虚拟企业图纸数据传输

图纸数据是企业间合作需交换的一种重要的信息。本文分析了虚拟企业环境下企业之间交换图纸数据的要求，提出了一种具体的解决方案，并给出实例。     

钠冷快堆中喷雾钠火的计算分析

根据钠冷快堆中喷雾钠火的特点建立了理论模型，编制了SSPRAY程序。该程序模拟钠喷雾燃料过程中钠滴的运动、钠和氧气的燃烧反应、热量传递和质量传递等瞬态过程。用该程序计算了气体和墙壁温度、气体压力、氧气摩尔份额、喷雾流燃烧速率和热量热递速率等主要参数。利用AI实验数据和美国SPRAY－3A的计算结果对程序进行了验证，结果符合较好。     

用红外热象技术研究有凸台热源倾斜板的散热

本文介绍红外热象技术在工程热物理研究中某些应用的探索。作为实例，通过热象技术，实验研究了具有水平凸台离散热源的倾斜板的散热，在研究它的自然对流换热部份结合利用激光全息干涉技术。此间着重于分析倾斜角对散热的影响。     

热流法计算高温炉内辐射传热

高温炉内的辐射传热是工业生产中常遇到的问题。过去，人们在应用热流法计算辐射传热时遇到了一定的困难，使计算结果与实际有一定的偏差。本文运用文献［１］中给出的新热流数学模型，开发出由新热流方程与能量方程相耦合的计算机程序，其中的比热流参数由线加热热源情况下计算得到。用该程序计算了实验室用马弗炉内的温度场，并且用热电偶实测了炉内的一些温度值，理论计算与实测值符合很好。     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

MS—Windows3.1下应用程序间数据交换方法及其选用策略

本文分析讨论了在ＭＳ－Ｗｉｎｄｏｗｓ操作系统下的应用程序间的数据交换的概念及方法，并给出了这些方法的选用策略。这些方法包括裁剪板，动态数据交换（ＤＤＥ），对象的连接及嵌入（ＯＬＥ），动态连接库（ＤＬＬ）。     

Impact of uncertainties on estimations of heat pump cycle performance

Sensitivity analysis is used to show how uncertainties in basic factors, such as heat transfer, pressure drop, compressor efficiency, and thermophysical properties, influence the result of heat pump cycle performance estimations. By comparing the results of this analysis with uncertainty levels of estimations and experimental determinations, it is established to what extent improvements in accuracy are needed. The investigation was carried out for several different applications and, as working fluids, both pure substances and non-azeotropic mixtures were examined. It was found that evaporation heat transfer and compressor efficiency are two areas where improvements are needed. It is also shown that estimates using very accurate, substance-specific, equations of state differ from those using more general types by up to 1 % for COP and 2% for capacity.     

Power Dissipation in Spintronic Devices Out of Thermodynamic Equilibrium

Quantum limits of power dissipation in spintronic computing are estimated. A computing element composed of a single electron in a quantum dot is considered. Dynamics of its spin due to external magnetic field and interaction with adjacent dots are described via the Bloch equations. Spin relaxation due to magnetic noise from various sources is described as coupling to a reservoir. Resulting dissipation of energy is calculated and is shown to be much less than the thermal limit, ∼kT per bit, if the rate of spin relaxation is much slower than the switching rate. Clues on how to engineer an energy efficient spintronic device are provided.     

Comparative studies on Ar and He closed-cycle MHD power plants combined with fossil fuel

Comparative studies on Ar and He closed-cycle MHD power plants combined with fossil fuel are performed. The sizes of a regenerative heat exchanger and a boiler are expected to be smaller for He than for Ar. The pressure loss of a working gas in a regenerative heat exchanger is reduced for He. The purification subsystem for He is expected to be more compact and economical than that for Ar; but a larger compressor is required for He than for Ar.     

Estimation of fouling in a plate heat exchanger through the application of neural networks

Pasteurization is an important, if not the most important, process step in the packaging of milk. It is subject to alterations stemming from the variation in the temperature, pH and raw milk quality. The variability may manifest itself in changes in the formation of the deposit (fouling) in the pasteurization unit, such that there is a need for tools, both instrumentation and computational, to help in monitoring the process and keeping it on the desired course. In this paper we describe a practical procedure based on artificial neural networks (ANN) that allows prediction of the deposit thickness, the overall heat transfer coefficient and the critical time (the time that the unit has to be stopped for cleaning) for reducing the impact of fouling on such processes. The procedure determines when the cleaning operation is required once the system is under critical conditions of operations. A combination of fundamental studies and plant measurements were used for study of the operating conditions and thus evaluation of the trades‐offs between operating conditions and longer operating life span. The results are encouraging, enough to validate current operating industrial techniques. Copyright © 2005 Society of Chemical Industry