Atomistic simulations of functionalization of aramid fiber-epoxy nanocomposite

Owing to its high degree of crystallinity and orientation, the surface of aramid fiber is smooth, causing its low bonding strength with polymer matrix. This has restricted the application of aramid fiber in reinforced polymer materials. Effective methods are by introducing functional groups through surface modification and by increasing its surface roughness thereby greatly improving its bonding strength with the polymer. In this work, molecular dynamics (MD) simulation study fiber functionalized with hydroxyl (OH), carboxyl (COOH), and the silane coupling agent as nanofillers for polymer nanocomposites. The interfacial characteristics and the mechanical behavior of polymer nanocomposites are investigated. The results show that the functionalization can enhance the interfacial shear stress and tensile strength. The functional group not only provides a stronger interface, but also provides additional mechanical interlocking effect, which effectively improves load-bearing transmission capacity. The analysis of the micro-mechanism from the energy level also provides new insights for the functionalized design of nanocomposites.     

Hierarchically porous N-doped carbon nanofibers derived from ZIF-8/PAN composites for benzene adsorption

Coupling with electrospinning technique, metal–organic-frameworks (MOFs)-derived porous carbon fibers exhibit a great potential application in the adsorption of volatile organic compounds (VOCs) because of their huge surface area, high porosity, as well as sufficient heteroatom-doped active sites. In this work, the hierarchically porous N-doped carbon nanofibers are obtained after the pyrolysis of zeolite imidazole framework-8 and polyacrylonitrile (ZIF-8/PAN) composite fibers synthesized by electrospinning method. The N-doped carbon nanofibers fabricated in N₂ atmosphere (N-CF-N₂) present an enhanced adsorption capacity of 694 mg/g for benzene because of the synergistic effect of the hierarchically porous structure and the abundant N-species-containing active sites. It is also interesting that the N-doped hierarchical carbon nanofibers fabricated in Ar atmosphere (N-CF-Ar) exhibit a low benzene adsorption as compared with the N-CF-N₂, which can be attributed to the porous structure damage caused by the bombardment of heavy Ar atoms on the pore shells during the pyrolysis. These results not only show a promising application of the as-fabricated N-CF-N₂ in adsorption of VOCs for air purification due to its merit of cost-efficient, large-scale production, and excellent adsorption capacity, but also expand the potential of electrospinning technology and composite fibers in volatile organic gas adsorption.     

Antibacterial self-healing anticorrosion coatings from single capsule system

It is highly desirable to develop self-healing anticorrosion coatings with enhanced antibacterial function to prevent the scratched area to be fouled or corroded in harsh environments. Herein, we report antibacterial self-healing anticorrosion coatings via the simple incorporation of the easily synthesized single polymer microcapsule system. Well-defined polymer microcapsules containing isophorone diisocyanate (IPDI) as a healing agent and 4,5-dichloro-2-n-octyl-4-isothiazolin-3-one (DCOIT) as antibacterial molecules were synthesized by one-pot polymerization. The diameter and core fraction were around 30 μm and 90%, respectively. The active DCOIT content in the core material could be precisely controlled by adjusting the DCOIT/IPDI feeding ratio. The DCOIT/IPDI microcapsules-embedded protective coating exhibits an adaptive self-healing anticorrosion property, as shown by electrochemical test under the condition of the salt-water immersion. Furthermore, the self-healing coating showed efficient antibacterial function against Escherichia coli and Pseudomonas aeruginosa, which is due to the released active biocide molecules on the damaged surfaces. In contrast to other systems, this single capsule system without any catalyst is perspective for extending the service time of the antibacterial self-healing materials in harsh environment.     

Engineering all-aromatic polyamide surface from hydrophilic to superhydrophobic and the accelerated strategy

Being a new kind of nanomaterials, aromatic polyamide nanofibers (ANF) have been much highlighted in recent studies. We here demonstrate an isopropyl alcohol (IPA) accelerated chemical cleavage on poly (p-phenylene terephthalamide) chopped fibers, which provides an efficient preparation method of ANF. The comprehensive study on the processes accelerated by different alcohols revealed that the preparation time of ANF in the mixed medium of dimethyl sulfoxide (DMSO)-alcohol (20:1 in volume) was shorten to 45 min and 75 min for methanol (ethanol) and isopropanol, respectively. However, the nanofibers prepared in DMSO-IPA exhibited the minimum in axial and radial dimensions, providing the finest and most uniform diameter of 16 nm. The corresponding ANF films through vacuum assisted filtration also showed the highest tensile strength of 150 MPa, in comparison with those of the ANF films prepared using other alcohols, which were about 110 MPa. Furthermore, ANF/silicon hybrid films were prepared by the ionic ring-opening reaction followed by the alkoxysilane condensation and nanoparticle fabrication. By changing the organo functional groups in the alkoxysilane, the surface of the films were adjustable in a wide contact angle range from 56° (hydrophilic) to 150° (superhydrophobic), suggesting the amendable interfacial properties potential applicable to composite fabrication with most of the resin matrix.     

基于改进深度残差收缩网络的电力系统暂态稳定评估

针对电力系统暂态稳定评估中,电力系统同步相量测量装置(PMU)量测数据在采集和传输过程可能存在噪声问题,以及由于暂态稳定与失稳样本不平衡,导致基于数据驱动的暂态稳定评估模型训练的倾向性和误判后果严重问题,提出基于改进深度残差收缩网络(IDRSN)的电力系统暂态稳定评估方法.首先将底层量测电气量构建成特征图形式作为模型输入,利用模型深层结构建立输入与稳定结果之间的映射关系.面对噪声问题,模型通过注意力机制,采用软阈值函数自动学习噪声阈值,减小噪声及无关特征干扰;并通过焦点损失函数(FL),引入权重系数修正模型训练的倾向性,利用调制因子重点关注误分类样本,提高模型训练效率和评估性能.通过新英格兰10机39节点系统进行仿真分析,所提模型能够有效减小不同程度的噪声干扰,在不平衡数据集上修正模型训练偏向性,以减少误分类样本,在不同PMU配置方案下,均取得较好评估效果.     

两种功能化硅氧基聚酰亚胺薄膜的高频沿面放电寿命对比研究

聚酰亚胺可用作高频电力变压器的包覆绝缘材料,但要面临因沿面放电而可能导致的绝缘失效问题,可借助于材料改性技术解决.该文通过引入1％、2％、5％摩尔含量的1,3?双(3?氨基丙基)?1,1,3,3?四甲基二硅氧烷(GAPD)和等效含量的SiO2,分别对聚酰亚胺薄膜进行分子结构改性和纳米复合改性,以两种方法实现其硅氧功能化,并基于紫外光谱、扫描电子显微镜(SEM)、电阻率等材料理化特性测试的对比研究,重点考核材料的高频沿面放电寿命.结果表明,两种改性方式均可获得聚酰亚胺较优异的热学、力学等基本理化特性,加入5％SiO2后,薄膜电寿命可提升至纯聚酰亚胺的3.4倍;而引入5％GAPD后,薄膜电寿命可提升至纯聚酰亚胺的4.77倍.两种改性方式的影响机制有所差异:加入SiO2,是将Si?O?Si结构的晶体添加至聚酰亚胺中形成纳米复合体系,并通过界面等作用提升材料的高频沿面放电耐受性能;引入GAPD,是将均匀分布、不分相的Si?O?Si网络键入至聚酰亚胺中,使基体结构更致密,同时可在外层被破坏后形成均匀的絮状无机粒子,多因素协同作用更有效地提升了材料的高频沿面放电寿命.利用GAPD对聚酰亚胺进行分子结构改性以实现其硅氧功能化,可获得更加优异的材料理化特性和放电耐受性能,具有潜在应用价值.     

分段长初级双边直线感应电动机建模分析

分段长初级双边直线感应电动机(LP-DSLIM)系统由若干段单元电机串联或并联组成,为准确描述次级运动过程中单元电机的推力等状态变化和过分段时的暂态过程,该文提出了一种新型单元电机数学模型及分段LP-DSLIM系统模型.首先,对分段LP-DSLIM系统的特点进行分析,通过引入虚拟次级板将每段单元电机耦合的次级延长至与初级等长,以使单元电机初级、次级之间始终保持全耦合;然后,结合耦合因数得到实际次级磁链和初级磁链,以此推导得到新型单元电机数学模型,并基于此建立串联供电和并联供电的分段LP-DSLIM系统模型;最后,有限元计算和实验结果验证了提出的新型单元电机数学模型和分段LP-DSLIM系统模型的正确性.     

高功率密度SiC静止无功补偿器强迫风冷散热综合建模及优化设计方法

为了提高大功率高功率密度电力电子装置热设计的准确性和设计效率,该文综合热传导、对流换热与流体力学理论,针对电力电子装置的典型强迫风冷散热系统,提出基于截面积二次方根为无量纲特征长度的综合热模型,并提出一种强迫风冷散热系统体积最优的优化设计方法.以380V/50kvar高功率密度静止无功补偿器(SVG)为例,比较所提出的综合热模型与温升实验的热阻,综合热模型平均热阻误差在7％以内,即在SVG满载运行时,散热器平均表面温升的绝对误差ΔTe在1.3℃以内.该文提出的综合热模型相对于传统热模型精度提升62％,证明了综合热模型的准确性.基于所提出的优化方法设计的散热系统体积4.03L,对比传统方法设计的体积5.7L,体积缩小30％,从而证明了该文提出优化设计方法的可行性.     

光储微网系统多目标协调控制策略

基于光伏储能+电采暖的光储微网系统可以有效解决西部农村社区清洁能源供应量不足问题.为了提高光储微网系统的能量控制效果和工程实用性能,该文通过构建包含电力费用、储能系统荷电状态(SOC)、供暖舒适度等变量的多目标函数,提出一种计算量较低的储能与电热负荷多目标协调控制策略.为了简化目标函数求解过程,引入求导和单调性分析等优化方法.为了降低系统计算量,以住宅建筑保温性能为评价标准,提出一种带宽能量控制模式,降低了被控单元的控制频率,提高了算法的工程应用价值.搭建Matlab/Simulink仿真平台,依据西部青海省某示范光储微网系统实际数据,仿真对比分析基于传统能量控制策略和基于多目标协调控制策略的光储微网系统性能,验证该文所提控制策略的有效性和优越性.     

基于深度学习增强的LSD杆塔倾斜度检测

杆塔的倾倒会对整个电网产生严重的破坏并威胁周围居民的生命安全,电力巡检无人机利用计算机视觉对杆塔进行巡检既节省了人力资源又显著地提高了电网的巡检效率.为了国网巡检人员在杆塔倾倒前及时得到预警,对电力巡检无人机中的基于计算机视觉的杆塔倾斜检测算法进行了研究,设计了一种基于YOLOv3的深度神经网络结合LSD线段提取方法对杆塔的倾斜进行检测.利用在山西电网无人机实际巡检的杆塔图片制作杆塔的VOC2007数据集并利用YOLOv3神经网络对杆塔进行目标检测,并将检测后得到的Bounding box根据网络训练后的mIOU参数进行微调后作为LSD检测的ROI.接着,该方法在ROI中将检测的线段进行过滤和融合,根据杆塔特点进行杆塔的二次识别.最后利用得到的杆塔外边线做出该方向上的杆塔中线并计算杆塔在该方向的倾斜度.该文中实验利用山西国网电力公司提供的数据进行验证,结果表明,杆塔的倾斜检测效果在各种拍摄高度和背景干扰下都较为精确,杆塔目标的正确识别率达到97％,倾斜度检测平均误差小于0.85°.