Lu Liu

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.

The performance optimization and reliability of electromagnetic orbital launchers need to take into account the uncertainties that exist in the manufacturing and service processes. Considering that it is difficult to identify the problem of the parameter probability distribution in advance for electromagnetic orbital launchers, this paper uses the interval number to describe the uncertain variables and realizes the conversion of uncertainty optimization into deterministic optimization problems based on interval sequential relations; moreover, it establishes interval uncertainty optimization methods. The converted deterministic optimization problem is solved by combining a high-precision proxy model with an optimization algorithm to search for the optimal solution set. Finally, reliability estimation is achieved by taking the armature of the electromagnetic orbital launcher as the optimization object. The computational example proves that the method is able to deal with the optimization problem of the parameter interval of an electromagnetic orbital launcher containing uncertain parameters and has good engineering applicability.

In view of the significant computational resources required for numerical simulation of electromagnetic comprehensive performance in modern electrical equipment, and the security and transaction fairness issues of existing delegated computation schemes, we propose a delegated computation scheme under the blockchain framework that is suitable for numerical simulation of electromagnetic comprehensive performance. This scheme combines the multi-level data characteristics of electromagnetic field numerical simulations with practical needs. Firstly, we propose a method for handling multi-layered data in delegated computation. Secondly, we design a delegated computation architecture that involves at least three participants and a detailed transaction mechanism, and develop three smart contracts to ensure data privacy protection and transaction fairness in delegated computation. Finally, the feasibility of the proposed scheme is proved by delegated computations for “Solving the Poisson equation of two-dimensional electrostatic field” and “Optimization of amorphous alloy transformer”. The research results have achieved a reliable and credible solution to the large-scale data computing problem in the field of numerical simulation of electromagnetic comprehensive performance, and provided a new theory and solution for data security in this field.