Vacuum induction melting gas atomization (VIGA) has evolved as an important production technique of superalloy powders used in additive manufacturing. However, the development of powder preparation techniques is limited because the crushing process of gas-atomized metal melt is difficult to characterize by conventional experimental methods. Herein, we report the application of computational fluid dynamics to simulate the breaking behavior of droplets in the process of preparing nickel-based superalloy powders by VIGA, as well as the results on the effect of gas pressure on the atomization process and powder particle size distribution of metal melt. In the process of primary atomization, the crushing morphology of superalloy melt shows an alternate transformation of umbrella shapes and inverted mushroom cloud shapes, and with the increase in atomization pressure, the disorder of the two-phase flow field increases, which is conducive to sufficient breakage of the melt. Most importantly, in the process of secondary atomization and with the increasing atomization pressure, the particle size distribution becomes narrower, the median particle diameter and average particle size decrease, and the decreasing trend of the particle size increases gradually. The simulation results are compliant with the performed nickel-based superalloy powder preparation tests. This study provides insight into the production and process optimization of superalloy powder prepared by the VIGA method.

Battery parameters identification is crucial for accurate prediction of battery life in electric vehicles. In order to optimize battery parameter identification, an improved PSO (particle swarm optimization) algorithm was proposed based on the use of Thevenin battery model to abstract the problem into an optimization problem. The experimental results show that the computational accuracy of the improved PSO algorithm is higher than that of the genetic algorithm and the original PSO algorithm, and the battery parameters calculated by the improved PSO algorithm are also more accurate.

Additive manufacturing (AM) technology has the advantages of designability, short process times, high flexibility, etc., making it especially suitable for manufacturing complex high-performance components for high-end industrial systems. However, the intensive temperature gradients caused by the rapid heating and cooling processes of AM can generate high levels of residual stresses, which directly affect the precision and serviceability of the components. Taking Inconel 690 alloy, which is widely used in nuclear power plants, as the research object, a thermo-coupled mechanical model of temperature field and residual stress field of directed energy deposition (DED) of Inconel 690 was established based on ABAQUS 2019 finite element software to study the influence of process parameters on the temperature history and the distribution of residual stresses in the DED process. The experimental results show that the peak temperature of each layer in the fabrication process increases with the increase in laser power and preheating temperature, and decreases with the increase in scanning speed and interlayer dwell time. Substrate preheating only has a large effect on the peak temperature of the first four layers. Residual stresses are mainly concentrated in the upper and middle parts, the bottom of the substrate, and the sides combined with the substrate, and the residual stresses increase with the increasing laser power and decrease with the increasing interlayer dwell time. Decreasing laser power, longer dwell time, higher preheating temperature, and appropriate scanning speed are beneficial for the reduction in residual stresses in Inconel 690 components. This research has important significance for the process design and residual stress modulation in the additive manufacturing of Inconel 690 alloy.

With the increasing demand for energy resource, the development of marine energy harvesting technology has attracted widespread attention. Herein, a spherical electret wave power generator is designed and constructed for ocean wave energy harvesting. Based on electrostatic induction from electret material, the generator converts wave energy into electric energy through the bias voltage of the folded electret power generation unit. With ultra-low excitation frequency at 2Hz, the peak-to-peak output voltage and output power can be reached up to around 300 V and 20 mW, respectively. Compared with other traditional wave energy generators, the utilization of space in the sphere is improved by the folded structure, which also has the advantages in terms of simple structure, high-energy conversion efficiency, light weight and small size. The generator is also capable of collecting low-frequency vibration energy in random directions, as well as realizing large-scale power supply after deploying an array of structures.