Observation and Dynamical Analysis of Hydrographic Characteristics in the Pearl River Estuary in summer of 2015

本文以实测CTD数据为基础，总结了2015年夏季珠江口海域及其冲淡水扩展路径上的水文特征。在结合遥感风场和定点浮标风场数据的基础上，分析了台风“莲花”影响下珠江口水体的温、盐度特征，以及珠江冲淡水扩展方式对于风场变化的响应情况。同时对航次过程中观测到的温度逆转现象的特征进行统计分析；并结合遥感数据对冲淡水影响区域的海表温度数据和叶绿素a浓度的分布特征进行研究。 2015年7月6日至17日珠江口航次CTD资料的分析表明：在香港的西南侧观测到相对的低温高盐中心；珠江口附近海域存在海水的垂向逆温现象，逆温差平均值为0.42℃，上界深度在1m-6m间，下界深度在3m-10m间，逆温层平均厚度约为4m...On the basis of the in-situ CTD data, the hydrological characteristics and the expanding patterns of the Pearl River Diluted Water (PDW) have been studied. The paper has also analyzed the influencing factors with the help of the remote sensing and in-situ wind data. Then the sea surface temperature and Chl-a distributions have been applied to reveal the PDW. Meanwhile, the temperature inversion al...学位：理学硕士院系专业：海洋与地球学院_物理海洋学学号：2232014115128

Particle Swarm Optimization Clustering Algorithm with Chaos Search

聚类可以看成是寻找K个最佳聚类中心的过程。文中把一组聚类中心视为一个粒子(P),把各个数据到各自聚类中心的欧式距离之和看成优化函数(f(P)),使用带混沌搜索的粒子群聚类算法(C-PSO)算法寻找最优函数值,从而找到最佳聚类中心。该算法改进了粒子速度的初始化,把混沌搜索嵌入到粒子群的搜索过程中,提高了粒子群的搜索能力。实验结果表明,该算法的聚类效果明显好于K-means和PSO聚类。Clustering can be regarded as the process of finding K optimal centers.Considered that a group of centers can be seen as a particle(P),and the sum of Euclidean distance between data and its clustering center as optimal function(f(P)),and then using particle swarm optimization clustering algorithm with chaos search to find the optimal function value,so as to find the optimal centers.This algorithm improved on initialization of particle velocity,embeding the chaos search into particle search,so improved the capability of global search of particle swarm.The experiment showed that the clustering result of this algorithm was better than K-means and PSO clustering

基于Fluent的榨汁机流场模拟及优化

针对企业生产的刀片式榨汁机存在果料沉淀,出汁率不高的情况,运用fluEnT6.3对其内部流场进行模拟,分析整体流场分布情况及刀片组切割范围内的流体流动情况,以榨汁机杯体和刀片组作为研究对象,分别就杯体外形和刀片扭转角度提出改进方案,为榨汁机的设计和优化提供参考

2015年7—8月珠江冲淡水扩展特征的观测与分析

以航次实测温、盐剖面数据为基础,对2015年7—8月珠江冲淡水扩展特征进行观测,并结合定点浮标风场以及漂流浮球的数据,对影响其扩展特征的主要因素进行分析,从而为研究珠江口碳、营养盐等物质的输移、分布等过程提供基础资料.研究发现珠江冲淡水调查期间存在两种扩展形态:离岸东向扩展和沿岸西向扩展.在西南季风稳定时,冲淡水主要以离岸东向扩展为主,水平扩展范围距珠江口超过350km且垂向覆盖厚度(即盐度小于32的水层厚度)约为5～20m,表层32等盐线与南海北部陆架内50m等深线一致.夏季稳定的西南季风有利于珠江冲淡水在Ekman输运下持续被带离河口,当珠江冲淡水跨越20m等深线后受到沿岸东北向流和离岸运动的共同作用,以东向扩展为主;但在西南季风减弱甚至发生风向的变化时,珠江冲淡水的离岸运动也随之减弱,从而在20m等深线以内的珠江口区域形成堆积,在地转偏向力的作用下,此时珠江冲淡水的扩展形态以沿岸西向为主.国家重点基础研究发展计划(973计划)(2015CB954004);;国家自然科学基金(U1405233,41776027

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