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Analytical target cascading (ATC) is a hierarchical multi‐level design methodology. According to the state‐of‐the‐art, it is confirmed that for problems with unattainable targets, strict design consistency cannot be achieved with finite weighting factors. This paper aims to address these issues.

A new formulation is proposed to improve the ATC convergence. The weighted sum of deviation metric is transformed into a multi‐objective formulation. An original optimization problem with a single global optimal solution is used as a benchmark.

It is found that carrying out an industrial application to design optimally a tram traction system demonstrates the efficiency of the proposed solution.

This paper is of value in showing how to improve the convergence of a multi‐level optimization algorithm by best management of the consistency constraints.

The paper highlights the process of electric vehicles optimal design as an inverse problem and presents the global constrained optimization as the best way to solve it.

The electric vehicle optimal design is carried out by a new approach. It consists an electric vehicle design model managed by constrained optimization techniques. It includes sizing models for all drive train components and a vehicle dynamic model build in a new “design way” as an energy‐based model using the response surface methodology. The sensitivity of first simple sizing models can be evaluated by the experimental design method, giving information about the most important part of the model that must be improved.

The result shows the superiority of the constrained optimization technique that treats simultaneously the global optimization and the model adjustment. This method of simultaneous resolution is much more powerful than the successive resolution of each subproblem. The proposed “design approach” used for electric vehicle optimal design offer a large potential in the field of the complex systems design.

The electric vehicle design process is treated on a vehicle design model based on a design approach. It allows determining the drive train components specifications for imposed vehicle performances, taking into account the dynamic model of the vehicle and all components interactions. Furthermore, considering fine components sizing models, the components can be sized taking into account the whole system behavior in an optimal global design.

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.

The Children's Hearings System is a Scottish welfare-based tribunal-based system in which decisions are made about the care and protection of children in conflict with the law and/or in need of additional care and protection. The Covid-19 pandemic resulted in the rapid implementation of a virtual Children's Hearings System. This system, which operated as the sole mechanism through which decisions were made between March and July 2020, continued to be used alongside in-person and hybrid Hearing formats for the duration of the pandemic. Early research into the use of virtual Hearings identified that their use presented significant barriers to participation, particularly in relation to the impacts of digital literacy and digital poverty. However, much of this research focused upon the experiences of adult participants in Hearings and failed to capture the experiences of children. In this chapter, we present findings from a qualitative study designed to explore the impact of virtual Hearings upon the participation and rights of children. In doing so, we demonstrate that virtual Hearings acted as both a barrier and facilitator of children's participation.

This paper aims to investigate three low-evaluation-budget optimization techniques: output space mapping (OSM), manifold mapping (MM) and Kriging-OSM. Kriging-OSM is an original approach having high-order mapping.

The electromagnetic device to be optimally sized is a five-phase linear induction motor, represented through two levels of modeling: coarse (Kriging model) and fine.The optimization comparison of the three techniques on the five-phase linear induction motor is discussed.

The optimization results show that the OSM takes more time and iteration to converge the optimal solution compared to MM and Kriging-OSM. This is mainly because of the poor quality of the initial Kriging model. In the case of a high-quality coarse model, the OSM technique would show its domination over the other two techniques. In the case of poor quality of coarse model, MM and Kriging-OSM techniques are more efficient to converge to the accurate optimum.

Kriging-OSM is an original approach having high-order mapping. An advantage of this new technique consists in its capability of providing a sufficiently accurate model for each objective and constraint function and makes the coarse model converge toward the fine model more effectively.

The purpose of this paper is to propose a methodology to seek the optimal topology of electromagnetic devices using the density method while taking into account the non-linear behaviour of ferromagnetic materials. The tools and methods used are detailed and applied to a three-dimensional (3D) electromagnet for analysis and validation. Resulting topologies with and without the non-linear behaviour are investigated.

The polynomial mapping is used with the density method for material distribution in the optimisation domain. To consider the non-linear behaviour of the materials, an analytical approximation based on the Marrocco equation is used and combined with the polynomial mapping to solve the problem. Furthermore, to prevent the occurrence of intermediate materials, a weighted sum of objectives is used in the optimisation problem to eliminate these undesired materials.

Taking into account the non-linear materials behaviour and 3D model during topology optimisation (TO) is important, as it produces more physically feasible and coherent results. Moreover, the use of a weighted sum of objectives to eliminate intermediate materials increases the number of evaluations to reach the final solution, but it is efficient.

Considering non-linear materials behaviour yields results closer to reality, and physical feasibility of structures is more obvious in absence of intermediate materials.

This work tackles an obstacle of TO in electromagnetism which is often overlooked in literature, that is, non-linear behaviour of ferromagnetic materials by proposing a methodology.

The purpose of this paper is to present a new approach to optimizing the design of 3D magnetic circuits. This approach is based on topology optimization, where derivative calculations are performed using the continuous adjoint method. Thus, the continuous adjoint method for magnetostatics has to be developed in 3D and has to be combined with penalization, filtering and homotopy approaches to provide an efficient optimization code.

To provide this new topology optimization code, this study starts from 2D magnetostatic results to perform the sensitivity analysis, and this approach is extended to 3D. From this sensitivity analysis, the continuous adjoint method is derived to compute the gradient of an objective function of a 3D topological optimization design problem. From this result, this design problem is discretized and can then be solved by finite element software. Thus, by adding the solid isotropic material with penalization (SIMP) penalization approach and developing a homotopy-based optimization algorithm, an interesting means for designing 3D magnetic circuits is provided.

In this paper, the 3D continuous adjoint method for magnetostatic problems involving an objective least-squares function is presented. Based on 2D results, new theoretical results for developing sensitivity analysis in 3D taking into account different parameters including the ferromagnetic material, the current density and the magnetization are provided. Then, by discretizing, filtering and penalizing using SIMP approaches, a topology optimization code has been derived to address only the ferromagnetic material parameters. Based on this efficient gradient computation method, a homotopy-based optimization algorithm for solving large-scale 3D design problems is developed.

In this paper, an approach based on topology optimization to solve 3D magnetostatic design problems when an objective least-squares function is involved is proposed. This approach is based on the continuous adjoint method derived for 3D magnetostatic design problems. The effectiveness of this topology optimization code is demonstrated by solving the design of a 3D magnetic circuit with up to 100,000 design variables.

For a given rotor, the study of the impact of stator MMF from different winding distributions is usually carried out using analytical model under some simplifying hypotheses to limit time computation. To get more accurate results, finite element model is thus more suitable. However, testing different combinations of stator windings with the same rotor can be tedious when considering the stator slots. Indeed, this introduces mesh constraint, reluctance variation of the air gap and possibly taking into account of the connection between stator coils. To avoid this, a current sheet supplied such to represent the stator MMF and spread all around the inner slotless stator surface can be used. In addition, such an approach can be very useful to didactically assess the effect of each winding space harmonic on machine performance separately. The purpose of this paper is to use a current sheet coupled to an external analytical tool in order to easily test different windings or to quantify the effect of a given spatial harmonic of the winding.

In the proposed approach, the current sheet supply is obtained from an analytical tool that allows determining the spatiotemporal stator MMF of any winding considered. Moreover, stator teeth height is not modelled, and only the thickness of the stator yoke is considered along with the same air gap thickness. Results with the proposed approach are compared to the real stator modelling for two different winding configurations. Last, linear and non-linear magnetic material behaviours are investigated to validate the proposed approach in term of magnetic distribution.

For both studied cases, results in term of local and global physical quantities show good agreement between the real stator modelling and the proposed approach.

Current sheet is used with finite element model to study the inherent effect of different winding configurations on local and global physical quantities of an AC electrical machine. The proposed approach avoids the constraints in terms of stator slot geometry and electrical circuit definition. This is very useful to quickly test different winding configurations or to isolate a specific winding space harmonic to quantify its effect on the electrical performances. This cannot be performed using classical modelling as all space harmonics are taken into account.

The purpose of this paper is to develop an algorithm and software for determining the size of a line-start permanent magnet synchronous motor (LSPMSMs) based on its optimization.

The software consists of an optimization procedure that cooperates with a FEM model to provide the desired behavior of the motor under consideration. The proposed improved version of the genetic algorithm has modifications enabling efficient optimization of LSPMSMs. The objective function consists of three important functional parameters describing the designed machine. The 2-D field-circuit mathematical model of the dynamics operation of the LSPMSMs consists of transient electromagnetic field equations, equations describing electric windings and mechanical motion equations. The model has been developed in the ANSYS Maxwell environment.

In this proposed approach, the set of design variables contains the variables describing the stator and rotor structure. The improved procedure of the optimization algorithm makes it possible to find an optimal motor structure with correct synchronization properties. The proposed modifications make the optimization procedure faster and more

This proposed approach can be successfully applied to solve the design problems of LSPMSMs.

The purpose of this paper is to present two optimization methodologies based on interval branch‐and‐bound algorithm.

These techniques decrease the total time of computation, even in spite of discrete nature of some of the design variables. Computational experiments performed on multivariable optimization problem reveal great accuracy and technical validity of developed approaches. As an example, the optimal design of the induction machine (IM) was treated, where the aim was to find the set of the most efficient and, at the same time, cheapest in the manufacturing configurations.

In this paper, two approaches were developed for resolving the problem of optimal design of IM with discrete variables. The strategy of constructing the meta‐models is utilized and put in practice. The methods show relatively high efficiency and robustness of obtained results.

These approaches are the core technics of the developed industrial application, which help identify the set of optimal configurations of IM with the criteria of optimality such as total cost of manufacturing and the efficiency of IM.