Magnetic losses in Si-Fe alloys for avionic applications

This paper presents an experimental analysis of the rotational power losses of the magnetic materials of transformers, motors and actuators used in avionic environment. A large frequency range is investigated using a suitable experimental test frame developed to measure the power losses for a circular magnetization. The results about different silicon iron alloys with different textures and thickness are considered and compared

Data for: Towards online evaluation of Goss-texture in grain-oriented ferromagnetic sheets.

The authors intend to share the scripts that implement the magnetic model of the cubic grain they have developed and used to perform the simulations reported

Magnetic losses in Si-Fe alloys for avionic applications

This paper presents an experimental analysis of the rotational power losses of the magnetic materials of transformers, motors and actuators used in avionic environment. A large frequency range is investigated using a suitable experimental test frame developed to measure the power losses for a circular magnetization. The results about different silicon iron alloys with different textures and thickness are considered and compared

Numerical simulations of vector hysteresis processes via the Preisach model and the Energy Based Model: An application to Fe-Si laminated alloys

The paper presents the state of the art and the problems already open in modelling the hysteresis phenomenon in 2-D for laminated soft ferromagnetic materials. Firstly, a thorough experimental investigation has been carried out at a very low frequency by a single disk tester (SDT) for a specimen of innovative NGO electrical steel sheet. Scalar, rotational and elliptic magnetization processes have been experimentally measured under controlled waveforms of the magnetic induction vector thanks to an effective digital feedback algorithm. Two numerical model of hysteresis have been taken into account to reproduce the measured magnetization processes: the vector Preisach model (VPM) and the Energy Based Model (EBM). The main advantages and limitations in the use of the two hysteresis models are comprehensively analysed and discussed, taking into consideration both the problem of identification and the simulation results. In particular, the effective capability of the models to reproduce the vector field trajectories and to predict the hysteresis power losses has been shown. Conclusive considerations involve the memory usage and the computational time for the low level of abstraction implementation of the two hysteresis models

An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms

A computationally efficient and robust neural network-based model to reproduce the hysteresis phenomenon for soft ferromagnetic alloys is here presented, as well as a dedicated procedure to generate a suitable training set from a minimal set of experimental data. Firstly, an accurate experimental verification has been performed for a commercial NGO electrical steel, measuring a family of hysteresis loops under sinusoidal and non-sinusoidal magnetic induction waveforms. The Preisach model of hysteresis, identified with the sinusoidal loops, has been exploited to generate a wider data set, which consists of a family of first-order reversal curves (FORCs), suitable to train the neural network. Then, a neural network-based hysteresis model, with the capability to reproduce the eventual presence of sub-loops, has been developed. The two simulation approaches have been validated taking into account the other experimental data, which consist of a family of hysteresis loops measured under different types of magnetic induction waveforms. The comparison between the Preisach model and the neural network-based model also covers the simulation of the waveforms found in magnetic systems supplied by pulse-width modulated (PWM) signals. The substantial agreement found indicates that the neural network model can replicate the behaviour of the Preisach model with a considerable advantage in terms of computational cost and memory allocation. In addition, the possibility to be quickly inverted makes the proposed method suitable for matching with FEM solvers

Comparison between different models of magnetic hysteresis in the solution of the TEAM 32 problem

The numerical modeling of magnetic materials in simulators is a difficult task, above all in real devices with specific excitation. The aim of this work is to compare the accuracy of scalar and vector Preisach models in a well know test benchmark: the TEAM 32 problem. The availability of measured data for this benchmark test and the simple geometry allow us to build hysteresis models and to test them in a 2D finite element analysis (FEA) scheme. The specific numerical formulation of each hysteresis model implemented is described, including the technique for the numerical identification of parameters starting from measured data. The comparison among the models is done in terms of accuracy, but also in terms of easy implementation in the FEA scheme and of computational cost

On the Use of Feedforward Neural Networks to Simulate Magnetic Hysteresis in Electrical Steels

The present investigation aims at the definition of an efficient and robust neural network-based model to simulate the magnetic hysteresis in performing magnetic alloys suitable for aircraft applications. Starting from a set of measured hysteresis loops, a convenient and effective method to train the network consists to identify the Preisach model and use it for the generation of the training set. The obtained neural network turned out to be particularly robust and able to reproduce the behaviour of the Preisach model with a significant reduction of the computational time. The comparative analysis between the two approaches takes into account different kinds of excitation waveforms

Deep neural networks for the efficient simulation of macro-scale hysteresis processes with generic excitation waveforms

An effective and performing hysteresis model, based on a deep neural network, with the capability to reproduce the evolution of magnetization processes under arbitrary waveforms of excitation is here presented. The proposed model consists of a standalone multi-layer feed-forward neural network, with reserved input neurons for the past values of both the input (H) and output (M), aiming at the reproduction of the storage mechanism typical of hysteretic systems. The training set has been opportunely prepared starting from a set of simulations, performed by the Preisach hysteresis model. The optimized training procedure, based on multi-stage check of the model performance, will be comprehensively discussed. The comparative analysis between the neural network-based model, implemented at low level of abstraction, and the Preisach model covers additional hysteresis processes, different from those involved in the training. The mild/moderate memory requirement and the significant computational speed make the proposed approach suitable for a future coupling with finite-element analysis

A moving approach for the Vector Hysteron Model

A moving approach for the VHM (Vector Hysteron Model) is here described, to reconstruct both scalar and rotational magnetization of electrical steels with weak anisotropy, such as the non oriented grain Silicon steel. The hysterons distribution is postulated to be function of the magnetization state of the material, in order to overcome the practical limitation of the congruency property of the standard VHM approach. By using this formulation and a suitable accommodation procedure, the results obtained indicate that the model is accurate, in particular in reproducing the experimental behavior approaching to the saturation region, allowing a real improvement respect to the previous approach