This paper proposes and evaluates a method that improves the adaptability and efficiency of genetic algorithms (GAs) when applied to the minimal loss reconfiguration problem. This research reduces the searching space (population) when a new codification strategy and novel genetic operators, called accentuated crossover and directed mutation, are used. This allows a drastic reduction of the computational time and minimizes the memory requirements, ensuring a efficiency search when compared to current GA reconfiguration techniques. The reduced population is created through the branches that form "system loops." This means that almost all individuals created for the GA are feasible (radial networks) generating topologies that can only be limited by the system's operational constraints. The results of the proposed reconfiguration method are compared with other techniques, yielding smaller or equal power loss values with less computational efforts.

In this paper a multi-physics model is proposed for a multi-V-shape Interior Permanent Magnet (IPM) motor with concentrated winding. This work develops a non-linear magnetic model that computes the flux density in the motor. The analytical model includes several novel aspects: it takes into account the local saturation near the iron bridges, it proposes a method for modeling the concentrated tooth winding, it calculates the slot tangential leakage flux and includes it in the flux linkage calculation. The magnetic model is coupled with an electrical model that computes the power factor and the voltage at the motor terminals. A loss model is developed in order to calculate the copper and the iron losses. They are used as inputs to a nodal thermal model that introduces a thermal circuit for concentrated end-winding and computes the temperature of the motor. A mechanical model is developed. It evaluates the mechanical constraints encountered by the structure. The models are verified using finite element computations and numerical calculations performed with dedicated software. Besides, the coupled analytical model is experimentally validated using a prototype motor. Finally, two multi-physics bi-objective optimizations are carried out in order to design the motor for high torque and low speed application.

This paper concerns the 3D simulation of corona discharge using high performance computing (HPC) managed with the message passing interface (MPI) library. In the field of finite volume methods applied on non-adaptive mesh grids and in the case of a specific 3D dynamic benchmark test devoted to streamer studies, the great efficiency of the iterative R&B SOR and BiCGSTAB methods versus the direct MUMPS method was clearly demonstrated in solving the Poisson equation using HPC resources. The optimization of the parallelization and the resulting scalability was undertaken as a function of the HPC architecture for a number of mesh cells ranging from 8 to 512 million and a number of cores ranging from 20 to 1600. The R&B SOR method remains at least about four times faster than the BiCGSTAB method and requires significantly less memory for all tested situations. The R&B SOR method was then implemented in a 3D MPI parallelized code that solves the classical first order model of an atmospheric pressure corona discharge in air. The 3D code capabilities were tested by following the development of one, two and four coplanar streamers generated by initial plasma spots for 6 ns. The preliminary results obtained allowed us to follow in detail the formation of the tree structure of a corona discharge and the effects of the mutual interactions between the streamers in terms of streamer velocity, trajectory and diameter. The computing time for 64 million of mesh cells distributed over 1000 cores using the MPI procedures is about 30 min ns ⁻¹ , regardless of the number of streamers.