Juheng Song

Linear propulsion electromagnetic energy equipment utilizes the Lorentz force to apply power to the emitter, achieving launch speeds and efficiencies that far surpass those of traditional chemical propulsion technologies. This capability has significant strategic and practical value in both defense and civilian applications. Analyzing and calculating the armature electromagnetic thrust is essential for studying the dynamic characteristics of linear propulsion electromagnetic energy equipment. However, high-speed scenarios often present challenges in obtaining numerical solutions due to “pseudo-oscillation.” To address this issue, we propose a novel prediction method for the armature electromagnetic thrust in linear propulsion electromagnetic energy equipment using deep operator networks. First, a finite element simulation model of the system was developed to generate stable numerical simulation data under varying excitation currents. This model is then used to analyze the dynamic characteristics of the armature electromagnetic thrust and evaluate the stability of the numerical solutions. Based on the simulation data, we introduce a deep operator network-based prediction method for armature electromagnetic thrust. Finally, the prediction performance of the model is validated using linear propulsion electromagnetic energy equipment as a computational example. The results showed that the prediction error for the electromagnetic thrust on the test set was within 1%. After training, the model can calculate the electromagnetic thrust under different excitation currents in a matter of seconds, providing a fresh perspective for analyzing the dynamic characteristics of linear propulsion electromagnetic energy equipment.

Endowed with high sensitivity, fast response, and excellent flexibility, flexible piezoelectric sensors proffer substantial advantages for establishing efficacious contact with coarse object surfaces. They exhibit remarkable potential for application in wearable devices, motion state recognition, and device vibration monitoring. This paper introduces the development of a novel pliable piezoelectric nanocomposite harnessing modified lead zirconate titanate (PZT) and a flexible photosensitive resin. The PZT-based flexible piezoelectric sensors were fabricated using the light-curing molding technique. By establishing a sensor test platform, the performance parameters of the PZT-based sensors were obtained. Compared to conventional flexible piezoelectric sensors, the output voltage of the PZT-based sensors increased by 1.67 times under the same excitation, resulting in an improved response time and sensitivity. Moreover, the sensors demonstrated an excellent voltage output performance for recognizing human motion states and monitoring electrical equipment vibrations, both in real-time motion state changes and in core vibration under high-frequency operating conditions. These 3D-printed flexible piezoelectric sensors assume paramount significance across diverse domains, including motion science and real-time equipment condition monitoring.

TEAM Problem 35, a benchmark problem in the optimization of electrical equipment, is a multi-objective, multi-variable optimization problem involving uniform field optimization. This paper proposes a novel guided optimization algorithm to address this problem and achieve optimal magnetic field regulation. First, the deep learning method is used to predict the magnetic field of the solenoid; second, the generated magnetic field cloud map is screened to extract the magnetic field information of the more optimal solution with uniform magnetic field distribution by calculating the variance of the generated magnetic field map; finally, the magnetic field features are combined with the particle swarm optimization algorithm; specifically, the variance of the calculated magnetic field map is added to the objective function of the optimization algorithm to strengthen the optimization process. This integration improves the convergence speed of the optimization process and ensures the continuity and stability of the obtained Pareto solutions. To optimize the solenoid, this paper considers the uniformity of magnetic field distribution and power loss as the primary optimization objectives. The radius of the coil is chosen as the design variable. The optimization results are compared with those obtained by conventional optimization algorithms, demonstrating superior optimization performance.