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  1. Lorenzis, Laura (Ed.)
    Green’s function characterizes a partial differential equation (PDE) and maps its solution in the entire domain as integrals. Finding the analytical form of Green’s function is a non-trivial exercise, especially for a PDE defined on a complex domain or a PDE with variable coefficients. In this paper, we propose a novel boundary integral network to learn the domain independent Green’s function, referred to as BIN-G. We evaluate the Green’s function in the BIN-G using a radial basis function (RBF) kernel-based neural network. We train the BIN-G by minimizing the residual of the PDE and the mean squared errors of the solutions to the boundary integral equations for prescribed test functions. By leveraging the symmetry of the Green’s function and controlling refinements of the RBF kernel near the singularity of the Green function, we demonstrate that our numerical scheme enables fast training and accurate evaluation of the Green’s function for PDEs with variable coefficients. The learned Green’s function is independent of the domain geometries, forcing terms, and boundary conditions in the boundary integral formulation. Numerical experiments verify the desired properties of the method and the expected accuracy for the two-dimensional Poisson and Helmholtz equations with variable coefficients. 
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    Free, publicly-accessible full text available March 1, 2025
  2. It is very important to perform magnetostatic analysis accurately and efficiently when it comes to multi-objective optimization of designs of electromagnetic devices, particularly for inductors, transformers, and electric motors. A kernel free boundary integral method (KFBIM) was studied for analyzing 2D magnetostatic problems. Although KFBIM is accurate and computationally efficient, sharp corners can be a major problem for KFBIM. In this paper, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal inductor core with an airgap (C-core) is used to show the effectiveness of the proposed approach for addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out using KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method. 
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  3. null (Ed.)
    This study presents a time-efficient modelling approach for dynamic behavior and efficiency analysis of a Switched Reluctance Machines (SRM). It employs a hybrid model combining Simulink, finite element analysis (FEA), and hardware measurements to create an accurate behavioral model of the machine. In order to enhance accuracy of the estimated performance, Steinmetz equation is employed to characterize core loss in the machine across different operating points. This approach serves as a template for developing a time-efficient model to analyze performance of any SRM with a high degree of accuracy. Simulation and experimental results are used to show effectiveness of the proposed approach. 
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  4. null (Ed.)
    This study presents a computationally cost-effective modeling approach for a switched reluctance machine (SRM) towards predicting vibration and acoustic noise. In the proposed approach, the SRM is modeled using Finite Element (FE) software for capturing magnetic snapshots from static simulations. Using an advanced field reconstruction method (FRM), these snapshots are used to develop basis functions to estimate magnetic fields under any arbitrary stator excitation and at any desired rotor position. This method includes magnetic properties of the machine and can estimate flux density at once instead of partially predicting it. The vibration model is built in FE software while the acoustic noise is predicted using the analytical method. The proposed study can significantly reduce the computational time for vibration and noise analysis with decent accuracy. Dynamic simulation by finite-element analysis (FEA) software and experimental verification have been carried out to verify the effectiveness of the proposed hybrid model. 
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  5. null (Ed.)
    This paper presents a comparative evaluation of power electronic control approaches for vibro-acoustic noise reduction in High Rotor-Pole Switched Reluctance Machines (HR-SRM). It carries out a fundamental analysis of approaches that can be used to target acoustic noise and vibration reduction. Based on the comprehensive study, four candidates for control have been identified and applied to the HR-SRM drive to evaluate their effectiveness and identify challenges. These four methods include phase advancing, current shaping based on field reconstruction, and random hysteresis band with and without spectrum shaping. The theoretical background, implementation, and vibro-acoustic noise reduction performance of each method are presented in detail. Comparative studies from simulation and experimental measurements have been used to identify the most effective solution to acoustic noise and vibration reduction in HR-SRM configuration. 
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  6. null (Ed.)
  7. Switched reluctance motors (SRM) have been seen as a potential candidate for automotive, aerospace as well as domestic applications and High-Rotor pole SRM (HR-SRM) present a significant advancement in this area. This machine configuration offers most of the the benefits offered by conventional SRMs and has shown significant benefits in efficiency and torque quality. However, HR-SRM has a narrower inductance profile with a lower saliency ratio as compared to a conventional SRM with an identical stator. This can make it inherently challenging to directly adopt mathematical models and sensorless control approaches currently in use. This paper presents a time-efficient analytical model for the characterization of a 6/10 SRM using an inductance model utilizing truncated Fourier series as well as multi-order polynomial curve-fitting algorithm. The inductance model is extended to accurately predict back-EMF and electromagnetic torque response towards obtaining a comprehensive model for every operating point of the machine during dynamic operation. The effectiveness of the proposed concept has analyzed for a prototype machine and verified using Finite Element Analysis (FEA). 
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