Fabrication of alumina ceramics with functional gradient structures by digital light processing 3D printing technology

Alumina ceramics with different unit numbers and gradient modes were prepared by digital light processing (DLP) 3D printing technology. The side length of each functional gradient structure was 10 mm, the porosity ratio was controlled to 70%, and the number of units were (1 × 1 × 1 unit) and (2 × 2 × 2 unit) respectively. The different gradient modes were named FCC, GFCC-1, GFCC-2 and GFCC-3. SEM, XRD, and other characterization methods proved that these gradient structures of alumina ceramics had only α-Al2O3 phase and good surface morphology. The mechanical properties and energy absorption properties of alumina ceramics with different functional gradient structures were studied by compression test. The results show that the gradient structure with 1 × 1 × 1 unit has better mechanical properties and energy absorption properties when the number of units is different. When the number of units is the same, GFCC-2 and GFCC-3 gradient structures have better compressive performance and energy absorption potential than FCC structures. The GFCC-2 gradient structure with 1 × 1 × 1 unit has a maximum compressive strength of 19.62 MPa and a maximum energy absorption value of 2.72 × 105 J/m3. The good performance of such functional gradient structures can provide new ideas for the design of lightweight and compressive energy absorption structures in the future.     

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

梯度铝蜂窝夹芯板的力学行为

梯度分层铝合金蜂窝板是一种有效的吸能结构,本工作在梯度铝蜂窝结构的基础上根据梯度率的概念,通过改变蜂窝芯层的胞壁长度,设计了4种质量相同、梯度率不同的铝蜂窝夹芯结构。通过准静态压缩实验,并结合非线性有限元模拟准静态及冲击态下梯度铝蜂窝夹芯结构的变形情况及其力学性能,分析对比了相同质量下梯度铝蜂窝夹芯结构在准静态下的变形模式以及冲击载荷下分层均质蜂窝结构和不同梯度率的分层梯度蜂窝结构的动态响应和能量吸收特性。结果表明:在准静态压缩过程中,铝蜂窝梯度夹芯板的变形具有明显的局部化特征,蜂窝芯的变形为低密度优先变形直至密实,层级之间的密实化应变差随芯层密度的增大而逐渐减小;在高速冲击下,梯度蜂窝板并非严格按照准静态过程中逐级变形直至密实,而是在锤头冲击惯性及芯层密度的相互作用下整体发生的线弹性变形、弹性屈曲、塑性坍塌及密实化;另外,在本工作所设计的梯度率中,当梯度率为γ₁=0.0276时,梯度蜂窝夹芯板的吸能性达到最好,相较于同等质量下的均质蜂窝夹芯板,能量吸收提高了10.63%。     

Effect of pressure on elemental diffusion in sulfur–iron reaction

The reaction between sulfur and iron under high pressure and high temperature (HPHT) was studied. Sulfur–iron reaction models under different pressure levels were constructed. The morphology and formation mechanism of the reactants were comprehensively analyzed by scanning electron microscopy, energy-dispersive spectroscopy–line scanning, metallographic microscopy, and Raman spectroscopy. The results indicated that the pressure of the reaction could significantly affect the diffusion behavior of sulfur and iron during the reaction. With an increase in pressure, the diffusion of iron in the system was inhibited, whereas that of sulfur was enhanced. The pressure distribution gradient at the reaction interface was simulated by finite element calculation. The effect of pressure gradient as the driving force of the reaction on the diffusion behavior of elements was evaluated by thermodynamics combined with experimental results. Based on the experimental results, finite element simulation, and formula derivation, a new standpoint was proposed: the diffusion of substances in the HPHT system was affected by the pressure gradient at the interface.     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

提升管内颗粒浓度梯度力的特性

由于Kutta-Joukowski横向力与浓度梯度力的共同作用，提升管内颗粒沿径向在边壁大量聚集并形成稳定的环?核结构。根据实验数据，分析了颗粒浓度梯度的径向分布特征，考察了不同操作条件下浓度梯度力系数K的分布特性。由Kutta-Joukowski横向力与浓度梯度的关系，提出了浓度梯度力的表达式Fρ=K(dρ/dr)A及浓度梯度力系数K的表达式K=[?ρg(νg?νp)(?v/?r)r]/?dρ/dr+(d2ρ/dr2)r?。提升管内颗粒群受到的浓度梯度力与浓度梯度力系数K有关。浓度梯度力系数K在提升管中心处为0，沿提升管径向呈“N”型分布，随表观气速增加而增加；提升管内充分发展段K的数值明显大于提升管加速区和出口约束区，总结了浓度梯度力系数K的经验关联式。     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

换热网络优化的随机摄动方法及其应用

启发式算法在处理换热网络问题时具有可操作性强、搜索域大等优点,但由于局部解众多,算法很难寻得全局最优。本研究基于强制进化随机游走算法,以费用下降为强制进化方向,按照换热量最小,公用工程、流股匹配回路是否存在的优先顺序确定摄动对象;并以一定的概率对其进行随机地换热量线性变化或直接消去。重复寻找原结构下的更优分布或者新的网络结构。此外,引入梯度近似公式提高随机摄动方法的搜索精度。最后,通过计算10股流和20股流算例得到相较文献更低的年综合费用,分别为5 586 942和1 739 079$/a,证明该方法能够有效地促进换热网络结构进化,得到更优的网络结构。     

低渗透油藏中不同压裂注采井网非稳态产量计算分析

在井网压裂基础上进行注水，可以有效改善流场，增大泄油面积，是开发低渗透油藏的重要手段。快速准确预测低渗透油藏注水开发产量可以为开发优化设计奠定基础，但低渗透油藏注水开发呈现非稳态、非线性渗流特征，基于达西定律形成的油藏工程方法并不适用。本次研究通过流场分析，来划分等效流动单元，并在此基础上考虑了油水两相渗流启动压力梯度，采用流线积分法建立了不同压裂注采井网的水驱非稳态产量解析计算方法。与物理模拟及数值模拟相比，计算方法更简单，计算速度更快，可以为低渗透油藏压裂注采方式优选及注水开发对策制定提供手段。采用本方法计算并剖析了启动压力梯度、压裂注采方式及裂缝长度对油井生产动态的影响，结果表明：启动压力梯度增大了渗流阻力，与不考虑启动压力梯度相比，油井产量更低；受流动单元控制，不同压裂注采方式的增产效果及见水时间完全不同，同时压裂注采方式的增产效果最好，能够增产3.1倍，但见水时间仅为24个月；随着压裂缝长增加，油井产量越高，但当缝长超过最佳长度，增油效果不明显。     

Microbial community dynamics of a blue-veined raw milk cheese from the United Kingdom

A commercial blue-veined cheese made from unpasteurized milk was examined by conventional culturing and PCR denaturing gradient gel electrophoresis analysis of the bacterial community 16S rRNA genes using 3 primer sets, V3, V4V5, and V6V8. Genomic DNA for amplification was extracted directly from raw milk, starter culture, cheese at different stages of production, fully ripened cheese, and from the cultured cells grown on various media. The outer rind was sampled separately from the inner white core and blue veins. A diverse microbiota containing Lactococcus lactis ssp. lactis, Lactobacillus plantarum, Lactobacillus curvatus, Staphylococcus gallinarum, Staphylococcus devriesei, Microbacterium sp., Sphingobacterium sp., Mycetocola sp., Brevundimonas sp., Enterococcus faecalis, Proteus sp., and Kocuria sp. was detected in the raw milk using culturing methods, but only Lactococcus lactis ssp. lactis, Lactobacillus plantarum, and Enterococcus faecalis survived to the final cheese and were detected both in the core and the rind. Using PCR denaturing gradient gel electrophoresis analysis of the cheese process samples, Staphylococcus equorum and Enterococcus durans were found in the rind of prepiercing samples but not in the core and veins; after piercing, these species were found in all parts of the cheese but survived only in the rind when the cheese was fully ripened. Brevibacterium sp., Halomonas sp., Acinetobacter sp., Alkalibacterium sp., and Corynebacterium casei were identified only by PCR denaturing gradient gel electrophoresis and not cultured from the samples. Brevibacterium sp. was initially identified in the cheese postpiercing (core and veins), Halomonas sp. was found in the matured cheese (rind), and Acinetobacter sp., Alkalibacterium sp., and Corynebacterium casei were also found in the prepiercing samples (rind) and then found through the subsequent process stages. The work suggests that in this raw milk cheese, a limited community from the milk survive to the final cheese, with salt addition and handling contributing to the final cheese consortium.