Conference Agenda

Calibration methods based on electromagnetic coils are of great significance for the application of optically pumped magnetometer (OPM) arrays, as accurate calibration ensures the reliability of magnetic source analysis. However, analyzing the influence of the coil structure on the calibration results is always a challenging issue. In this paper, we investigate the design of coil arrays that consider the coupling effect between the magnetic shield and coil for the spatial position calibration of OPM sensors in a magnetocardiography measurement system. The basic frame of the coil was designed based on the structure of the cylindrical magnetic shield barrel, and the key geometric parameters affecting the calibration accuracy are also determined. We employed a multi-objective grey wolf optimization (MOGWO) algorithm to refine the cost function associated with position uncertainty, yielding satisfactory results.

The growing use of wireless power transfer technology demands the development of fast and optimized computational strategies for executing complex designs. In recent years, full-wave methods have gained prominence for providing accurate results in the optimization of electrical parameters and the geometric design of coils. However, this approach often faces challenges due to the high computational cost associated with handling large matrices. In this context, this work proposes a novel optimization methodology based on circuit theory for a new wireless power transfer system utilizing three solenoidal resonant coils. This system stands out by incorporating an intermediate coil that guides the magnetic field from the transmission stage to the reception stage. The results demonstrate that the proposed optimization achieves reduced computational time without compromising accuracy in terms of input impedance and scattering parameters. The proposed methodology serves as a precise and computationally efficient strategic solution, with the novel geometric design achieving a high-energy transfer rate.

This study presents a polarization insensitive, ultra-broadband metamaterial absorber designed for stability under oblique incident angles. The proposed metamaterial absorber consists of nickel resonator disks on a polytetrafluoroethylene substrate with a ground plane made of nickel. Numerical simulations performed using CST Studio Suite® confirm the performance of the absorber across a broad spectral range of 300–3000 nm, achieving an average absorption rate of 97.96% and a peak absorption of 99.72%. The proposed structure presents insensitivity to both transverse electric and transverse magnetic modes due to the symmetry of the unit cell geometry. The proposed absorber demonstrates stability under oblique incidence angles of up to 60°, offering high absorption efficiency, cost-effectiveness, and a thinner, simplified design.

To address the spatial structural un-continuity of topology optimization, this paper proposes a multi-material topology optimization approach for permanent magnet synchronous motor (PMSM) under consideration of manufacturing constraints. This method integrates normalized Gaussian network (NGnet) with differential evolution, embedding the K-means clustering algorithm to achieve effective multi-material topology optimization. The proposed mesh sequence cleaning algorithm is utilized to optimize structure topology by systematically eliminating micro-regions within the design domain. The obtained rotor structure with varying cleaning level parameters is subjected to analysis. Numerical examples illustrate that the proposed method not only ensures manufacturability of optimized design but also enhances average torque of PMSM rotor while reducing the volume of permanent magnet.

This paper proposes a multi-objective optimization design method for a split-tooth permanent magnet vernier motor (ST-PMVM) to enhance the motor's torque output and short-term overload capacity. The optimization process begins with a sensitivity analysis to classify design variables into sensitive and insensitive levels. Subsequently, the response surface model (RSM) is integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate a Pareto-optimal solution set for the sensitive variables, from which the optimal design variables are determined. Finite element analysis (FEA) is employed to compare the electromagnetic performance of the initial and optimal motor designs. The results show that the optimal ST-PMVM demonstrates a 13.6% enhancement in average torque, a 9.6% improvement in torque density, and a 68.8% and 120% extension in maximum operation time under 2 times and 2.5 times overload conditions, respectively, demonstrating the effectiveness of the proposed optimization method.

Topology design is the concept design of a product, which can create a novel topology of the product that might be “unthinkable” beforehand. Topology Optimization (TO) has now become the paradigm of the predominant engineering techniques to provide a quantitative design method for modern engineering design. Nevertheless, the extremely high dimensionality and overwhelmingly computational heavy burden are the main factors restricting the implementation of TOs in engineering practice. To address the aforementioned issues, a TO methodology based on material-field series expansion (MFSE) is proposed. Based on the MFSE, the structural topology is represented by a newly-introduced unbounded material field with spatial dependency and the TO problem is approximated as a constrained model with the truncated series expansion coefficients as the design variables, significantly reducing the dimensionality of design variables and inherently avoiding the checkerboard pattern without any additional measure. The numerical example is given to validate the feasibility and effectiveness of the proposed methodology and demonstrate its advantage for dimensionality and computational cost reductions in TO designs.

It is essential to have an accurate soil thermal resistivity to determine the real time ampacity to make full use of a power cable. However, the determination of the soil thermal resistivity involves the solution of an expensive multi-physical field inverse problem including a high-fidelity model, and the existing optimization methods still encounter some difficulties. In this respect, a reinforcement learning-based two-modes surrogate-assisted evolutionary algorithm (EA) is proposed. In the proposed algorithm, Mode 1 focuses on balancing the convergence and diversity while Mode 2 aims to accelerate the convergence. The search will adaptively switch between the two modes. Using the reinforcement learning, two adaptive evolutionary operator selection strategies are designed to select an operator for each individual in the two modes. Also, a new ensemble approach of surrogate models is proposed to ensure the solution accuracy and robustness. The superiorities of the proposed method are confirmed by the optimization results of a test function and a prototype cable.

This paper proposes a new topology optimization (TO) framework that employs closed Non-uniform rational B-spline curve (CNRBC)-controlled boundaries and curved triangle remeshing (CTR). The proposed CNRBC can represent the closed boundaries of material with nearly any shape and control the boundaries of material with few parameters. The proposed CTR can accurately describe the curve boundaries of CNRBC based on an initial fixed mesh, and the time-consuming remeshing for the whole domain is avoided. Moreover, the adaptive mesh refinement based on a posteriori error estimation is applied to ensure accuracy and efficiency. The proposed CNRBC-CTR is applied to the TO of a magnetic actuator to verify its effectiveness.

Nowadays the high torque density permanent magnet synchronous motors (PMSMs) are desired in a wide range of applications. The axial flux motor has receive intensive attention dur to its inherent merits in terms of high torque density and dynamic response. The multi-objective optimization was carried out for the two different topology motors, and their electromagnetic performance was compared.

Computational optimization using stochastic methods, such as metaheuristics, can help optimize real engineering problems. This paper presents an new algorithm inspired by the sea turtle life cycle - STO (Sea Turtle Optimization). The algorithm has been used and applied successfully to optimize continuous problems. This strategy is naturally hybrid, alternating phases of intensification and diversification during the search process. This methodology is applied on high-dimensional benchmark problems, and the results are better or similar than ones obtained by other strategies.

This paper focuses on optimizing the geometric parameters and shape of a permanent magnet synchronous motor (PMSM) through isogeometric analysis, aiming to streamline the design process by reducing the parameter space. Utilizing a variance-based Global Sensitivity Analysis (GSA), we efficiently pinpoint key parameters that significantly impact motor performance and cost. By leveraging GSA outcomes, we minimize the optimization space while maintaining robust design efficacy. Additionally, this analysis provides insights into the model’s behavior, which will enhance future optimization approaches.

In this work we deal with the shape optimization of an electric machine considering time-dependent effects such as eddy currents. The considered electric machine is an interior permanent magnet synchronous machine and we minimize the average dissipated power due to the eddy currents in the magnets over a period of time corresponding to a rotation. Our approach is based on the computation of the shape derivative which -- beside the computation of a sequence of state problems -- also involves solving a sequence of adjoint equations. The challenge of this problem is related to the dependency of each one of the N adjoint equations on two different time steps, because of the finite difference used in the eddy current calculations.