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11.
姚晓刚 《筑路机械与施工机械化》2005,22(3):28-28,36
为降低沥青混凝土搅拌机的生产成本,采用ZZR重渣油燃烧机替代柴油燃烧机取得了很好的经济效益,给出了燃渣油时渣油含水及油路阻塞的判断方法,并介绍了出现故障的排除方法. 相似文献
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文章分析了某散货轮在试航中主机多次出现因滑油低压而报警停车的故障现象,并将该故障出现的原因及排除过程进行了总结和思考,以期为船厂、船东以及试航的相关人员提供参考。 相似文献
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分析5 000 PCC"SHANGHAI HIGHWAY"船主机伺服油压低引起主机启动故障的原因、介绍调查与处理过程,在实施了恰当的处理方法后,解决了问题。以期为类似问题的处理与解决提供借鉴和参考。 相似文献
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An experimental study to measure the evaporation rates, engine performance and emission characteristics of cotton seed biodiesel (cotton seed oil methyl ester) and its blends in different volumetric proportions with diesel is presented. The thermo-physical properties of all the fuel blends have been measured and presented. Evaporation rates of neat cotton seed biodiesel, neat diesel and their bends have been measured under slow convective environment of air flowing with a constant temperature. Evaporation constants have been determined by using the droplet regression rate data. The neat fuels and fuel blends have been utilized in a test engine with different load conditions to evaluate the performance, combustion and emission characteristics of the fuels. The specific fuel consumption values of the two blends, viz. B25 and B75 are found to be same. At the highest load, B0 records the lowest CO volume followed by B100. From the observed evaporation, performance and emissions characteristics, it is suggested that a blend of B50 and B75 can be optimally used in standard diesel engine settings. 相似文献
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The importance of accurate prediction of limiting cavity depths during offshore spudcan foundations installation has been variously highlighted in the literature. Nonetheless, most of the previous research is deterministic in nature and confined to homogenous soils. Since offshore clayey soils can be highly spatially variable, there is a practical need to take proper account of the spatial variability in the prediction of limiting cavity depths. In a bid to remedy this situation, large deformation finite element calculations combined with three-dimensional random fields were repeatedly conducted in this study within a Monte-Carlo framework. The continuous penetration of a spudcan initiated from surface was explicitly modeled until a full-localized flow-around mechanism was observed. Spatial variability was found to clearly affect the soil back-flow and thereby the limiting cavity depth, the latter of which takes a range of values that can be approximately modeled as a log-normal distribution. Characteristic limiting cavity depths at various probability levels were ascertained. An algebraic expression was proposed to explicitly predict the characteristic limiting cavity depths in random soils from the fractile. Particular attention was paid to the lower and upper 5% characteristic values, which are likely to be useful for reliability-based design. 相似文献
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FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points. 相似文献
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