Model Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods

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

Non Linear Proper Generalized Decomposition method applied to the magnetic simulation of a SMC microstructure

Improvement of the magnetic performances of Soft Magnetic Composites (SMC) materials requires to link the microstructures to the macroscopic magnetic behavior law. This can be achieved with the FE method using the geometry reconstruction from images of the microstructure. Nevertheless, it can lead to large computational times. In that context, the Proper Generalized Decomposition (PGD), that is an approximation method originally developed in mechanics, and based on a finite sum of separable functions, can be of interest in our case. In this work, we propose to apply the PGD method to the SMC microstructure magnetic simulation. A non-linear magnetostatic problem with the scalar potential formulation is then solved

Error estimation of a proper orthogonal decomposition reduced model of a permanent magnet synchronous machine

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.This work is supported by the IAP7/M2E2S (Belgium state) and MEDEE pole supported by the region council of Nord Pas de Calais (France) and the European Community

Predetermination of Currents and Field in Short-Circuit Voltage Operation for an Axial-Flux Permanent Magnet Machine

Risk of irreversible magnet demagnetization during short-circuit fault is analyzed in case of an axial-flux dual-rotor machine, using a three-dimensional finite-element method (3D-FEM). In order to validate the numerical model, calculated waveforms of the currents are compared with experimental results for short-circuit at low speeds. Then currents and magnetic flux density inside the magnets are computed for short-circuit at higher speeds in order to predetermine the maximum admissible speed for the machine

Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems

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

Non Linear Proper Generalized Decomposition method applied to the magnetic simulation of a SMC microstructure

Improvement of the magnetic performances of Soft Magnetic Composites (SMC) materials requires to link the microstructures to the macroscopic magnetic behavior law. This can be achieved with the FE method using the geometry reconstruction from images of the microstructure. Nevertheless, it can lead to large computational times. In that context, the Proper Generalized Decomposition (PGD), that is an approximation method originally developed in mechanics, and based on a finite sum of separable functions, can be of interest in our case. In this work, we propose to apply the PGD method to the SMC microstructure magnetic simulation. A non-linear magnetostatic problem with the scalar potential formulation is then solved

Suramin protects from cisplatin-induced acute kidney injury.

In rodent models, suramin improves recovery following acute kidney injury (AKI). We hypothesized that suramin would be useful in protection against cisplatin-induced AKI. This hypothesis was tested by pre-treating C57BL/6j mice with suramin prior to cisplatin. Our data indicates that suramin protects the kidney from injury by decreasing cisplatininduced decreases in kidney function. Renal histology indicated that suramin significantly protected from cisplatin-induced AKI. Data indicate that suramin pretreatment attenuated mRNA expression of pro-inflammatory cytokines and chemokines following cisplatin treatment, while also decreasing markers of cell stress and cell death. We utilized the same experimental design with 10-month old FVB mice expressing mutant Kras driven lung adenocarcinomas. Assessment of lung histology and kidney function indicated that suramin protected mice from cisplatin-induced AKI and did not inhibit cisplatin’s anti-tumor efficacy. These results suggest that suramin shows great potential as a renoprotective agent for the treatment/ prevention of cisplatininduced AKI