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To design an optimal active shield for the mitigation of the magnetic stray field around an induction heating device.

The active shield consists of several compensation coils in series and generates a counter field opposite to the main field. One extra compensation winding – the “generating compensation winding” (GCW) – is positioned close to the excitation coil and works as the secondary winding of a transformer. The power in this winding is used to drive the other compensation coils (the active shield), which are the load of the transformer. A circuit with passive components is inserted between the GCW and the other compensation coils. The shield is optimal if it achieves a high field reduction, while the energy dissipation is low. By using a genetic algorithm (GA) that minimizes an objective function, the optimization algorithm finds the optimal geometry and the optimal current for the GCW and the other compensation coils. The objective function uses time harmonic and axisymmetric finite element calculations.

The transformer driven active shield reduces the magnetic field effectively. It is cheap and easy to build, but it works well only for one frequency.

The shield is sensitive to tuning of the passive circuit and to changes in the frequency of the induction heater.

This transformer driven shield is an alternative for the classical active shield with external power supply.

An active shield that does not need an external power supply is a cheap solution for the shielding of magnetic fields.

Mechanical stress can heavily affect the magnetic behaviour law in ferromagnetic materials. This paper, aims to take into account the effect of mechanical stress into a hystreresis model. This model is implemented in a finite element analysis code and tested in the case of a simple system.

A simple extension of the classical Preisach model is proposed, in which a function linked to the Preisach density is parameterized using the mechanical stress as a supplementary parameter. The methodology is based on experimental measurements for identifying the required function. As a first approach, a linear interpolation is used between the measurements in order to have a continuous evolution of the magneto‐mechanical behaviour. This model has been tested in the case of a steel sheet in which width is not constant in order to obtain a non‐uniform distribution of stress and magnetic flux density.

The model can predict the magneto‐mechanical behaviour with a good accuracy in the case of tensile stress. Implementation of the model in finite element analysis has shown that the model can predict the behaviour of steel sheet subject to a non‐uniform stress distribution.

This paper shows that a classical hysteresis model can be extended to take into account the magneto‐mechanical behaviour. This is useful for the design of electrical machines which are subject to non‐negligible mechanical stress.

This paper deals with the numerical modelling of electromagnetic losses in electrical machines, using electromagnetic field computations, combined with advanced material characterisations. The paper gradually proceeds to the actual reasons why the building factor, defined as the ratio of the measured iron losses in the machine and the losses obtained under standard conditions, exceeds the value of 1.

This paper deals with the magnetic vector and scalar potential formulation for two‐dimensional (2D) finite element (FE) calculations including a vector hysteresis model, namely a vectorized Jiles‐Atherton model. The particular case of a current‐free FE model with imposed fluxes and magnetomotive forces is studied. The non‐linear equations are solved by means of the Newton‐Raphson method, which leads to the use of the differential reluctivity and permeability tensor. The proposed method is applied to a simple 2D model exhibiting rotational flux, viz the T‐joint of a three‐phase transformer.

The purpose of this article is expository in the main; critical to a lesser degree. It will attempt to show how Karl Marx, enraged by the imperfections and inhumanity of the capitalist society, “fought” for its supersession by the communist society on which he dwelt so fondly, that society which would emerge from the womb of a dying capitalism. It asks such questions as these: Is it possible to create the truly human society envisaged by Marx? Is perfection of man and society a mere will‐o'‐the‐wisp? A brief analysis, therefore, of the imperfections of capitalism is undertaken for the purpose of revealing the evils which Marx sought to eliminate by revolution of the most violent sort. In this sense, the nature of man under capitalism is analysed. Marx found the breed wanting, in a word, dehumanised. An attempt is, therefore, made to discuss the new man of Marxism, man's own creation, and the traits of that new man, one freed at last from the alienating effects of private property, division of labour, money, and religion. Another question that springs to mind is this: how does Marx propose to transcend alienation?

The common good refers to contextual conditions that contribute to human wellbeing and flourishing, such as prosperous communities and environmental sustainability. In this paper, we consider how entrepreneurship impacts society by investigating the generalized outcomes of social entrepreneurship on the common good. From a qualitative study of ten large and profitable social enterprises in the United Kingdom, we theorize how social entrepreneurship contributes to the common good in the short and long term. We also conjecture how some commercial practices undermine the common good and further, explain how the common good performs as a conceptual anchor for social entrepreneurship.

The influence of overlap joints in transformer cores on the local flux and eddy current distribution and on overall transformer characteristics is studied by means of two‐dimensional finite element (2D FE) models. A simplified 2D FE model of a single overlap joint is used for estimating the resulting increased magnetomotive force and increased eddy current losses. Both effects can be accounted for in a 2D FE model of the complete transformer by locally adopting modified material characteristics (viz. BH‐curve and electrical conductivity) in the cross‐section of the core. This novel method is demonstrated and validated by applying it to a three phase transformer. The calculated no‐load currents and losses are compared to the measured ones.

The aim of this paper is to reduce the evaluations number of the fine model within the output space mapping (OSM) technique in order to reduce their computing time.

In this paper, n-level OSM is proposed and expected to be even faster than the conventional OSM. The proposed algorithm takes advantages of the availability of n models of the device to optimize, each of them representing an optimal trade-off between the model error and its computation time. Models with intermediate characteristics between the coarse and fine models are inserted within the proposed algorithm to reduce the number of evaluations of the consuming time model and then the computing time. The advantages of the algorithm are highlighted on the optimization problem of superconducting magnetic energy storage (SMES).

A major computing time gain equals to three is achieved using the n-level OSM algorithm instead of the conventional OSM technique on the optimization problem of SMES.

The originality of this paper is to investigate several models with different granularities within OSM algorithm in order to reduce its computing time without decreasing the performance of the conventional strategy.

Magnetic material properties of an electromagnetic device (EMD) can be recovered by solving a coupled experimental numerical inverse problem. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the EMD need to be perfectly modeled using a complex numerical model. However, these fine models demand a high computational time. Alternatively, less accurate coarse models can be used with a demerit of the high expected recovery errors. The purpose of this paper is to present an efficient methodology to reduce the effect of stochastic modeling errors in the inverse problem solution.

The recovery error in the electromagnetic inverse problem solution is reduced using the Bayesian approximation error approach coupled with an adaptive Kriging-based model. The accuracy of the forward model is assessed and adapted a priori using the cross-validation technique.

The adaptive Kriging-based model seems to be an efficient technique for modeling EMDs used in inverse problems. Moreover, using the proposed methodology, the recovery error in the electromagnetic inverse problem solution is largely reduced in a relatively small computational time and memory storage.

The proposed methodology is capable of not only improving the accuracy of the inverse problem solution, but also reducing the computational time as well as the memory storage. Furthermore, to the best of the authors knowledge, it is the first time to combine the adaptive Kriging-based model with the Bayesian approximation error approach for the stochastic modeling error reduction.

The purpose of this paper is to present an application of a multidisciplinary multi-level design optimization methodology for the optimal design of a complex device from the field of electrical engineering throughout discipline-based decomposition. The considered benchmark is a single-phase low voltage safety isolation transformer.

The multidisciplinary optimization of a safety isolation transformer is addressed within this paper. The bi-level collaborative optimization (CO) strategy is employed to coordinate the optimization of the different disciplinary analytical models of the transformer (no-load and full-load electromagnetic models and thermal model). The results represent the joint decision of the three distinct disciplinary optimizers involved in the design process, under the coordination of the CO's master optimizer. In order to validate the proposed approach, the results are compared to those obtained using a classical single-level optimization method – sequential quadratic programming – carried out using a multidisciplinary feasible formulation for handling the evaluation of the coupling model of the transformer.

Results show a good convergence of the CO process with the analytical modeling of the transformer, with a reduced number of coordination iterations. However, a relatively important number of disciplinary models evaluations were required by the local optimizers.

The CO multi-level methodology represents a new approach in the field of electrical engineering. The advantage of this approach consists in that it integrates decisions from different teams of specialists within the optimal design process of complex systems and all exchanges are managed within a unique coordination process.