两相流有限穿越可视图演化动力学研究

针对小管径两相流流动特性, 全新优化设计弧形对壁式电导传感器. 通过动态实验在获取传感器测量信号的基础上, 采用有限穿越可视图理论构建对应于不同流型的两相流复杂网络. 通过分析发现, 有限穿越可视图网络异速生长指数和网络平均度值的联合分布可实现对小管径两相流的流型辨识; 有限穿越可视图度分布曲线峰值可有效刻画与泡径大小分布相关的流动物理结构细节特征; 网络平均度值可表征流动结构的宏观特性; 网络异速生长指数对流体动力学复杂性十分敏感, 可揭示不同流型演化过程中的细节演化动力学特性. 两相流测量信号的有限穿越可视图分析为揭示两相流流型的形成及演化动力学机理提供了新途径. 关键词： 两相流 复杂网络 有限穿越可视图 网络异速生长指数     

Fixed charge and interface traps at heterovalent interfaces between Si(1 0 0) and non-crystalline Al2O3–Ta2O5 alloys

Characterization by Auger electron spectroscopy (AES) and Fourier transformation infrared spectroscopy (FTIR) confirms (Ta₂O₅)_x(Al₂O₃)_1−x alloys are homogeneous pseudo-binary alloys with increased thermal stability with respect to end member oxides, Ta₂O₅ and Al₂O₃. Capacitance–voltage (C–V) and current density–voltage (J–V) data as a function of temperate show that the Ta d-states of the alloys act as localized electron traps, and are at an energy approximately equal to the conduction band offset of Ta₂O₅ with respect to Si.     

成组Broyden修正矩阵的紧凑形式与成组记忆修正算法

1 引言 成组型线性方程组 其中,p是适中的数值,由于其有相当的实际应用背景,人们一直在研究有效的数值方法,特别是近年来,实际问题中归结出来的成组型方程组,其规模越来越大,又具有稀疏结构,因而使用迭代法是一种有效的途径,目前使用比较多的是Krylov子空间方法中的Lanczos方法,CG方法,GMRES方法等等。这种成组型算法的建立,其基本出发点是使算法具有较少的计算量和存储量,具体体现在: 1)成组型算法在应用于问题(1.1)的求解时,也具有有限终止性性质,而其终止步数一般要比单个型算法的步数减少了户倍,由于成组型算法每迭代一步的计算量基本上等同于单个型算法使用户次的计算量,如此,算法的计算量会有明显的改善。 2)当A存储在二级(secondary)内存时,在迭代计算时需要不断地进行存取交换,由于成组型算法的迭代步数减少了户倍,如此,用在这种交换的时间也要减少户倍,相当有效。 3)由于在成组型算法中,出现的多是AX的形式,其中,故成组型算法便于计算并行化。 4)即使用于求解单个方程组,当A的少数几个极端特征值分离甚远时,这种成组型算法也有可能改善其收敛速度,如成组型的CG方法。 目前,这种成组型算法已体现出很大的实用计算价值,然而其进一步的理论分析还有待深入研究。     

Shift,Rotation and Limited Scale Invariant Pattern Recognition Using Synthetic Discriminant Functions

Making use of the idea of synthetic discriminant functions (SDF), a simple way to synthesize a shift, rotation and limited size correlation filter is proposed. The SDF is synthesized by superimposing four 2nd order circular harmonics of a training reference pattern in 4 different sizes. Computer simulation experiments have shown that the filter is indeed shift, full rotation and limited size invariant over a size range from 1 to 1.82. The invariant range can be increased if more training patterns are used.     

气体探测器雪崩过程的研究

基于汤逊气体放大电子雪崩模型和一定的物理假设,导出描绘气体探测器中正比模式和有限正比模式的一个统一公式.该公式很好地符合实验事实,由此还得到了描述空间电荷作用强弱的参数──等效电容以及表征探测器工作状态的参量──线性度.同时,在假定SQS雪崩中空间电荷起重要作用的情况下,该公式很好地描述了其输出电量随高压的线性关系.     

Photocurrent analysis of organic photovoltaic cells based on CuPc/C60 with Alq3 as a buffer layer

The performance of an organic photovoltaic(OPV) cell based on copper phthalocyanine CuPc/C60 with a tris(8-hydroxyquinolinato) aluminum(Alq3) buffer layer has been investigated.It was found that the power conversion efficiency of the device was 1.51% under illumination with an intensity of 100 mW/cm2,which was limited by a squareroot dependence of the photocurrent on voltage.The photocurrent-optical power density characteristics showed that the OPV cell had a significant space-charge limited photocurrent with a varied saturation voltage and a three quarters power dependence on optical power density.Also,the absorption spectrum was measured by a spectrophotometer,and the results showed that the additional Alq3 layer has a minor effect on photocurrent generation.     

On the structural properties of small-world networks with range-limited shortcut links

We explore a new variant of Small-World Networks (SWNs), in which an additional parameter (r$r$) sets the length scale over which shortcuts are uniformly distributed. When r=0$r=0$ we have an ordered network, whereas r=1$r=1$ corresponds to the original Watts–Strogatz SWN model. These limited range SWNs have a similar degree distribution and scaling properties as the original SWN model. We observe the small-world phenomenon for r?1$r?1$, indicating that global shortcuts are not necessary for the small-world effect. For limited range SWNs, the average path length changes nonmonotonically with system size, whereas for the original SWN model it increases monotonically. We propose an expression for the average path length for limited range SWNs based on numerical simulations and analytical approximations.     

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

The fine-tuning of the enzymatic hydrolysis of proteins may provide a pool of peptides with predefined molar mass distributions. However, the complex mixture of molecules (peptides and amino acids) that results after the proteolysis of cheese whey turns unfeasible the assessment of individual species. In this work, a hybrid kinetic model for the proteolysis of whey by alcalase, multipoint-immobilized on agarose, is presented, which takes into account the influence of pH (8.0-10.4) and temperature (40-55 degrees C) on the activity of the enzyme. Five ranges of peptides' molar mass have their reaction rates predicted by neural networks (NNs). The output of NNs trained for constant pH and temperatures was interpolated, instead of including these variables in the input vector of a larger NN. Thus, the model complexity was reduced. Coupled to differential mass balances, this hybrid model can be employed for the online inference of peptides' molar mass distributions. Experimental kinetic assays were carried out using a pH-stat, in a laboratory-scale (0.03 L) batch reactor. The neural-kinetic model was integrated to a supervisory system of a bench-scale continually stirred tank reactor (0.5 L), providing accurate predictions during validation tests.     

Improved Hessian approximations for the limited memory BFGS method

This paper considers simple modifications of the limited memory BFGS (L-BFGS) method for large scale optimization. It describes algorithms in which alternating ways of re-using a given set of stored difference vectors are outlined. The proposed algorithms resemble the L-BFGS method, except that the initial Hessian approximation is defined implicitly like the L-BFGS Hessian in terms of some stored vectors rather than the usual choice of a multiple of the unit matrix. Numerical experiments show that the new algorithms yield desirable improvement over the L-BFGS method. This revised version was published online in June 2006 with corrections to the Cover Date.     

Diffusional coagulation of superparamagnetic particles in the presence of an external magnetic field

In this paper, we investigate the diffusional coagulation of colloidal superparamagnetic (SP) latex particles that are under the influence of an external magnetic field. The cluster size distributions (CSDs) that evolve with time were determined using an optical set-up that permits the direct visualization of particle clusters. Following the dynamic scaling analysis of van Dongen and Ernst (Phys. Rev. Lett. 54 (1985) 1396), we find that the CSDs all collapse onto a master curve when properly scaled. The bell-shape of this master curve indicates that large clusters preferentially scavenge small clusters in our system. From the time evolution of the average cluster size we infer that the reactivity between large clusters diminishes with increasing cluster size. These results are consistent with a simple mathematical formulation of the coagulation rate constant, or kernel, for the Brownian coagulation of magnetic particles. Moreover, our results support a growing body of evidence that the dynamic scaling theory developed by van Dongen and Ernst is a useful framework with which to study the microscale processes governing particle coagulation.