A new monitoring system for piping systems in fossil fired power plants

A CEC-funded project has been performed to tackle the problem of producing an advanced Life Monitoring System (LMS) which would calculate the creep and fatigue damage experienced by high temperature pipework components. Four areas were identified where existing Life Monitoring System technology could be improved:

1. 1. the inclusion of creep relaxation

2. 2. the inclusion of external loads on components

3. 3. a more accurate method of calculating thermal stresses due to temperature transients

4. 4. the inclusion of high cycle fatigue terms.

The creep relaxation problem was solved using stress reduction factors in an analytical in-elastic stress calculation. The stress reduction factors were produced for a number of common geometries and materials by means of non-linear finite element analysis. External loads were catered for by producing influence coefficients from in-elastic analysis of the particular piping system and using them to calculate bending moments at critical positions on the pipework from load and displacement measurements made at the convenient points at the pipework. The thermal stress problem was solved by producing a completely new solution based on Green's Function and Fast Fourier transforms. This allowed the thermal stress in a complex component to be calculated from simple non-intrusive thermocouple measurements made on the outside of the component. The high-cycle fatigue problem was dealt with precalculating the fatigue damage associated with standard transients and adding this damage to cumulative total when a transient occurred.

The site testing provided good practical experience and showed up problems which would not otherwise have been detected.     

Study of photosensitive mixtures of TMAE and helium, hydrocarbon or CF4-based carrier gases

Among the photocathodes used for particle identification based on the Cherenkov Ring Imaging technique, the TMAE molecule is still the best in terms of quantum efficiency. Despite the fact that TMAE gaseous photocathodes have already been used in a number of large experiments, one still seeks answers to many detailed questions. We present a systematic study of gaseous photocathodes based on TMAE mixed with helium, hydrocarbon and CF₄-based gases at normal pressure. The study includes a measurement of the electron drift velocity, gas quenching properties, single electron pulse height spectra and anode wire aging. The paper makes recommendations for carrier gas mixtures to obtain the best quenching, and suggests how to manage TMAE wire aging. This study was motivated by a specific particle identification detector proposal, the Fast Drift CRID proposed for the B-factory at SLAC.     

Recovery of fluorocarbons in Japan as a measure for abating global warming

The objective of this study is to evaluate the potential for recovering fluorocarbons as measures for the abatement of global warming. In this study, we focused on the three different kinds of fluorocarbons: CFCs, HCFCs and HFCs, and targeted refrigerant use because of the availability of relevant data. We first estimated future fluorocarbon emissions from the targeted appliances; we next compared those emissions in the units of CO₂ equivalent to the level of CO₂ emissions in 1990 from a quantitative point of view. As the result of this study, it was found that fluorocarbon emissions in 1999 and 2010 would be equal to approximately 7 and 3% of the level of CO₂ emissions in 1990 respectively. Moreover, if we implement a 100% recovery rate in every recovery route, we can reduce a large amount of emissions which correspond to approximately 2–5% of the level of CO₂ emissions in 1990, even if we take into account the energy-related CO₂ emissions by the transportation and decomposition of fluorocarbons.     

测定钠中杂质的手动阻塞计的研究

文章介绍了测定钠中杂质浓度的手动阻塞计的原理、装置和实验结果。为了减少测量误差,我们研究了影响准确测定阻塞温度的因素,并且找到了减少测量误差的办法。在同样的杂质饱和温度下,该阻塞计测得的高、低阻塞温度所对应的杂质浓度差是很接近的。对氧其差值为1.03ppm;对氢为0.0763ppm。     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

Magnetic field dependence of shielding current density in Y-Ba-Cu-O rings at 77 K

A method for contactless measurement of the shielding critical current density and its dependence on the external magnetic field is described and analyzed. The obtained values are compared with those measured resistively on two different samples. It is shown that the shielding critical current densityJ _cs and the intergranular transport current densityJ _cr are identical if the measurement conditions are similar. A degradation ofJ _cs measured in the external field with AC ripple has been observed.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.