全组分木质活性炭球的制备及其乙烯吸附性能研究

目的 制备全组分木质活性炭球,应用于乙烯吸附,筛选出乙烯吸附效果最好的活性炭球制备工艺。方法 以木材液化物为原料,探究不同的固化时间对制备的全组分木质活性炭球微观形貌的影响,利用场发射扫描电子显微镜观察其微观结构,通过氮气吸附-解析等温线计算样品孔径分布及其比表面积;使用气相色谱仪,结合乙烯吸附标准曲线,分析时间及吸附剂用量对4种活性炭球吸附乙烯性能的影响。结果 随着固化时间的增加,碳球表面趋于光滑、内部结构越来越致密,石墨化程度逐渐提高,比表面积和孔容逐渐减小。当固化时间为0.5 h时,比表面积高达2 073 m²/g,乙烯吸附量高达197.99 mg/g,约3 h达到吸附平衡。结论 全组分木质活性炭球能有效吸附乙烯气体,固化时间太长,活性炭球内部孔隙减少,不利于吸附乙烯气体。活性炭球孔容越大,乙烯吸附效果越好。     

梁柱半刚性连接对钢结构整体抗震性能的影响研究

本文研究空间钢结构力学模型中,梁柱之间纯刚性连接和不同程度的半刚性连接对整体结构抗震性能的影响.以一阶横向振动周期、结构梁柱轴力比和层间位移限角为评价指标,在实测地震波的激励下,采用ANSYS软件分析横向弯曲和轴向扭转刚度对塔形钢结构整体抗震性能的影响.仿真分析表明,横向弯曲刚度对结构抗震性能的影响显著,从而为钢结构建筑的设计和分析提供参考.     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

Vortex analysis and flow field optimization for the particle classifier with three rotary cages

The particle classifiers with three rotary cages have significant advantage in powder handling capacity. The flow field inside the classifiers were investigated from the perspective of vortex using Q criterion. Formation process and distribution of the vortices were intuitively exhibited. Structure of the guide cone was further optimized, and the classifying performance of the classifiers was evaluated. The results show that complex vortex structures were formed inside the classifier. The large-scale columnar vortex under the guide cone oscillates irregularly. This unwanted vortex is eliminated by extending the guide cone. The structure of the guide cone has little effect on the cut size, but the optimized guide cone with the long cylinder and cone significantly enhances the separation degree of the fine and coarse particles. The classifier obtains finer silica powder with a median size of 2.5 μm and higher Newton efficiency about 71.5%.     

岩体结构的可靠性设计

考虑岩体性质的空间变异性和随机性,本文讨论了基于可靠度理论的岩体结构的可靠性设计。分析了岩体结构的可靠性设计的研究现状,提出了面临的问题和相应的措施。     

梁组合结构的模态分析

对工程中常见的梁组合结构的固有模态进行了理论和测试分析,得到了梁组合结构固有模态随单梁根数变化的一般规律。     

限矩式离心联轴器

提出一种新型限矩式离心联轴器,说明了设计要点。该联轴器结构简单,有较好的应用前景。     

论株洲产业结构的调整与优化

从株洲产业结构的现状与特征出发,对如何进一步调整优化株洲的产业结构进行了探讨,提出了调整优化的产业结构的基本方针和对策。     

Main-chain complementarity in protein-protein recognition

We present a statistical analysis of protein structures basedon interatomic C_a distances. The overall distance distributionsreflect in detail the contents of sequence-specific substructuresmaintained by local interactions (such as -helixes) and longerrange interactions (such as disulfide bridges and ß-sheets).We also show that a volume scaling of the distances makes distancedistributions for protein chains of different length superimposable.Distance distributions were also calculated specifically foramino acids separated by a given number of residues. Specificfeatures in these distributions are visible for sequence separationsof up to 20 amino acid residues. A simple representation, whichpreserves most of the information in the distance distributions,was obtained using six parameters only. The parameters giverise to canonical distance intervals and when predicting coarse-graineddistance constraints by methods such as data-driven artificialneural networks, these should preferably be selected from theseintervals. We discuss the use of the six parameters for determiningor reconstructing 3-D protein structures.