上升气泡内气溶胶沉降机理研究

气溶胶池洗过滤是反应堆严重事故中去除放射性源项的重要手段。本文以严重事故条件下上升气泡中气溶胶的滞留过程为背景，设计搭建了可视化单气泡鼓泡实验装置。通过该装置研究了气溶胶在上升气泡中的沉降效率，并与MELCOR中的气溶胶沉降模型计算结果进行了对比。结果表明，气溶胶沉降效率对气泡尺寸的变化较为敏感，当气体流量大于0.1 L/min时，气泡等效直径迅速增加，相应的气溶胶沉降效率快速降低；与MELCOR模型计算结果的对比表明，两者在总体趋势上呈现出较好的一致性，但计算结果低估了液相对气溶胶的实际去除能力，导致这种偏差的主要原因是气泡在上升过程中存在无规则的晃动以及气液界面的波动。     

Low-temperature synthesis of CdCu3Ti4O12 powders with high dielectric permittivities

The molten salt method was adopted in this work to synthesize CdCu₃Ti₄O₁₂ precursor powders and was found capable of notably reducing the sintering temperature. It was also found that the grain size gradually increases and the grain shape changes from spherical to cubic with increasing proportions of molten salt. The optimal sintering conditions were found to be at approximately 730?°C with the addition of 20% molten salt. After further sintering at 975?°C, the obtained perovskite ceramics exhibited a high dielectric constant at low frequencies.     

Preparation of high-activity coal char-based catalysts from high metals containing coal gangue and lignite for catalytic decomposition of biomass tar

The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications.     

永磁同步电机双矢量模型预测磁链控制

针对传统模型预测转矩控制定子磁链和转矩脉动大、计算负担重、权重系数调节繁杂的问题,提出了一种基于定子磁链矢量跟踪误差最小化的双矢量模型预测磁链控制方法。首先,将传统模型预测中定子磁链和转矩误差的价值函数转化为对定子磁链跟踪误差的价值函数,避免了权重系数的整定;接着基于无差拍思想预测出参考电压矢量,减轻了处理器计算负担;然后为进一步改善系统稳定性,引入了双矢量占空比计算方法;最后,基于磁链矢量跟踪误差最小化原理,确定所选电压矢量的持续时间。通过实验验证所提控制策略的正确性与有效性,结果表明相较于传统模型预测转矩控制,所提出的双矢量模型预测磁链控制提高了永磁同步电机控制系统的动、稳态运行性能。     

09CrCuSb方坯表面裂纹形成机制分析

针对某钢厂生产的09CrCuSb钢连铸方坯表面裂纹严重的现象,通过金相显微镜和扫描电镜对裂纹试样进行观察,并通过裂纹宏观形态分析,研究了表面裂纹产生原因.试验结果表明:表面裂纹缺陷是由于卷渣氧化和Cu元素富集造成的,且内弧和外弧容易产生裂纹.可以通过炼钢脱氧工艺的优化,净化钢水质量,适宜的连铸拉速控制,以及控制轧制温度来进行改进.     

传承大国山河规画传统：新时代开展 国土景观规划的基本任务

中国国土空间辽阔,历史悠久,长期追求并维系着大 一统的局面。从国土空间规划角度看,以都城为核心的城市体 系(城邑天下)是国土空间结构的统率,城市体系与行政体系高 度吻合,与交通网络相辅相成,与大国山河相得益彰,为统一 多民族国家的塑造提供了基本的空间骨架,共同在广域国土空 间控制与社会治理中发挥枢纽作用。在当前建立国土空间规划 体系并监督实施的过程中,亟需传承大国山河规画传统,突出 城市体系枢纽地位,合理组织“人居-生态-农业”空间,缔造 美丽国土与美好人居     

基于COSMO-RS方法筛选离子液体用于焦油脱除

焦油的脱除是生物质气化规模化应用的难题。离子液体具有饱和蒸气压低、分子结构可设计等优势,在催化和吸收领域有广泛的应用前景,但在脱除焦油方面鲜有探索。以改进的COSMO-RS方法为基础,借助COSMOtherm软件推算离子液体对苯、甲苯、苯酚和萘等焦油模拟物的无限稀释活度系数。并进一步通过偏摩尔过量焓验证以上筛选结果。结果表明,优选的两取代基咪唑类离子液体对四种焦油模拟物的无限稀释活度系数值主要分布在0.4~1之间,预计具有良好的吸收性能。当阴离子相同时,两取代基咪唑阳离子随着R₁位置烷基侧链的增长吸收性能变好,其中[C₈MIM][NTf₂]表现出了较佳性能,吸收苯、甲苯和萘的γ^∞分别为0.95、1.24和1.36,但此时离子液体黏度较大;对于苯酚体系,[BF₄]^-阴离子性能较佳。     

航空发动机转子组件电子束焊变形预测

航空商用发动机组件尺寸精度要求较高,且组件焊接后盘心位置不能进行机械加工,因此采用数值模拟的方法对转子组件进行焊接变形预测. 试验将基于热弹塑性理论计算提取固有应变数值;通过理论计算得到GH4169合金电子束焊缝的固有应变值,分析焊接工艺参数对焊缝固有应变的影响规律;建立结构件模型,分析焊接工艺、焊接顺序及工装条件下组件焊接变形. 结果表明,相比于第一,第二和第三级盘,第四级盘心轴向变形最大,且通过增大扫描速度和改变约束方式的方法可以有效减小焊接变形,从而完成对转子组件焊接变形进行预测和控制.     

Erratum to: Laser-Aided Direct Writing of Nickel-Based Single-Crystal Super Alloy (N5)

Metallurgical and Materials Transactions A -     

Face Alignment by Explicit Shape Regression

We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 min for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.