Evaluating Particle Deposition in the Artificial Groundwater Recharge Process by Physical and CT Imaging Experiments

Particle clogging in the artificial groundwater recharge process is one of the main factors influencing the artificial groundwater recharge efficiency, and particle deposition is the microscopic mechanism of the occurrence and development of particle clogging. Particle deposition in porous media changes the pore structure. The computed tomography (CT) scanning technique is a nondestructive testing method and determines the spatial distribution of pores in porous media. This study combines physical and CT scanning experiments to identify the change process of the pore structure in the artificial groundwater recharge process and compares the pore changes during recharge experiments between two columns containing different media. Porous media changes are observed with the CT scanning technique. The fractal theory is applied in the analysis of CT scan images and physical experiment results. The results of this study indicate that particle deposition can be examined by using CT scan images to obtain pore-related parameters, the internal pore structure of porous media determined through CT scan images can be applied in numerical simulation, and a mathematical model for particle deposition calculation in porous media is established. Compared to the physical experiment measurements, the spatial particle deposition information acquired with the CT scanning technique exhibits a higher accuracy and contains much more relevant data. Not only does this research reveal more clearly the particle clogging mechanism which is based on particle deposition, but also characterize, simulate and predict more accurately the development tendency of particle clogging during artificial groundwater recharge.

     

Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting

3D printing offers great potential for developing complex flexure mechanisms. Recently, thickness-correction factors (TCFs) were introduced to correct the thickness and stiffness deviations of powder-based metal 3D printed flexure hinges during design and analysis. However, the reasons for the different TCFs obtained in each study are not clear, resulting in a limited value of these TCFs for future design and fabrication. Herein, the influence of the porous layer of 3D printed flexure hinges on the hinge thickness is investigated. Samples of parallelogram flexure mechanisms (PFMs) were 3D printed using selective laser melting (SLM) and 316L stainless steel powder. A 3D manufacturing error analysis was completed for each PFM sample via 3D scanning, surface roughness measurement and morphological observation. The thickness of the porous layer of the flexure hinge was independent of the designed hinge thickness and remained close to the average powder particle diameter. The effective hinge thickness could be estimated by subtracting twice the value of the porous layer thickness from the designed value. Guidelines based on finite element analysis and stiffness experiments are proposed. The limitations of the presented method for evaluating the effective hinge thickness of flexure hinges 3D printed via SLM are also discussed.

     

CBR系统中一种实例相似度算法的改进

本文对CBR系统中实例相似度的算法进行了改进,在传统实例相似性算法的基础上加入实例属性缺失度因子和实例可复用性因子。因此在实例检索中考虑实例检索结果的精确度和实例的可复用度,进而使检索的精确度提高和修改难度降低,最终提高CBR系统的精确度和实用性。     

Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.     

Robust H ∞ control of uncertain nonlinear switched systems using constructive method

This paper focuses on the problem of robust H _?? control of nonlinear switched systems with parameter uncertainty via the multiple Lyapunov functions (MLFs) approach. The uncertain parameters are assumed to be in a known compact set and are allowed to enter the system nonlinearly. Based on the explicit construction of Lyapunov functions, which avoids solving the Hamilton-Jacobi-Isaacs (HJI) inequalities, sufficient conditions for the solvability of the robust H _?? control problem of cascade nonlinear switched systems are derived under some switching signal. Then, the result is extended to solve the robust H _?? control problem of nonlinear switched systems in strict feedback form. Finally, the effectiveness of the proposed results is illustrated through a simulation example.     

FD—140型高灵敏测氡仪及其应用

FD—140型高灵敏测氡仪用于测量氡的浓度,测氡灵敏度可达15mBq/m~3 min。该仪器可用于普查与勘探铀矿床,也可用于找石油、煤、金、地下水和地震预报以及地质构造调查等方面。     

项目管理在IT企业中的推广与应用

现阶段传统的管理模式还是一些中小型IT企业的主要管理方式。这种方式导致很多中小型IT企业发展的新旧项目成功率都很小。所以,在当前情况下,要大大加强项目管理的涉及度,使之进入中小IT企业。从而,提高中小企业的核心竞争力。那么什么IT企业,何为项目管理,项目管理在IT企业中的推广应用,以及二者之间的相互关系就是我们研究的重要课题。     

33UH高矫顽力烧结NdFeB材料的制备

针对永磁电机对磁钢的特殊要求，通过采用重稀土取代轻烯土，减少氧含量且低温长时间细晶粒液相烧结技术制备33UH高矫顽力烧结NdFeB材料，经测试其静态性能指标同时达到Br≥1.15T(11.5kG),Hcj≥200kA/m(25.0kOe),(BH)max≥256kJ/m^3(32MGOe).     

Damping forced oscillations in power system via interline power flow controller with additional repetitive control

With the continuous expansion of power systems and the application of power electronic equipment, forced oscillation has become one of the key problems in terms of system safety and stability. In this paper, an interline power flow controller (IPFC) is used as a power suppression carrier and its mechanism is analyzed using the linearized state-space method to improve the system damping ratio. It is shown that although the IPFC can suppress forced oscillation with well-designed parameters, its capability of improving the system damping ratio is limited. Thus, combined with the repetitive control method, an additional repetitive controller (ARC) is proposed to further dampen the forced power oscillation. The ARC control scheme is characterized by outstanding tracking performance to a system steady reference value, and the main IPFC controller with the ARC can provide higher damping, and further reduce the amplitude of oscillations to zero compared with a supplementary damping controller (SDC). Simulation results show that the IPFC with an ARC can not only greatly reduce the oscillation amplitude, but also actively output the compensation power according to the reference value of the ARC tracking system.     

BPDA/ODA体系微支化交联聚酰亚胺薄膜制备与性能研究

以三官能度的1,3,5-三(4-氨基苯氧基)苯(TAPOB)为交联剂,在3,3′,4,4′-联苯四甲酸二酐(BPDA)和4,4′-二氨基二苯醚(ODA)体系聚酰亚胺(PI)薄膜中构建微支化交联结构,制备出一系列具有不同TAPOB含量的PI薄膜,研究了TAPOB含量对薄膜力学性能、热力学性能、介电性能和吸水率的影响.结果表明:TA-POB的引入可明显提高BPDA/ODA体系PI薄膜的综合性能,交联结构的存在有利于提高薄膜的力学性能、降低热膨胀系数(CTE)和吸水率,微支化结构则对降低介电常数有一定的作用.