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。     

Second moment of error term of mean value for binary Egyptian fractions

We study the mean square of the error term of the mean value for binary Egyptian fractions.We get an asymptotic formula under the Riemann Hypothesis.     

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.     

GaN垒层厚度渐变的双蓝光波长发光二极管

针对单蓝光波长芯片与Y3Al5O12∶Ce3+黄光荧光粉封装白光发光二极管存在显色性不足的问题,提出了采用双蓝光波长芯片激发Y3Al5O12∶Ce3+黄光荧光粉实现高显色性白光发射法,并分析了其可行性.利用金属有机化学气相沉积系统在(0001)蓝宝石衬底上顺序生长两个In0.18Ga0.82N/GaN量子阱和两个In0.12Ga0.88N/GaN量子阱的双蓝光波长发光二极管,并对不同GaN垒层厚度的双蓝光波长发光二极管的光电性能进行分析,结果表明沿n-GaN到p-GaN方向减小GaN垒层厚度能实现双蓝光发射,并有较好的发光效率.交流阻抗谱结果显示相关双蓝光波长发光二极管可以用一个电阻Rp与电容Cp并联后与一个Rs串联电路来模拟,GaN垒层变化能调节并联电阻和电容,对串联电阻没有影响.此外,基于垒层减小的双蓝光波长芯片激发Y3Al5O12∶Ce3+荧光粉实现了高显色指数的白光发射.     

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.     

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.     

一类非线性压缩生成的不变集的局部维数

本文以有界变差原理和有界畸变原理为工具,讨论了满足开集条件,且二次可微的一族递增的非线性压缩映射所产生的不变集的局部维数。     

基于光子晶体的一维位置敏感探测器

位置敏感探测器（PSD）是基于半导体的横向光电效应的光电转换器件。根据横向光电效应的原理,增强光生载流子的寿命可提高光生载流子的扩散长度,从而实现横向光电效应的增强。利用缺陷和完整光子晶体设计了一维位置敏感探测器,并用传输矩阵法计算了缺陷光子晶体的透射率和完整光子晶体的反射率。从所设计的位置敏感探测器对入射光的响应理论研究表明,提高缺陷光子晶体的透射率和完整光子晶体的反射率可提高光生载流子的寿命,从而实现增强位置敏感探测器的横向光电效应。