Failure behavior of horseshoe-shaped tunnel in hard rock under high stress: Phenomenon and mechanisms

A particle flow code (PFC) was first applied to examining the mechanical response of a horseshoe-shaped opening in prismatic rock models under biaxial compression. Next, an improved complex variable method was proposed to derive the stress distribution around the opening. Lastly, a case study of tunnel failure caused by rock burst in Jinping II Hydropower Station was further analyzed and discussed. The results manifest that a total of four types of cracks occur around the opening under low lateral confining stress, namely, the primary-tensile cracks on the roof-floor, sidewall cracks on the sidewalls, secondary-tensile cracks on the corners and shear cracks along the diagonals. As the confining stress increases, the tensile cracks gradually disappear whilst the spalling failure becomes severe. Overall, the failure phenomenon of the modelled tunnel agrees well with that of the practical headrace tunnel, and the crack initiation mechanisms can be clearly clarified by the analytical stress distribution.     

Study on the relationship between the particle size distribution and the effectiveness of the K‐powder fire extinguishing agent

The relationship between the particle size distribution and the extinguishing effectiveness of the new K‐powder fire extinguishing agent has been studied experimentally, to explore the reason of the great extinguishing efficiency exhibited by the new K‐powder fire extinguishing agent on Class B fire (liquid fuel fire). The results of the experiment showed that the extinguishing effectiveness increased along with the decrease of the particle size distribution. In addition, a sharp discontinuity appeared around the limiting size, about 40 μm. The powder with the particle size below 40 μm exhibited highly effective extinguishing with the minimum effective extinguishing concentration C_xr = 23 g·m^?3, while the powder with the particle size above 40 μm exhibited little fire extinguishing efficiency. Compared with other fire extinguishing agents produced by different substances, the new K‐powder fire extinguishing has the bigger limiting size. That means, in the same particle size distribution, the new K‐powder fire extinguishing agent contains more highly effective powder than others contain, and is more effective.     

Effect of pore characteristics of fine lignite on particle density in separation medium

Effect of porosity on particle density was analyzed, which can be a potential factor of low efficiency. The porosity of particle with different densities was detected by surface area analyzer. In addition, Three-dimensional X-ray micro-imaging system (3D-XRM) was used to analyze the spatial distribution of internal pores. Results show that mesoporous and microporosity content increase with the increase of particle density. Three-dimensional distribution of pores shows that the pores are very rich and show a good connection. The density model including porosity and medium density was established, which shows the effect of medium filling on the change of particle density.     

Fewer Defects in the Surface Slows the Hydrolysis Rate,Decreases the ROS Generation Potential,and Improves the Non‐ROS Antimicrobial Activity of MgO

Magnesium oxide (MgO) is recognised as exhibiting a contact‐based antibacterial activity. However, a comprehensive study of the impact of atomic‐scale surface features on MgO's antibacterial activity is lacking. In this study, the nature and abundance of the native surface defects on different MgO powders are thoroughly investigated. Their impacts on the hydrolysis kinetics, antibacterial activity against Escherichia coli (ATCC 47076), Staphylococcus epidermidis and Pseudomonas aeruginosa and the reactive oxygen species (ROS) generation potential are determined and explained. It is shown that a reduction in the abundance of low‐coordinated oxygen atoms on the surface of the MgO improves its resistance to both hydrolysis and antibacterial activity. The ROS generation potential, determined in‐situ using a fluorescence microplate assay and electron paramagnetic resonance spectroscopy, is not an inherent property of the studied MgO, rather it is a side product of hydrolysis (only for the most highly defected MgO particles) and/or a consequence of the MgO/bacteria interaction. The evaluation of the mutual correlations of the hydrolysis, the antibacterial activity and the ROS generation, with their origin in the surface defects' peculiarities, led to the conclusion that the acid/base reaction between the MgO surface and the bacterial wall contributes considerably to the MgO's antibacterial activity.     

基于EEMD能量熵和混合算法的电机轴承故障诊断

针对提升机电机轴承振动信号的非平稳特性和单一粒子群算法(PSO) 优化径向基函数（RBF）神经网络时存在网络收敛速度慢和适应度值易陷入局部最小的缺点，提出基于集合经验模态分解（EEMD）能量熵和模拟退火粒子群混合算法（SAPSO）优化RBF神经网络的提升机电机轴承故障诊断方法。基于EEMD求取振动信号各固有模态函数分量的能量熵，并使用相关性分析方法剔除虚假的分量，把筛选后的有效数据作为故障识别的特征向量；利用模拟退火（SA）算法具有局部概率突跳的特性，将SA算法和PSO算法相结合，在优化RBF诊断模型隐含层参数时以实现不同算法间的优劣互补。仿真结果表明，使用SAPSO算法优化后的RBF神经网络模型在提升机电机轴承故障诊断中能够加快网络收敛速度和提升故障识别精度。     

6061铝合金均匀化过程中AlMnSi弥散颗粒的析出尺寸对再结晶行为的影响

利用力学性能测试、光学显微镜、扫描电镜、透射电镜和能谱分析技术等手段研究了均匀化过程中AlMnSi弥散颗粒的析出尺寸对6061铝合金再结晶行为的影响。结果表明:AlMnSi弥散颗粒在均匀化过程中沿着铝基体[001]晶带轴析出长大,晶体结构为体心立方结构。随着均匀化温度的升高,弥散颗粒的析出尺寸逐渐增大,当AlMnSi弥散颗粒平均尺寸由0.07 μm增长到0.42 μm,最大尺寸由0.21 μm增大到1.12 μm时,力学性能则是先升高后降低。6061铝合金T6态组织仍为变形组织;AlMnSi弥散颗粒尺寸增大到1.12 μm,6061合金T6态组织中出现粗大再结晶,力学性能急剧降低。     

基于优化粒子数和分段权值的粒子群直线电机PID整定

以工程中直线电机伺服系统为研究对象,提出一种优化粒子数量加分段式惯性权重递减的粒子群PID控制器参数优化算法。优化粒子数量的方法可降低函数调用次数,通过对近两代的全局最优值进行比较,得到的误差值如果大于设定值,认为是在初始寻优阶段,保持粒子数量,否则在最终优化阶段,减少粒子数量,所减少的粒子特征是最接近最佳粒子的粒子,以保证在欧氏距离内实现粒子的分散性。最后再结合指数衰减曲线加线性递减曲线构成的分段式惯性权重递减策略提升算法的全局寻优和局部寻优能力。经数值验证分析,该优化算法在保证遍历性的同时,在一定程度上提高了算法的运行速度和寻优精度。实验仿真结果表明,该算法对PID控制器进行参数优化,直线电机系统响应速度快,超调量小,调节时间短。     

Effect of particle shape on methanol partial oxidation in a fixed bed using CFD reactor modeling

Particle shape is one of the most important parameters in the design and optimization of fixed-bed processes. To address the impact of particle shape on methanol partial oxidation to formaldehyde over molybdate catalyst, packings of spheres, cylinders, rings, and trilobes are numerically generated. The generated packings are used to carry out resolved particle Computational Fluid Dynamics (CFD) simulations under industrial conditions. Pressure drop, voidage and velocity profiles, radial heat transfer, and local and overall conversion and selectivity results are presented. Despite their lower particle surface area, lower particle effectiveness and more uneven flow distribution than trilobes, and lower overall heat transfer coefficient than cylinders, rings had the best conversion and selectivity due to their balance between the factors. Three longer tubes of rings, rings and cylinders, and rings and trilobes are simulated and show a small gain in selectivity for the rings and trilobes.     

Microfluidic synthesis of ultrasmall Co nanoparticles over reduced graphene oxide and their catalytic properties

We prepared a one-stage microfluidic-based method for continuous synthesis of cobalt (Co) nanoparticles over reduced graphene oxide (rGO) to produce Co/rGO composites. These were generated by the coreduction of Co²⁺ ions and GO with NaBH₄ which was confined within discrete aqueous plugs segmented by octane as continuous phase. Owing to the excellent transfer properties from recirculation in these plugs, ultrasmall Co nanoparticles were distributed homogeneously on the GO sheets without using any surfactants. As compared to batch methods, the average size of Co nanoparticles and the relative standard deviation decreased from 4.0 ± 1.42 nm and 35.9% to 2.0 ± 0.45 nm and 22.6%, respectively. The as-prepared Co/rGO composites exhibited superior activity towards the catalytic reduction of p-nitrophenol to p-aminophenol with NaBH₄ compared with Co nanoparticles and rGO; this enhanced activity could be attributed to the synergistic effect between Co nanoparticles and rGO.     

基于深度脊波神经网络的电力系统短期负荷预测模型

针对电力系统负荷的非线性预测问题,本文构造了一种基于深度脊波神经网络的电力系统短期负荷预测模型。该模型的隐含层采用脊波神经元,神经元的激励函数采用脊波变换函数。对该预测模型采用受限的玻尔兹曼机学习原理进行预训练,最后利用粒子群优化算法对其进行深度优化精调。通过对某地区实际电网负荷系统进行仿真预测,结果表明,与传统的BP神经网络、脊波神经网络和常规深度神经网络模型相对比,深度脊波神经网络预测模型的日平均绝对误差百分比分别降低了1.96%、1.12%和0.3%,日最大绝对误差分别降低了3.91%、2.19%和1.78%,验证了深度脊波神经网络预测模型具有较好的预测准确度和稳定性。