The Joule-Thomson effect is one of the important thermodynamic properties in the system relevant to gas switching reforming with carbon capture and storage(CCS). In this work, a set of apparatus was set up to determin...The Joule-Thomson effect is one of the important thermodynamic properties in the system relevant to gas switching reforming with carbon capture and storage(CCS). In this work, a set of apparatus was set up to determine the Joule-Thomson effect of binary mixtures(CO_(2)+ H_(2)). The accuracy of the apparatus was verified by comparing with the experimental data of carbon dioxide. The Joule-Thomson coefficients(μ_(JT)) for(CO_(2)+ H_(2)) binary mixtures with mole fractions of carbon dioxide(x_(CO_(2))= 0.1, 0.26, 0.5,0.86, 0.94) along six isotherms at various pressures were measured. Five equations of state EOSs(PR,SRK, PR, BWR and GERG-2008 equation) were used to calculate the μ_(JT)for both pure systems and binary systems, among which the GERG-2008 predicted best with a wide range of pressure and temperature.Moreover, the Joule-Thomson inversion curves(JTIC) were calculated with five equations of state. A comparison was made between experimental data and predicted data for the inversion curve of CO_(2). The investigated EOSs show a similar prediction of the low-temperature branch of the JTIC for both pure and binary systems, except for the BWRS equation of state. Among all the equations, SRK has the most similar result to GERG-2008 for predicting JTIC.展开更多
识别复杂网络中的关键节点对优化网络结构以及信息的有效传播起着至关重要的作用。局部结构熵(LE)利用局部网络对整个网络的影响代替节点对整个网络的影响以识别重要节点,然而LE未考虑高聚集性网络和节点与邻居节点形成环的情况,存在一...识别复杂网络中的关键节点对优化网络结构以及信息的有效传播起着至关重要的作用。局部结构熵(LE)利用局部网络对整个网络的影响代替节点对整个网络的影响以识别重要节点,然而LE未考虑高聚集性网络和节点与邻居节点形成环的情况,存在一定的局限性。针对以上不足,首先,提出了改进LE的节点重要性评价方法PLE(Penalized Local structural Entropy),即在LE的基础上引入集聚系数(CC)作为惩罚项,从而适当惩罚网络中的高聚集性节点;其次,由于PLE的惩罚项对三元闭包结构上的节点惩罚力度过大,又提出了PLE的改进方法PLEA(Penalized Local structural Entropy Advancement),即在惩罚项前引入一个控制系数,以控制惩罚力度。对5个不同规模的真实网络进行选择性攻击实验,实验结果表明,在美国西部各州电网和美国航空网两个网络中,与LE方法相比,PLEA的识别准确率分别提升了26.3%和3.2%;与K-Shell(KS)方法相比,PLEA的识别准确率分别提升了380%和5.43%;与DCL(Degree and Clustering coefficient and Location)方法相比,PLEA的识别准确率分别提升了14.4%和24%。同时,PLEA识别的重要节点对网络造成的破坏更大,验证了引入CC作为惩罚项的合理性,以及PLEA的有效性和优越性。PLEA综合考虑了节点的邻居个数和节点的局部网络结构,计算简单,对于刻画大规模网络的可靠性与抗毁性具有十分重要的意义。展开更多
基金supported by the National Natural Science Foundation of China (21878056)Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology (2019Z002)。
文摘The Joule-Thomson effect is one of the important thermodynamic properties in the system relevant to gas switching reforming with carbon capture and storage(CCS). In this work, a set of apparatus was set up to determine the Joule-Thomson effect of binary mixtures(CO_(2)+ H_(2)). The accuracy of the apparatus was verified by comparing with the experimental data of carbon dioxide. The Joule-Thomson coefficients(μ_(JT)) for(CO_(2)+ H_(2)) binary mixtures with mole fractions of carbon dioxide(x_(CO_(2))= 0.1, 0.26, 0.5,0.86, 0.94) along six isotherms at various pressures were measured. Five equations of state EOSs(PR,SRK, PR, BWR and GERG-2008 equation) were used to calculate the μ_(JT)for both pure systems and binary systems, among which the GERG-2008 predicted best with a wide range of pressure and temperature.Moreover, the Joule-Thomson inversion curves(JTIC) were calculated with five equations of state. A comparison was made between experimental data and predicted data for the inversion curve of CO_(2). The investigated EOSs show a similar prediction of the low-temperature branch of the JTIC for both pure and binary systems, except for the BWRS equation of state. Among all the equations, SRK has the most similar result to GERG-2008 for predicting JTIC.
文摘识别复杂网络中的关键节点对优化网络结构以及信息的有效传播起着至关重要的作用。局部结构熵(LE)利用局部网络对整个网络的影响代替节点对整个网络的影响以识别重要节点,然而LE未考虑高聚集性网络和节点与邻居节点形成环的情况,存在一定的局限性。针对以上不足,首先,提出了改进LE的节点重要性评价方法PLE(Penalized Local structural Entropy),即在LE的基础上引入集聚系数(CC)作为惩罚项,从而适当惩罚网络中的高聚集性节点;其次,由于PLE的惩罚项对三元闭包结构上的节点惩罚力度过大,又提出了PLE的改进方法PLEA(Penalized Local structural Entropy Advancement),即在惩罚项前引入一个控制系数,以控制惩罚力度。对5个不同规模的真实网络进行选择性攻击实验,实验结果表明,在美国西部各州电网和美国航空网两个网络中,与LE方法相比,PLEA的识别准确率分别提升了26.3%和3.2%;与K-Shell(KS)方法相比,PLEA的识别准确率分别提升了380%和5.43%;与DCL(Degree and Clustering coefficient and Location)方法相比,PLEA的识别准确率分别提升了14.4%和24%。同时,PLEA识别的重要节点对网络造成的破坏更大,验证了引入CC作为惩罚项的合理性,以及PLEA的有效性和优越性。PLEA综合考虑了节点的邻居个数和节点的局部网络结构,计算简单,对于刻画大规模网络的可靠性与抗毁性具有十分重要的意义。
