冲击荷载作用下热处理花岗岩动态力学特性研究

为研究冲击速度和热处理温度对黑云母花岗岩动态力学特性的影响,利用改进的霍普金森压杆系统对25~800 ℃共9个温度等级的热处理试样分别进行3种弹速下的冲击压缩试验。试验结果表明：随着冲击速度的增加,25~700 ℃热处理试样的应力—应变曲线由“Ⅱ型”转变为“Ⅰ型”,而800 ℃热处理试样均表现出“Ⅰ型”应力—应变曲线特征。同一温度热处理下试块的峰值应力、应变和平均应变均随动荷载的升高而增大。相同的冲击速度下,300 ℃热处理试样的动力学性能有所改善,500 ℃后试样的动力学性能开始逐渐劣化。同一温度热处理试样的破碎程度随冲击速度的增加而增加;相同冲击速度下,热处理试样的破碎程度随温度的升高先减弱后增强。     

采用表面粘涂技术提高设备的抗腐蚀性能

通过表面粘涂，对受到腐蚀、冲蚀、气蚀的设备进行表面修补与防护，可提高设备的使用寿命。     

Thermal Shock Failure in Thick Epoxy Coatings

In this paper we describe an apparatus for reproducibly measuring thermal shock resistance of thick polymer layers bonded to metals. The thermal shock behavior is discussed in terms of epoxy samples bonded to an aluminum substrate. It was found that both high resin toughness and low resin thermal expansion coefficient improved thermal shock resistance of thick coatings, but only a sample containing 60wt. % glass beads did not develop a failure crack. Effects of sample thickness, temperature gradient, and resin composition on thermal shock behavior are discussed.     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

Failure load prediction of structural adhesive joints : Part 2: Experimental study

The objective of this study was to determine how the fracture of adhesive joints depends on elastic beam parameters describing the adherends and the applied loads. The basic specimen geometry was the cracked lap shear joint constructed of aluminium alloy with various adherend and bondline thicknesses. Loads were applied in different combinations of bending, tension and shear to generate a failure envelope for each adhesive and specimen geometry. It was found that crack propagation for precracked specimens occured at a critical strain energy release rate but was also a function of the G_I/G_II ratio and the bondline thickness. The experiments also showed that the loads required to propagate a crack in a precracked specimen were always lower than the loads required to break the fillet. Hence, by treating uncracked joints as being cracked, where the fictitious crack tip is assumed to coincide with the location of the fillet, a conservative estimate of the failure load is obtained.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

Unified reliability estimation and management of NoC based chip multiprocessors

We present a new architecture level unified reliability evaluation methodology for chip multiprocessors (CMPs). The proposed reliability estimation (REST) is based on a Monte Carlo algorithm. What distinguishes REST from the previous work is that both the computational and communication components are considered in a unified manner to compute the reliability of the CMP. We utilize REST tool to develop a new dynamic reliability management (DRM) scheme to address time-dependent dielectric breakdown and negative-bias temperature instability aging mechanisms in network-on-chip (NoC) based CMPs. Designed as a control loop, the proposed DRM scheme uses an effective neural network based reliability estimation module. The neural-network predictor is trained using the REST tool. We investigate how system’s lifetime changes when the NoC as the communication unit of the CMP is considered or not during the reliability evaluation process and find that differences can be as high as 60%. Full-system based simulations using a customized GEM5 simulator show that reliability can be improved by up to 52% using the proposed DRM scheme in a best-effort scenario with 2–9% performance penalty (using a user set target lifetime of 7 years) over the case when no DRM is employed.     

供电企业配电线路故障及策略

通过分析供电线路安全运行的影响因素,提出了防范故障的策略,希望能够对配电线路的安全运行提供帮助。     

电力工程自动化系统故障分析的方法

由于电力工程自动化系统的操作程序不断复杂化,故障的出现频率也随之增加。因此,必须建立完善的故障分析和解决方案,方能保证电力工程的正常、顺利推进。在此基础上,就电力工程自动化系统故障分析方法进行分析、探讨。     

基于广义Hoek-Brown准则的节理岩体力学参数折剪计算

节理化岩体力学参数是矿山工程地质和稳定性分析的重要基础数据,一般在实验室通过单轴、三轴试验只能得到岩石的力学参数,无法得到岩体的力学参数。目前,利用岩石力学参数确定岩体力学参数的方法较多,但都过于烦琐或准确度不高,急需一种新的简易确定方法。因此,通过利用基于广义Hoek-Brown准则的地质强度指标（GSI法）,并结合相应的岩土分析软件,强度折剪岩石力学参数值,从而得到岩体的力学参数。通过将该方法应用于赤峰中色白音诺尔矿业有限公司岩体力学参数的计算中,证明采用该方法考虑岩体的结构面和完整性,可较为准确地反映节理化岩体的力学特性,且方便可行,具有工程实用价值。?