During the implementation of laser wireless power transmission (LWPT), various factors, such as clouds and atmospheric turbulence, can potentially result in nonuniform irradiation, leading to a power mismatch of the photovoltaic (PV) array. Dynamic reconfiguration technology which adopted in solar energy system is an effective method to improve the power output of PV arrays, but the current execution speed and accuracy cannot meet the fast response requirements of LWPT. In this article, a novel adaptive threshold iteration algorithm for irradiance equalization based on total-cross-tied (TCT) configuration is proposed. Based on this algorithm, a large number of data samples characterized and labeled by irradiance and optimal layout are obtained in the simulation platform. The irradiance values are then converted into current values of special nodes for the training of deep neural networks, which simplifies both model complexity and hardware complexity. A theoretical analysis of the simulation was performed on a 3×4 scale PV array, followed by the design of the hardware circuit and evaluation of the experimental results with metrics. The results show that the output power enhancement of the array can reach up to 63.29% compared to TCT-type configuration. The conversion efficiency is improved by 17.21%, and the reconfiguration execution time is about 61 ms.

In a long-distance wireless power transmission system with a non-uniform distribution of laser irradiation, it will significantly reduce the output power of the photovoltaic array, resulting in a large amount of power loss in the system and a decrease in conversion efficiency. This paper proposes an efficient and reliable optimal circuit connection algorithm for the 5 × 5 scale photovoltaic array. Under the laser illumination of 300 W, a 20 m wireless power transmission experiment was performed on four 5 × 5 scale photovoltaic arrays. The results show a 56.49% increase in the maximum output power of the 5 × 5 scale photovoltaic array.

Laser wire directed energy deposition (LWDED) has garnered significant attention for the fabrication of large metallic components. However, the complex coupling effects among its process parameters pose challenges for porosity control. Optimizing parameter combinations to effectively minimize porosity is therefore critical to the broader adoption of this technology. In this study, systematic experiments and modeling were conducted to optimize the LWDED process parameters and predict porosity. First, single-factor and orthogonal experiments were performed to evaluate the individual effects of laser power, scanning speed, wire feeding speed, and air pressure on porosity. Subsequently, range analysis and analysis of variance were employed to determine the influence of each parameter and the significance of their interactions. Four machine learning models—SVR, RF, GPR, and XGBoost—were then trained and compared. Among them, the SVR model exhibited the best predictive performance, achieving an R² of 0.8960, an RMSE of 0.19, and an MAE of 0.15, outperforming the other three models. Based on this, the SVR model was further utilized to establish the mapping between process parameters and porosity. Contour maps and three-dimensional surface plots were generated to visualize porosity variation patterns under interacting parameters. Validation experiments showed that the maximum relative error between model predictions and experimental measurements was 0.514%, with an average error of 0.251%. This study provides a reliable reference for selecting low-porosity parameter combinations in the LWDED fabrication of 5356 aluminum alloy components.

Fabricating parabolic moth-eye structures on silica optical diffusers via dewetting-assisted plasma etching effectively manipulates light transmission and scattering. The tunable structures enhance transmittance by up to 17.42% and haze by 23.64%.