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91.
金属基弥散燃料元件在特殊工况下会发生表面起泡失效。燃料颗粒开裂是金属基体开裂的前提条件,只有当金属基体开裂后元件才会发生表面起泡。燃料颗粒开裂后,裂纹宽度和塑性区长度等裂纹特征决定了金属基体开裂行为。基于弹塑性断裂力学和应力平衡条件,建立了基于弥散燃料颗粒开裂的金属基体裂纹特征模型。计算结果表明:裂纹张开位移随退火温度和燃耗深度的升高而增加;裂纹尖端塑性区长度主要与退火温度相关。裂纹张开位移和塑性区长度的计算结果与实验数据均符合较好,验证了金属基体裂纹特征模型的有效性。 相似文献
92.
醇胺法捕集CO2技术是一种较成熟的CO2捕集技术,具有吸收速度快、脱除效果好等显著优点,但其操作费用高、解吸能耗大。本文以降低醇胺法捕集烟气中CO2系统再生能耗为出发点,对常规醇胺法捕集CO2工艺统进行了节能优化研究。在常规工艺流程基础上引入压缩式热泵节能技术,并利用Aspen Plus软件建立了基于压缩式热泵技术的CO2捕集工艺流程模型。研究了压缩式热泵与机械蒸汽压缩回收(MVR)热泵、分流解吸、分布式换热、级间冷却4种节能工艺耦合,通过模拟计算与优化,结果说明了最佳节能工艺组合为“解吸塔压缩式热泵+贫液MVR热泵+分流解吸+级间冷却”耦合的CO2捕集工艺流程,当解吸塔顶气体分流比为0.25∶0.75、冷富液分流比为0.05∶0.95、级间冷却器位于吸收塔17块塔板位置、吸收塔输入冷量为-3.0GJ/h时,系统再生能耗最低,为2.533 GJ/tCO2,相比常规有机胺工艺(再生能耗4.204GJ/tCO2)节能率39.748%。 相似文献
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Xin-zhuang Cui She-qiang Cui Qing Jin Yi-lin Wang Lei Zhang Zhong-xiao Wang 《Geotextiles and Geomembranes》2018,46(1):66-76
To measure geosynthetic reinforcement strains, sensor-enabled geobelts (SEGB) that perform the reinforcement and self-measurement functions were developed in this paper. The SEGB of high-density polyethylene (HDPE) filled with carbon black (CB) were fabricated by both the industry and the laboratory. To study the mechanical properties and tensoresistivity performance of the SEGB, in-isolation tests and in-soil tests were performed. Hot pyrocondensation pipes (HPP) were used to protect the SEGB against the influence of water. For the SEGB specimens developed in the laboratory, the optimal CB filler content was 47.5%. For the SEGB fabricated by the industry, the optimal CB content was slightly decreased compared to the SEGB fabricated in the laboratory. For the modified SEGB sealed with HPP, the strain at the fracture was improved, while its tensile stress and the frictional property of the geobelt-soil interfaces both decreased slightly. In the pull-out tests, the self-measurement function of the SEGB was proved to be effective for evaluating the deformation behavior of geosynthetic reinforcement. The results are helpful for further application of SEGB technology in engineering. 相似文献
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For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative. 相似文献
100.
Alireza Baghban Amin Piri Mostafa Lakzaei Mohammad Baghban 《Petroleum Science and Technology》2019,37(11):1231-1237
The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO2 injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO2 as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility. 相似文献