宁波港北仑港区冲淤里初探

本文根据北仑港区的水文资料,运用三种不同方法进行初步的冲淤计算。计算成果与北仑港区地形测量资料比较一致。分析了水动力条件,床面切应力以及地形等资料,认为目前本区是处于稳定阶段,水流足能把来沙带走。     

复杂气固两相系统的微观结构

流化床中的气固两相流动是一个高度复杂的非线性混沌系统。本文利用激光粒子动态分析仪（ＰＤＡ）得到的循环流化床中颗粒脉动速度信号,采用ＦＦＴ分析了脉动信号的宽频谱特征,在此基础上应用小波法分析了脉动信号的动态特征,得到了颗粒脉动速度的微观结构,指出颗粒脉动速度的非线性特性是流化床具有混沌特性的根源,且在不同的尺度上颗粒脉动速度表现出各向异性的特征。     

移动床热煤气脱硫气固反应过程模拟——Ⅱ-错流移动床非催化气固反应过程模拟

基于前文错流移动床反应器模型方程,模拟计算并分析了该类反应器中气、固相流动对热煤气脱硫等非催化气固反应过程的影响。研究表明,床层在床深方向按反应速率的快慢可分为粗脱区和精脱区,在颗粒流动方向上气相浓度差异较大,并主要体现在粗脱区内,床层出口处颗粒转化率呈现较大分布,反应器内气固交错流动、气相浓度和颗粒转化率共同作用（ 于气固反应速率） 等因素是造成过程特征的主要因素。因此反应器优化应满足对两相流动的优化,将粗脱区设置成错流移动床而精脱区设置成固定床,并使粗脱区内颗粒流速沿气流方向逐渐减小,以减小出口颗粒转化率的分布并提高颗粒利用率,同时沿颗粒流动方向应逐渐减小过床气流体积分率以利于床内气固反应速率的均一分布。由此指出对该类床型其底部渐缩下料段和过床气流对床内颗粒流动的影响以及床层结构及颗粒流动对过床气流分布的影响研究的必要性。     

双射流流化床流动特性的研究

在一300×2600mm二维双射流流化床中,采用多路压力信号采集装置,详细研究了射流气速、射流喷口管间距离、静床高度、物性参数对双射流流化床射流流动特性的影响,发现双射流从单独存在到两射流在其射流区内发生射流合并可由压力波动时间序列的功率谱主频和Hurst指数的变化定量确定,结果还表明,双射流流化床管间距减小时,射流在射流区发生合并的射流气速降低;而管间距相同时,静床高变大,射流在射流区发生合并的射流气速也降低;对于相同粒径的固体颗粒,颗粒密度增大使得射流在射流区发生合并的射流气速变大。     

流化床中生物质热解气化的模型研究

对水蒸汽和氮气流化条件下，流化床内生物质热解气化生成的纯煤气产率及低位热值随反应温度的变化特性进行了研究。在五种生物质原料实验数据的基础上，进一步研究了流化床内生物质热解气化生成气体产物的反应动力学模型，得到了纯煤气产率和热值的计算公式，并推荐了循环流化床条件下生物质热解的计算方法。     

应用电容层析成象和压差对比法对流化床内固体颗粒浓度分布的测量研究

本文报告了一种可以用于对流化床内固体浓度分布测量的电容层析成象技术。应用这一技术的测量结果,与目前常用的压差法测量结果进行了对比。对在鼓泡流化床的测量结果,二者吻合较好,偏差在８．５％以内;随着流化速度增加,二种测量结果的差距逐渐增加,偏差最大值产生在腾涌流化状态。本文分析了造成偏差的原因,并对二种测量方法的特性进行了讨论。     

流化床中瞬态颗粒浓度信号的小波分析

气固流化床内一般处于非均匀的聚式流化状态,系统中颗粒和流体分离严重,呈现流体密集的稀相和颗粒聚集的密相共存的两相结构。床内的颗粒流体两相流的主要特征在于其非均匀的两相时空动态结构,该系统在大多数的情况下处于非线性、非平衡的状态,是一种典型的耗散结构［１］。本文介绍应用小波分析技术对流化床中瞬态颗粒浓度信号进行分析,以揭示这种结构的本质特性。     

明渠层流近床面颗粒拖曳力研究

泥沙颗粒受到的拖曳力是泥沙运动的主要驱动力,而当前应用于计算流体力学-离散颗粒法(CFD-DPM)耦合模型进行水沙运动模拟的泥沙颗粒拖曳力公式均没有考虑明渠流底床边壁作用的影响。求解不可压缩Navier-Stokes方程,对明渠层流不同雷诺数条件下床面附近不同高度处颗粒所受拖曳力进行了模拟,根据模拟结果变化规律,提出了综合考虑床面和水流惯性对标准拖曳力影响的修正拖曳力计算公式。与常用的单颗粒标准拖曳力公式和考虑遮蔽效应的多颗粒拖曳力公式相比,采用本文修正公式得到的水沙作用力更接近高精度数值解,应用于CFD-DPM输沙模拟获得的输沙结果与输沙率公式结果一致,应用分析表明输沙模拟应当采用粗糙底床边界。     

Influence of cycle time distribution on coating uniformity of particles in a spray fluidized bed by using CFD-DEM simulations

Cycle Time Distribution (CTD) plays a critical role for determining uniformity of particle coating in spray fluidized beds. However, the CTD is influenced by both geometrical structure and operating conditions of fluidized bed. In this study, a spray fluidized bed of coating process is simulated by a comprehensive Computational Fluid Dynamics-Discrete Element Model (CFD-DEM). To achieve different behaviors of CTD, some modifications are designed on a pseudo-2D internally circulating fluidized bed, which traditionally composes of a high-velocity upward bed and low-velocity downward bed. These modifications include making the air distributor slope and/or laying a baffle in the downward bed. First, the CTD and evolution of particle size distribution under different bed structures are compared. The CTD directly influences the coating uniformity. By making the particles flowing along a parallel direction in the downward bed through the geometrical modifications, the CTD becomes narrower and the coating uniformity is significantly improved. Second, under the optimized bed structure, the influence of operating conditions on the coating uniformity is studied. Properly increasing the fluidization gas velocity and the fluidization gas temperature and reducing the liquid spray rate can improve the coating uniformity.     

An image processing method for feature extraction and dynamic tracking of particle clusters in CFBs

A new image processing method based on the high-speed camera is proposed to identify, locate, and track clusters. The instantaneous characteristic parameters of particle clusters in the riser of the circulating fluidized bed (CFB) can be acquired, such as solids holdup, vertical velocity, lateral displacement, aspect ratio and near-circularity. Experiments were carried out with glass bead particles, river sand particles and FCC particles. The time series of images of gas–solid flow in a CFB riser with a 100 mm × 25 mm cross-section and 3.2 m in length were obtained using high-speed cameras. The k-means++ clustering algorithm is utilized to identify the clusters, centroid is applied to locate the clusters, and the cross-correlation algorithm is employed to track the specific clusters and number them to get the instantaneous characteristic parameters. The results illustrate that the shapes of clusters in the center area are closest to circle, moving upwards at a uniform speed, while the clusters in the side-wall area are mostly elongated or long chain-like, moving slowly downwards. In the transition area, the clusters are more complex, moving upwards at a constant speed, and having large lateral displacement. The results show that the image processing method used in this study is successful in acquiring the dynamic and structural parameters of the clusters simultaneously.