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http://hdl.handle.net/10985/11834
Model Order Reduction of Electrical Machines with Multiple Inputs
FARZAM FAR, Mernhaz; BELAHCEN, Anouar; RASILO, Paavo; PIERQUIN, Antoine; CLENET, Stephane
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/118342017-01-01T00:00:00ZFARZAM FAR, MernhazBELAHCEN, AnouarRASILO, PaavoPIERQUIN, AntoineCLENET, StephaneIn 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.Structure Preserving Model Reduction of Low Frequency Electromagnetic Problem based on POD and DEIM
http://hdl.handle.net/10985/11758
Structure Preserving Model Reduction of Low Frequency Electromagnetic Problem based on POD and DEIM
MONTIER, Laurent; PIERQUIN, Antoine; HENNERON, Thomas; CLENET, Stephane
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/117582017-01-01T00:00:00ZMONTIER, LaurentPIERQUIN, AntoineHENNERON, ThomasCLENET, StephaneThe 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.Comparison of DEIM and BPIM to Speed up a POD-based Nonlinear Magnetostatic Model
http://hdl.handle.net/10985/11757
Comparison of DEIM and BPIM to Speed up a POD-based Nonlinear Magnetostatic Model
HENNERON, Thomas; MONTIER, Laurent; PIERQUIN, Antoine; CLENET, Stephane
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.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/117572017-01-01T00:00:00ZHENNERON, 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.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, Stephane
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.Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
http://hdl.handle.net/10985/12497
Data-Driven Model Order Reduction for Magnetostatic Problem Coupled with Circuit Equations
PIERQUIN, Antoine; HENNERON, Thomas; CLENET, Stephane
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.Model-Order Reduction of Magnetoquasi-Static Problems Based on POD and Arnoldi-Based Krylov Methods
http://hdl.handle.net/10985/9558
Model-Order Reduction of Magnetoquasi-Static Problems Based on POD and Arnoldi-Based Krylov Methods
PIERQUIN, Antoine; HENNERON, Thomas; BRISSET, Stéphane; CLENET, 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.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/95582015-01-01T00:00:00ZPIERQUIN, AntoineHENNERON, ThomasBRISSET, StéphaneCLENET, 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.Multirate coupling of controlled rectifier and non-linear finite element model based on Waveform Relaxation Method
http://hdl.handle.net/10985/10556
Multirate coupling of controlled rectifier and non-linear finite element model based on Waveform Relaxation Method
HENNERON, Thomas; PIERQUIN, Antoine; BRISSET, Stéphane; CLENET, Stephane
To study a multirate system, each subsystem can be solved by a dedicated sofware with respect to the physical problem and the time constant. Then, the problem is the coupling of the solutions of the subsystems. The Waveform Relaxation Method (WRM) seems to be an interesting solution for the coupling but until now it has been mainly applied on academic examples. In this paper, the WRM is applied to perform the coupling of a controlled rectifier and a non-linear finite element model of a transformer.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10985/105562016-01-01T00:00:00ZHENNERON, ThomasPIERQUIN, AntoineBRISSET, StéphaneCLENET, StephaneTo study a multirate system, each subsystem can be solved by a dedicated sofware with respect to the physical problem and the time constant. Then, the problem is the coupling of the solutions of the subsystems. The Waveform Relaxation Method (WRM) seems to be an interesting solution for the coupling but until now it has been mainly applied on academic examples. In this paper, the WRM is applied to perform the coupling of a controlled rectifier and a non-linear finite element model of a transformer.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, Stephane
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, StephaneWe 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.Mesh Deformation Based on Radial Basis Function Interpolation Applied to Low-Frequency Electromagnetic Problem
http://hdl.handle.net/10985/16535
Mesh Deformation Based on Radial Basis Function Interpolation Applied to Low-Frequency Electromagnetic Problem
HENNERON, Thomas; PIERQUIN, Antoine; CLENET, Stephane
In order to take into account a modification of the geometry during an optimization process or due to a physical phenomenon, a deformation of the elements of the spatial discretization is preferable to conserve a conformal mesh and to apply the Finite Element (FE) method. To perform the displacement of nodes, interpolation method can be investigated in this context. In this paper, the Radial Basis Function (RBF) interpolation method is applied for low frequency electromagnetic problems solved by the FE method.. A 2D magnetostatic example is considered to study the influence of the parameters of the RBF interpolation. To test the extension in 3D, a non destructive testing (NDT) problem is treated where the shape of the crack is modified by applying the proposed method.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/165352019-01-01T00:00:00ZHENNERON, ThomasPIERQUIN, AntoineCLENET, StephaneIn order to take into account a modification of the geometry during an optimization process or due to a physical phenomenon, a deformation of the elements of the spatial discretization is preferable to conserve a conformal mesh and to apply the Finite Element (FE) method. To perform the displacement of nodes, interpolation method can be investigated in this context. In this paper, the Radial Basis Function (RBF) interpolation method is applied for low frequency electromagnetic problems solved by the FE method.. A 2D magnetostatic example is considered to study the influence of the parameters of the RBF interpolation. To test the extension in 3D, a non destructive testing (NDT) problem is treated where the shape of the crack is modified by applying the proposed method.Optimisation process to solve multirate system
http://hdl.handle.net/10985/16567
Optimisation process to solve multirate system
PIERQUIN, Antoine; HENNERON, Thomas; BRISSET, Stephane; CLENET, Stephane
The modelling of a multirate system -composed of components with heterogeneous time constants- can be done using fixed-point method. This method allows a time-discretization of each subsystem with respect to its own time constant. In an optimisation process, executing the loop of the fixed-point at each model evaluation can be time consuming. By adding one of the searched waveform of the system to the optimisation variables, the loop can be avoided. This strategy is applied to the optimisation of a transformer.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/165672015-01-01T00:00:00ZPIERQUIN, AntoineHENNERON, ThomasBRISSET, StephaneCLENET, StephaneThe modelling of a multirate system -composed of components with heterogeneous time constants- can be done using fixed-point method. This method allows a time-discretization of each subsystem with respect to its own time constant. In an optimisation process, executing the loop of the fixed-point at each model evaluation can be time consuming. By adding one of the searched waveform of the system to the optimisation variables, the loop can be avoided. This strategy is applied to the optimisation of a transformer.