高速列车受电弓气动噪声分析与空腔降噪研究

随着高速列车运行速度的不断提高,受电弓气动噪声也愈加严重。针对这一问题,文中采用LES大涡模拟、边界层噪声源模型和FW-H声类比法,通过建立某型号受电弓局部1:1气动噪声分析模型进行数值模拟。文中研究了受电弓各部位的气动噪声贡献量,还探究了针对较大噪声位置空腔采用射流降噪方法的降噪效果。结果表明,当网格总数为4 323万个时,数值模拟精确度满足要求。受电弓空腔上游和空腔中部绝缘子是气动噪声的主要来源。在射流降噪前后,空腔内部气动噪声均为宽频带噪声,主要能量集中在0~4 500 Hz。对250 km·h^-1行驶速度下的空腔进行主动射流降噪,距列车25 m远处的垂向监测点声压级最小值为81.65 dB,比降噪前降低了2.64 dB。     

基于历史梯度平均方差缩减的协同参数更新方法

随机梯度下降算法(SGD)随机使用一个样本估计梯度,造成较大的方差,使机器学习模型收敛减慢且训练不稳定。该文提出一种基于方差缩减的分布式SGD,命名为DisSAGD。该方法采用历史梯度平均方差缩减来更新机器学习模型中的参数,不需要完全梯度计算或额外存储,而是通过使用异步通信协议来共享跨节点的参数。为了解决全局参数分发存在的“更新滞后”问题,该文采用具有加速因子的学习速率和自适应采样策略：一方面当参数偏离最优值时,增大加速因子,加快收敛速度;另一方面,当一个工作节点比其他工作节点快时,为下一次迭代采样更多样本,使工作节点有更多时间来计算局部梯度。实验表明：DisSAGD显著减少了循环迭代的等待时间,加速了算法的收敛,其收敛速度比对照方法更快,在分布式集群中可以获得近似线性的加速。     

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.     

基于深度学习的监督型典型相关分析

典型相关分析（CCA）是利用综合变量对之间的相关关系反映两组指标之间整体相关性的多元统计方法。传统的CCA方法无法有效利用样本的标签信息,导致准确率降低。将类信息融入到深度学习与CCA相结合的深度典型相关分析中,提出一种监督型降维方法DL-SCCA,用于处理带标签的非线性可分数据。在2个独立的深度神经网络（DNN）结构上,增加1个公共的输出维数与数据集类别数相同的全连接层,并且以softmax函数作为该层的激活函数,输出带有概率意义的编码向量。在此基础上,利用全连接输出与样本标签信息之间的交叉熵对DNN进行训练,获得分类性能较优的低维特征。实验结果表明,该方法采用最近邻分类器和网络本身结构得到的分类准确率分别为98.00%和97.82%,相比CCA、DisCCA、DCCA等方法,能够有效利用样本的标签信息,并且具有较优的分类性能。     

Study on the void reduction behaviour of porous asphalt pavement based on discrete element method

The objective of this study was to analyse the void reduction behaviour of porous asphalt mixture under load. A three-dimensional discrete element model of porous asphalt mixture based on aggregate gradation and void gradation was built in PFC3D software. The parameter of the model was obtained from creep test. The rutting test was simulated using this discrete element model. And a new method was developed to obtain and analyse the void structure in discrete element model. The simulation results were compared with one of the laboratory test. The comparative analysis indicates that, the discrete element method can be used to simulate the creep response and void reduction behaviour of porous asphalt mixture. Further research shows that porosity, effective porosity, number of connected components and section pores have a good correlation with strain of porous asphalt mixture. With the increase in strain, the proportion of section pores with diameter less than 2 mm increases. In the initial stage of loading, the void reduction is the main reason for rut increment of porous asphalt mixture. In the later stage, the void structure is almost incompressible; the lateral deformation of mixture becomes the domination factor.     

Displacement field denoising for high-temperature digital image correlation using principal component analysis

Principal component analysis (PCA) was extended to minimize the noise effect in digital image correlation (DIC) measurement under a high-temperature atmosphere environment. First, the principle of PCA was introduced, and the singular vectors and singular values for each component of the displacement fields from DIC were obtained. Then, the simulated high-temperature speckle images were developed to investigate the influences of noise on the DIC method under a high-temperature environment. Finally, the displacement fields of several special conditions were extracted from the simulated speckle images and experimental images; the effects of noise on the PCA were also analyzed.     

Mesoporous Germanium Anode Materials for Lithium‐Ion Battery with Exceptional Cycling Stability in Wide Temperature Range

Porous structured materials have unique architectures and are promising for lithium‐ion batteries to enhance performances. In particular, mesoporous materials have many advantages including a high surface area and large void spaces which can increase reactivity and accessibility of lithium ions. This study reports a synthesis of newly developed mesoporous germanium (Ge) particles prepared by a zincothermic reduction at a mild temperature for high performance lithium‐ion batteries which can operate in a wide temperature range. The optimized Ge battery anodes with the mesoporous structure exhibit outstanding electrochemical properties in a wide temperature ranging from ?20 to 60 °C. Ge anodes exhibit a stable cycling retention at various temperatures (capacity retention of 99% after 100 cycles at 25 °C, 84% after 300 cycles at 60 °C, and 50% after 50 cycles at ?20 °C). Furthermore, full cells consisting of the mesoporous Ge anode and an LiFePO₄ cathode show an excellent cyclability at ?20 and 25 °C. Mesoporous Ge materials synthesized by the zincothermic reduction can be potentially applied as high performance anode materials for practical lithium‐ion batteries.     

Engineering Bimetallic Ag–Cu Nanoalloys for Highly Efficient Oxygen Reduction Catalysts: A Guideline for Designing Ag‐Based Electrocatalysts with Activity Comparable to Pt/C‐20%

Development of highly active and stable Pt‐free oxygen reduction reaction catalysts from earth‐abundant elements remains a grand challenge for highly demanded metal–air batteries. Ag‐based alloys have many advantages over platinum group catalysts due to their low cost, high stability, and acceptable oxygen reduction reaction (ORR) performance in alkaline solutions. Nevertheless, compared to commercial Pt/C‐20%, their catalytic activity still cannot meet the demand of commercialization. In this study, a kind of catalysts screening strategy on Ag_x Cu_100?x nanoalloys is reported, containing the surface modification method, studies of activity enhancement mechanism, and applied research on zinc–air batteries. The results exhibit that the role of selective dealloying (DE) or galvanic displacement (GD) is limited by the “parting limitation”, and this “parting limitation” determines the surface topography, position of d‐band center, and ORR performance of Ag_x Cu_100?x alloys. The GD‐Ag₅₅Cu₄₅ and DE‐Ag₂₅Cu₇₅ catalysts alloys present excellent ORR performance that is comparable to Pt/C‐20%. The relationship between electronic perturbation and specific activity demonstrates that positive shift of the d‐band center (≈0.12 eV, relative to Ag) for GD‐Ag₅₅Cu₄₅ is beneficial for ORR, which is contrary to Pt‐based alloys (negative shift, ≈0.1 eV). Meanwhile, extensive electrochemical and electronic structure characterization indicates that the high work function of GD‐Ag₅₅Cu₄₅ (4.8 eV) is the reason behind their excellent durability for zinc–air batteries.     

Trimetallic PtCoFe Alloy Monolayer Superlattices as Bifunctional Oxygen‐Reduction and Ethanol‐Oxidation Electrocatalysts

A synthesis strategy for the preparation of trimetallic PtCoFe alloy nanoparticle superlattices is reported. Trimetallic PtCoFe alloy monolayer array of nanoparticle superlattices with a large density of high index facets and platinum‐rich surface are successfully prepared by coreduction of metal precursors in formamide solvent. The concentration of cetyl trimethyl ammonium bromide plays a vital role for the formation of a monolayer array of nanoparticle superlattices, while the size of nanoparticles is determined by NaI. The prepared monolayer array of nanoparticle superlattices is the superior catalyst for oxygen reduction reaction as well as for ethanol oxidation owing to their specific structural and compositional characteristics.