Application of the fluid-structure interaction technique in the design of a giant sculpture

在某大型雕塑作品的设计中,风力作用下雕塑结构的顶点位移至关重要,它决定了作品能否实现预想中风吹草舞的景观效果。由于该雕塑不是单一结构,它的各组成部分之间的流场干扰将对风荷载下的结构响应产生较大的影响,所以为了对景观效果进行预测,同时进一步验证结构在设计风速下的安全性,在按规范进行风荷载校核之外,该文建立了一种流固耦合的计算技术,对雕塑结构进行了细致的分析。首先将该耦合方法应用于单一结构进行验证分析,通过结构顶点最大位移的计算值与规范估算公式所得值的比较验证了该文方法的准确性;在此基础上针对复杂雕塑结构进行了耦合分析,给出了在不同强风条件下结构顶点的位移响应特性;然后对不同风强条件下的安全性进行了评估,确立了该雕塑作品在实际风条件下的适用性

Furfural Refining for a Mixed Crude Oil and Its Simulation

以混合轻脱油的糠醛精制为目标,单级精制的数据为基础,建立多级糠醛精制的模型方程。应用数值算法结合原油的质量指标,模拟并讨论精制温度、醛油比和平衡级数对精制油的质量指标和收率的影响。结果表明本文的方法合理有意义。By assuming four quality items (acid number,basic nitrogen,refractive index and Aniline Point) as four components in oil, the mass balance calculation of those components in two equilibrium phases was proposed. An artificial neural networks method was developed for the simulation of the equilibrium data of furfural refining, and a model describing the refining process was also established. With the simulated e-quilibrium data, extraction temperature, the ratio of furfural to oil and the data of quanlity items of our studied mixed crude oil, the refining yield and quality items of the purified oil at various conditions were predicted successfully

Study on Extraction of Lubricating Oils by Using Furfural

实验研究糠醛精制过程中温度、醛油比对精制效果的影响.结果表明糠醛精制润滑油存在一个最优操作温度区域；糠醛精制润滑油的温度、醛油比不宜同时取较大值.Extraction of lubricating oils with furfural was studied by considering the effects of temperature and the ratio of furfural to oil.Experimental results showed that there is an optimum temperature zone for the extraction process and it′s proposed that both high temperature and high ratio of furfural to oil are unsuitable

Furfural Refining and Its Prediction and Simulation of Equilibrium By Artificial Neural Networks

[中文文摘]实验研究糠醛精制润滑油过程中温度、醛油比对精制结果的影响.把油品的四个质量指标(折光率、酸值、碱性氮、苯胺点)虚拟成四个组分,对糠醛精制过程建立平衡衡算关系.用人工神经网络算法建立多变量体系的平衡计算.应用该平衡计算结合原油的质量指标预测一定温度和醛油比下精制油的质量指标和收率,得到满意的结果.[英文文摘]The effectoftem perature and the ratio offurfuralto lubricating oilon the resultoffurfuralrefining process is studied. Experim entaldata show thatthere existsan opti- mum region oftem perature forthe refining. By assum ing the fourquality items (acid number、 basic nitrogen、refractive index and Aniline Point) as four components in oil, the mass balance calculation ofthose components in tw o equilibrium phases is established. An artificialneural networks method is developed for the simulation equilibrium of furfural refining With extraction temperature the ratio of furfural to lubricating oiland the quality items data of the given oil the refining yield and the quality items of purified oil can be predicted successfully

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies