葛洲坝水电站水轮机磨蚀评析

作为长江第一坝的葛洲坝水电站,其水轮机磨蚀情况引起国内同行的关注。根据长江的水沙特点、葛洲坝工程泥沙研究成果、十年来运行观测实践、水轮机运行及磨蚀修复状况,对葛洲坝水轮机磨蚀的现状进行了评论和剖析,提出了今后评价和解决葛洲坝水轮机磨蚀的九点建议及意见。     

管外上升R－113流体的沸腾换热试验

详细介绍了Ｒ－１１３流体管外上升流动的沸腾换热试验，包括试验台架、流量计和温度测量的修正，以及试验数据。拟合的换热关系式为ｑ＝５２８（Ｔｗ─Ｔｓ）１．３３。     

淀积条件对GDa—SiNx薄膜电学特性的影响

本文讨论了用辉光放电法制备氮化硅薄膜时衬底温度、射频功率和气体流量比对薄膜的电导率、介电常数和击穿强度的影响。通过优化生长条件，制备了优质非晶氮化硅薄膜，其介电常数为７．５、击穿强度为５．５ＭＶ／ｃｍ、电导率为１０－１３（Ωｃｍ）－１。     

Two-level optimal output feedback stabilizer design

The paper addresses the design of two-level power system stabilizers using an optimal reduced order model whose state variables are torque angles and speeds. The reduced order model retains their physical meaning and is used to design a two-level linear feedback controller that takes into account the realities and constraints of electrical power systems. The two-level control strategy is used, and a global control signal is generated from the output variables to minimize the effect of interactions. The effectiveness of this controller is evaluated and a multimachine system is given as an example to illustrate the advantages of the proposed method. Responses of the system with a two-level scheme and an optimal reduced order scheme are included for comparative analysis.     

Gate and drain currents in off-state buried-type p-channel LDDMOSFETs

The buried-type p-channel LDD MOSFETs biased at high positive gate voltage exhibit novel characteristics: (1) the ratio of the drain to gate currents is about 1×10^-3 to 5×10^-3; and (2) the gate and drain currents both are functions of only the gate voltage minus the n-well bias. Such characteristics are addressed based on the formation of the surface n ⁺ inversion layer due to the punchthrough of the buried channel to the underlying shallow p-n junction. The measured gate current is due to the Fowler-Nordheim tunneling of electrons from this inversion layer surface and the holes generated within the high-field oxide constitute the drain current. The n⁺ inversion layer surface potential is found to be equal to the n-well bias plus 0.55 V. As a result, both the oxide field and the gate and drain currents are independent of drain voltage     

Production and properties of micro‐porous glas bubble zinc and aluminium composites

Micro‐porous syntactic foams were produced by means of integration of glass bubbles into aluminium and zinc matrices. Preforms of glass bubbles were pressure infiltrated with the alloys AlSi9Cu9 ans ZnAl4Cu using squeeze casting. The preforms were sintered thermically without the use of bonding agents. Using the combination of different sintering steps syntactic foams with locally different densities could be produced. The mechanical properties of the foams were tested indicating a high compression strength of the foams and a very good compression energy absorption. Furthermore, corrosion behaviour and behaviour at higher temperatures were investigated.     

From information to probability: An axiomatic approach—Inference is information processing

We define the very rich language of composed conditionals on a three‐valued logic and use this language as the communication tool between man and machine. Communication takes place for three reasons: knowledge acquisition, query, and response. Learning, thinking, and answering questions are of a pure information theoretical nature. The pivot of this knowledge processing concept is the amount of information (bit) we receive if a conditional becomes true. We follow an axiomatic approach to information theory rather than the classical probabilistic approach of Shannon; information comes first, and then comes probability. In the light of this philosophy, query and response experience new interpretations. Both, acquisition and response are realized by maximizing entropy and minimizing relative entropy, respectively. The iterative solution of these mathematical optimization problems gives new insights into the adaptation of prior knowledge to new information. Our expert system shell SPIRIT supports this kind of knowledge processing, which will be established by suitable examples. © 2003 Wiley Periodicals, Inc.     

Prediction of fire classification for wood based products. A multivariate statistical approach based on the cone calorimeter

Wood has long traditions as a building material, and is often used in construction elements, and as interior and exterior surfaces in the Nordic countries. In most applications, there are reaction to fire requirements to products used as surfaces, e.g. in escape routes and larger public spaces. Most wood products will therefore have to be treated with fire retardant (FR) agents to fulfil the strict requirements to properties connected to heat release and flame spread. Unfortunately, FR agents usually also increase the smoke production, as they cause a more incomplete combustion of the wood. The wood product manufacturers seek to find the optimal amount of FR additives where both heat release and smoke production in the classifying test are within the requirements given in the building regulations. This paper describes models for prediction of the European reaction to fire classes of wood products. The models are based on multivariate statistical analysis, and use test results from the cone calorimeter test as input. The presented models are, with very good precision, able to predict which Euroclass and additional smoke class a wood based product would obtain if it were to be tested in the single burning item test. Copyright © 2006 John Wiley & Sons, Ltd.     

On-line learning delivery decision support system for highlyproduct mixed semiconductor foundry

A production learning system (PLS) based on the tool model was constructed as a decision support and real-time information update system to forecast the cycle time. A tool model includes a waiting model and a processing model. Each of the waiting and processing models uses a backpropagation neural network to establish the relationship between the input and output (time) of the model. Hence, cycle time estimation, tool group move and confirm line item performance (CLIP) value can be obtained based on the memory stored in the neural network. The result shows that the forecasting ability of the PLS has an error rate below 8% on average