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https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 19 Apr 2024 21:13:53 GMT2024-04-19T21:13:53ZMultirate 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; CLENET, Stéphane; PIERQUIN, Antoine; BRISSET, Stéphane
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, ThomasCLENET, StéphanePIERQUIN, AntoineBRISSET, StéphaneTo 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.Enhanced Meta-model Based Optimization under Constraints using Parallel Computations
http://hdl.handle.net/10985/13417
Enhanced Meta-model Based Optimization under Constraints using Parallel Computations
EL BECHARI, Reda; BRISSET, Stéphane; CLENET, Stéphane; MIPO, Jean-Claude
Meta-models proved to be a very efficient strategy for optimization of expensive black-box models, e.g. Finite Element simulation for electromagnetic devices. It enables to reduce the computational burden for optimization purposes. Kriging is a popular method to build meta-model. Its statistical properties were firstly used in efficient global optimization for unconstrained problems. Afterwards many extensions were introduced in the literature to deal with constrained optimization. This paper presents a comparative study of some infill criteria for constraints handling and a new strategy for parallelization of the expensive computations of models. TEAM workshop problem 22 is taken as an electromagnetic test problem.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/134172017-01-01T00:00:00ZEL BECHARI, RedaBRISSET, StéphaneCLENET, StéphaneMIPO, Jean-ClaudeMeta-models proved to be a very efficient strategy for optimization of expensive black-box models, e.g. Finite Element simulation for electromagnetic devices. It enables to reduce the computational burden for optimization purposes. Kriging is a popular method to build meta-model. Its statistical properties were firstly used in efficient global optimization for unconstrained problems. Afterwards many extensions were introduced in the literature to deal with constrained optimization. This paper presents a comparative study of some infill criteria for constraints handling and a new strategy for parallelization of the expensive computations of models. TEAM workshop problem 22 is taken as an electromagnetic test problem.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.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.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; CLENET, Stéphane; BRISSET, Stéphane
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, ThomasCLENET, StéphaneBRISSET, StéphaneThe 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.Iterative Kriging-based Methods for Expensive Black-Box Models
http://hdl.handle.net/10985/12996
Iterative Kriging-based Methods for Expensive Black-Box Models
DENG, Siyang; EL BECHARI, Reda; BRISSET, Stéphane; CLENET, Stéphane
Reliability-Based Design Optimization (RBDO) in electromagnetic field problems requires the calculation of probability of failure leading to a huge computational cost in the case of expensive models. Three different RBDO approaches using kriging surrogate model are proposed to overcome this difficulty by introducing an approximation of the objective function and constraints. These methods use different infill sampling criteria (ISC) to add samples in the process of optimization or/and in the reliability analysis. Several enrichment criteria and strategies are compared in terms of number of evaluations and accuracy of the solution.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/129962018-01-01T00:00:00ZDENG, SiyangEL BECHARI, RedaBRISSET, StéphaneCLENET, StéphaneReliability-Based Design Optimization (RBDO) in electromagnetic field problems requires the calculation of probability of failure leading to a huge computational cost in the case of expensive models. Three different RBDO approaches using kriging surrogate model are proposed to overcome this difficulty by introducing an approximation of the objective function and constraints. These methods use different infill sampling criteria (ISC) to add samples in the process of optimization or/and in the reliability analysis. Several enrichment criteria and strategies are compared in terms of number of evaluations and accuracy of the solution.