无轴承旋翼柔性梁载荷特性研究

应用Hamilton原理建立了双路传力的无轴承旋翼运动方程。采用均匀入流模型,基于直升机飞行平衡条件,建立了无轴承旋翼柔性梁载荷的计算模型,并通过算例验证了模型的精度。利用该模型,研究了全机重心位置、机身气动阻力以及平尾安装角对柔性梁载荷特性的影响,给出了各因素对柔性梁载荷的影响趋势,得出了降低柔性梁载荷的方法。数值结果表明:2cm左右重心位置的变化能够引起9%~11%的柔性梁载荷变化量,15%气动阻力的增加会导致约9%的柔性梁载荷的增大;2°平尾安装角的变化引起约10%柔性梁载荷的变化量,3°平尾安装角的变化引起约26%柔性梁载荷的变化。     

The pyrolysis of propane

The pyrolysis of propane plays an important role in determining the combustion properties of natural gas mixtures and offers insight into the cracking patterns of larger fuels. This work investigates propane pyrolysis behind reflected shock waves with a multiwavelength laser-absorption speciation technique. Nine laser wavelengths, sensitive to key pyrolysis species, were used to measure absorbance time histories during the decomposition of 2% propane in argon between 1022 and 1467 K, 3.7-4.3 atm. Absorbance models were developed at each diagnostic wavelength to interrogate common initial conditions, and time histories of all major species are reported at 1250, 1290, 1330, 1370, and 1410 K. Nearly complete carbon recovery observed at lower temperatures enabled the inference of hydrogen formation from atomic conservation, while decaying carbon recovery at high temperatures suggests the formation of allene and 1-butene. The results show systematically faster pyrolysis than predicted by kinetic modeling and motivate further study into the kinetics of propane pyrolysis.     

Gas-Sensing Activity of Amorphous Copper Oxide Porous Nanosheets

In this paper, the gas-sensing properties of copper oxide porous nanosheets in amorphous and highly crystalline states were comparatively investigated on the premise of almost the same specific surface area, morphology and size. Unexpectedly, the results show that amorphous copper oxide porous nanosheets have much better gas sensing properties than highly crystalline copper oxide to a serious of volatile organic compounds, and the lowest detection limit (LOD) of the amorphous copper oxide porous nanosheets to methanal is even up to 10 ppb. By contrast, the LOD of the highly crystalline copper oxide porous nanosheets to methanal is 95 ppb. Experiments prove that the oxygen vacancies contained in the amorphous copper oxide porous nanosheets play a key role in improving gas sensitivity, which greatly improve the chemical activity of the materials, especially for the adsorption of molecules containing oxygen-groups such as methanal and oxygen.     

“神龙一号”直线感应加速器物理设计

介绍了 “神龙一号”直线感应加速器物理设计的主要考虑。“神龙一号”加速器是一台电子直线感应加速器,由3.6MeV感应迭加型注入器、72个感应加速腔、脉冲功率系统、束流输运和聚焦系统、控制系统和真空、绝缘油、绝缘气体以及去离子水系统组成。能产生20MeV、束流大于2.5kA,脉冲宽度为60ns的强流脉冲电子束,X光焦斑均方根直径为1.5mm。     

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Kinetic energy(KE) functional is crucial to speed up density functional theory calculation. However, deriving it accurately through traditional physics reasoning is challenging. We develop a generally applicable KE functional estimator for a one-dimensional (1D) extended system using a machine learning method. Our end-to-end solution combines the dimensionality reduction method with the Gaussian process regression, and simple scaling method to adapt to various 1D lattices. In addition to reaching chemical accuracy in KE calculation, our estimator also performs well on KE functional derivative prediction. Integrating this machine learning KE functional into the current orbital free density functional theory scheme is able to provide us with expected ground state electron density.     

Dynamic maintenance decision-making for series–parallel manufacturing system based on MAM–MTW methodology

Proper maintenance schedule is required to improve manufacturing systems’ profitability and productivity. A novel dynamic maintenance strategy is thus developed to incorporate both the single-machine optimization and the whole-system schedule for series–parallel system. Firstly, multiple attribute value theory and maintenance effects are considered in the single-machine optimization. A developed multi-attribute model (MAM) is used to determine the optimal maintenance intervals. Then, a series–parallel structure of the system is investigated in terms of the whole-system schedule. Maintenance time window (MTW) programming is presented to make a cost-effective system schedule by dynamically utilizing maintenance opportunities. The maintenance scheme achieved by using the proposed MAM–MTW methodology is demonstrated through a case study in a hydraulic steering factory. It is concluded that proper consideration of maintenance effects and time window leads to a significant cost reduction.     

Structural Transformations of Ni1 – xCuxFe2O4 Nanoparticles Depending on the Number of Cu Ions

Physics of the Solid State - Changes in the structure and properties of the Ni1 – xCuxFe2O4 ferrites-spinel magnetic nanoparticles depending on the Cu ion concentration (0...     

Fault Diagnosis of Rolling Bearings Based on WPE by Wavelet Decomposition and ELM

The fault diagnosis classification method based on wavelet decomposition and weighted permutation entropy (WPE) by the extreme learning machine (ELM) is proposed to address the complexity and non-smoothness of rolling bearing vibration signals. The wavelet decomposition based on ‘db3’ is used to decompose the signal into four layers and extract the approximate and detailed components, respectively. Then, the WPE values of the approximate (CA) and detailed (CD) components of each layer are calculated and composed to be the feature vectors, which are finally fed into the extreme learning machine with optimal parameters for classification. The comparative study of the simulations based on WPE and permutation entropy (PE) shows that the classification method of seven kinds of signals of normal bearing signals and six types of fault states (7 mils and 14 mils) based on WPE (CA, CD) with the number of nodes in the hidden layers of ELM determined by the five-fold cross-validation has the best performances, the training accuracy can reach 100%, and the testing accuracy can reach 98.57% with 37 nodes of the hidden layer by ELM. The proposed method using WPE (CA, CD) by ELM provides guidance for the multi-classification of normal bearing signals.     

Giant negative and positive lateral shifts in graphene superlattices

Electronic analogy of Goos-Hänchen shift has been well established and investigated in various graphene-based nanostructures, including p-n interface, single and double barriers. In this paper, we have studied the giant negative and positive lateral shifts of the transmitted electron beam through graphene superlattices. It is found that the lateral shifts, depending on the location of new Dirac point, can be negative as well as positive near the band edges of zero-k? (non-Bragg) gap. We have also achieved the enhanced opposite shifts in the graphene superlattice with defect layer, since such structure possesses the defect mode inside the zero-k? gap. The modulations of negative and positive lateral shifts by incidence angles, width and potential height of defect layer may lead to potential applications in the graphene-based electron wave devices.