENET, stephane
Transactions on magnetics
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/124972017-01-01T00:00:00ZPIERQUIN, AntoineHENNERON, ThomasCLENET, stephaneAmong 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.Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
http://hdl.handle.net/10985/12997
PIERQUIN, Antoine; HENNERON, Thomas; CLENET, Stephane
Transaction on Magnetics
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, StephaneAmong 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.