采用凝聚函数法对气固密相流化床流型转变过程的研究

本文对气固密相流化床中鼓泡流态化向湍动流态化转变的机理进行了研究.首次引入了凝聚函数分析法对密相压力脉动信号进行分析.结合时间域的统计分析,直接证明了这一流型转变乃是床内气泡的破碎程度超过合并程度所致.     

气固流化床压力脉动信号的Hilbert-Huang谱分析

首次将Hilbert-Huang变换（HHT）应用于气固流化床压力脉动信号的分析,提取并研究了压力脉动信号中隐含的表征复杂的粒子运动与气泡运动相互调制的非线性特征,以及压力脉动信号高、低阶内禀模态函数（IMF）之间的能量转换与流化床的流化状态相对应的新信息,在此基础上提出了采用压力脉动IMF分量的能量转移现象进行颗粒结块故障判别的新方法.结果表明,应用Hilbert-Huang谱对压力脉动信号分析的新方法能比现有的分析方法提供更多的有用信息,有助于更深入地揭示床内非线性流体动力学特征.     

气固流化床流型特性及其识别的复杂性研究

运用复杂性C2、涨落复杂性Cf及Lempel-Ziv复杂性C(n)等复杂性参数对气固流化床压力脉动信号进行分析,研究它们随流化床操作气速增大历经不同流型的变化趋势并将结果作了比较,进一步探讨了流化床流型特性的内在规律性,研究结果表明,在起始流化致鼓泡态转变的过程中,气－固体系会进行一种所谓的“重构”现象,并证实了气泡的存在是影响压力脉动信号复杂性的重要因素,同时实验显示复杂性参数能明确地指示固定床,鼓泡流化及湍动流化等不同流型之间的转变过程,为流型识别提供了新思路。     

气固流化床压力脉动信号的Hiibert-Huang谱分析

首次将Hilbert-Huang变换(HHT)应用于气固流化床压力脉动信号的分析，提取并研究了压力脉动信号中隐含的表征复杂的粒子运动与气泡运动相互调制的非线性特征，以及压力脉动信号高、低阶内禀模态函数(IMF)之间的能量转换与流化床的流化状态相对应的新信息，在此基础上提出了采用压力脉动IMF分量的能量转移现象进行颗粒结块故障判别的新方法．结果表明，应用Hilbert-Huang谱对压力脉动信号分析的新方法能比现有的分析方法提供更多的有用信息，有助于更深入地揭示床内非线性流体动力学特征．     

气-固流化床压力脉动信号的多尺度排列熵分析

采用多尺度排列熵算法对气-固流化床内4种流型的压力脉动信号进行研究,结果表明:不同流型由于其动力学特性不同,导致压力脉动信号的多尺度排列熵值存在差异——4种流花形态的排列熵值都是随着尺度的增加而增大,其中固定床在大尺度上熵值最大,气力输送床在小尺度上熵值最大,鼓泡流化床在所有尺度上熵值最小;此外,多尺度排列熵值变化速率特征是流型辨识的新指示器。     

气固流化床压力脉动信号的Hilbert-Huang变换与流型识别

采用Hilbert-Huang变换(HHT),提取出气固流化床压力脉动信号的各阶内禀模态函数(IMFs),进一步证明了压力脉动信号是由复杂的不同波间和波内频率调制成分所组成,具有气固两相运动相互调制的非线性特征。分析各阶内禀模态函数的能量分布及其转换,发现不同频段IMF的能量与流型状态之间有着很好的对应关系,能够从整体上反映流化床的流化状态,从而提出了应用IMF中频段能量进行流化床流型识别的新方法。该方法只需一个压力脉动信号,算法简单、实用,没有需要主观决定的参数,具有较好的工业应用前景。HHT分析比现有的分析方法更能深入地揭示流化床内的非线性流体动力学特征。     

隔板式内循环流化床压力脉动信号递归分析

隔板式内循环流化床中流化态及颗粒循环特性对压力脉动信号特征具有重要影响，其作用机制尚未完全清楚。测量了隔板式内循环流化床在不同气速比条件下的压力脉动信号，通过时域及递归分析，获得了压力脉动信号的标准差、递归率、确定性及香农熵等特征参数。结果表明，随着表观气速比的增加，内循环中颗粒循环状态存在未循环、鼓泡循环、过渡循环和湍动循环四个阶段；通过压力脉动信号的标准差、递归图黑白结构占比和递归特征参数可识别这四种循环状态，递归特征参数在不同循环区域内显示出良好的线性关系，可用于识别隔板式内循环流化床系统的循环状态。     

气固流化床中的信息传输

引　言压力脉动信号用于气固两相流检测已经有相当长的时间 ,一般认为压力脉动信号包含了流化床内的许多动态信息 ,是颗粒特性、床的几何特性、气泡运动特性等多种因素的综合反映 .如何有效地从中提取隐含信息也一直是一个广受关注的问题 .在这方面国内外的研究者已经做过不少工作 ,包括早期研究压力脉动信号的均值、方差、功率谱、相关函数等 ,以及后来基于信号的非平稳特性的小波、Ｗｉｇｎｅ -Ｖｉｌｌｅ谱等 ,得到了一些有用的信息 .近年来 ,随着非线性科学的发展 ,有的研究者提出流化床具有显著的混沌特性 ,Ｄａｗ等在 1 990年第 1次发…     

鼓泡流态化向湍动流态化过渡的判别

通过对流化床床层压力脉动信号的计算机在线分析,对气-固密相流化床中鼓泡流态化向湍动流态化的转变过程进行了研究.在较大范围内考察了颗粒重度、颗粒尺寸和床层结构条件对这一流型转变的影响.给出了鼓泡流化向湍动流化转变速度U_c的计算式:U_c/(gd_ρ)~(1/2)=[k(D_f/d_ρ)·((ρ_ρ-ρ_f)/ρ_f)]~n并察明Geldart的颗粒分类方法亦可反映床层流型转变的特征,从而赋予了Geldart颗粒分类方法以新的内涵.     

气固搅拌流化床压力脉动的小波分析

在内径188 mm、静床高400 mm的搅拌流化床中,采用Geldart D类颗粒为实验物料,通过小波分析研究了不同气速和搅拌桨转速下搅拌流化床的压力脉动行为.实验发现,搅拌桨的转动作用促使在普通流化床中不易散式流态化的D类颗粒形成了散式流态化.随着气速的增加,第1尺度的小波能量特征值在某一个气速范围内发生急剧变化,进而提出了将该气速范围的下限和上限分别定义为临界鼓泡速度和充分鼓泡速度的判据.随搅拌转速的增加,散式流态化的气速操作范围线性增加.在鼓泡流态化状态下,气速是流化床气泡行为的主导因素,搅拌桨转速的增加对气泡产生的频率无明显影响但可使气泡的直径变小.     

振动流化床中流动结构的混沌分析

在内径90mm、静床高800mm的高床层流化床中,用动态压力传感器检测了不同气速条件下普通流化床和振动流化床中沿轴向的压力脉动信号,通过小波变换对信号除噪后,用混沌理论对信号进行了分析.通过关联维数和Kolmogorov熵定量表征振动流化床中的流动结构特征.结果表明:压力脉动信号的关联维数和Kolmogorov熵能够描述振动流化床中的流化状态;振动流化床中床层的流动结构存在两个区,在近分布板区域为射流区,床层主体部分为均匀流化区.     

液固两相外循环流化床压力波动信号的统计及频谱分析

压力波动信号是表征流化床内流体运动特性的重要信息. 为了更好地了解液固外循环流化床内流体流动特征,在液固外循环流化床中,对床体壁面压力波动信号进行了时域、频域及自相关性分析. 结果表明,沿床体稳定流化段上的压力波动特征相似,流体流动和颗粒运动所引发的压力波动能量频带分别集中在0~10和30~40 Hz之间,压力波动的概率密度近似呈正态分布,液固两相外循环流化床中的压力波动信号介于周期信号和随机信号之间.     

基于HHT法的流化床内生物质和石英砂双组分颗粒压差脉动信号分析

采用Hilbert-Huang变换方法对流化床内的压差脉动信号进行分析,研究了不同工况下生物质和石英砂双组分颗粒的流动特征。在生物质质量分数为2%情况下对不同气速下压差脉动信号进行边际谱和能量分析,发现压差脉动信号频率主要集中在低频段(0～4 Hz),随气速的增加各阶IMF能量逐渐从高频段向中频段转移;在气速为1.01 m·s^-1情况下对不同生物质质量分数下的压差脉动信号进行能量分析,发现随生物质质量分数增加各阶 IMF能量逐渐从中频段向高频段转移;并借助高速摄影仪对流化床内的颗粒流动状态进行研究分析。结果为进一步研究流化床内气固两相流知识提供一定的理论依据。     

基于小波变换的循环流化床压力波动信号除噪分析

采用基于小波分析和分形理论的除噪方法对压力波动信号进行除噪,并将由此方法得到的信号与常用的Fourier滤波滤去20Hz、40Hz频率以上所得信号进行对比分析,结果表明,所提出的除噪方法能客观、有效地去除噪声,而Fourier滤波方法提取信号不能完全去除噪声的干扰。此外,循环流化床波动频率带随操作条件的不同而有所差异,所以在用压力波动信号对循环流化床混沌特性进行研究时,不能象Fourier滤波那样截去某个频率段。     

Testing of cold scaled bed modeling for fluidized-bed combustors

The purpose of this study was to determine whether cold scaled models with scaled particles give the correct hydrodynamic predictions for the gas and solids in an actual hot atmospheric pressure fluidized-bed combustor.Continuity equations and momentum balance equations of the gas and solid particles in fluidized beds were used in developing four dimensionless scaling groups which were utilized in sizing four different fluidized beds.Pressure fluctuations data collected from the different beds we are compared. Auto-correlation plots of the pressure fluctuations from one bed when compared with its scaled counterpart were observed to be of the proper scaled frequency. The fluctuations were of the same magnitude when scaled but the magnitudes were not statistically significant.     

Forced bed mass oscillations in gas–solid fluidized beds

A second-order mechanical vibration model is proposed to explain the oscillation phenomena and pressure fluctuations in gas–solid fluidized beds. Experiments were conducted in a 0.1-m ID gas–solid fluidized bed driven by periodic gas pulses with the frequency varying from 0.2 to 10 Hz. Experimental results indicate that the proposed mechanical vibration model, which considers the pressure fluctuation as the results of the response of the fluidized bed to the external excitation forces related to the bubble behavior in the bed, can reasonably explain the pressure fluctuations of the gas–solid fluidized bed. The behavior of pressure fluctuations is correlated with characteristics of both the bed system and excitation forces.     

Oscillations in gas-fluidized beds: Ultra-fast magnetic resonance imaging and pressure sensor measurements

Ultra-fast Magnetic Resonance Imaging (MRI) and pressure sensor measurements have been applied to study: (i) pressure fluctuations, (ii) the eruption of bubbles at the top of a bed and (iii) the formation of bubbles in a gas-fluidized bed. Ultra-fast MRI has been applied for the first time to study the formation and eruption of bubbles; the technique is non-intrusive and provides measurements with good temporal and spatial resolutions. The MRI measurements revealed that bubbles are formed periodically, rather than randomly at a distributor, which in this case was a perforated plate. The frequency at which bubbles erupted from the top of the bed matched the frequency of the pressure fluctuations measured just above the distributor, where the measured pressure is predicted very well for the case of slug flow by Kehoe and Davidson's [P.W.K. Kehoe, J.F. Davidson, Pressure fluctuations in slugging fluidized beds, AIChE Symp. Ser. 128 (69) (1973) 34-40] correlation, originally developed for locations high up a bed. Both findings lead to the conclusion that the passage and eruption of bubbles at the top of a bed are the dominant cause of the pressure fluctuations, which are subsequently propagated downwards through the bed. Two new correlations are proposed for predicting the frequency of pressure fluctuations in a bubbling bed; both correlations agree well with experimental measurements. A modification of Baeyens and Geldart's [J. Baeyens, D. Geldart, An investigation into slugging fluidized beds, Chem. Eng. Sci. 29 (1974) 255-265] correlation predicts the frequency of pressure fluctuations when slugs are formed, but are not fully developed. The frequency of bubble formation, as measured by MRI, is equal to or higher than both the frequency of bubble eruption at the top of the bed and the frequency of pressure fluctuations, depending on the depth of the bed. The frequency of bubble formation is significantly lower than predicted by Davidson and Schüler's [J.F. Davidson, B.O.G. Schüler, Bubble formation at an orifice in an inviscid liquid, Trans. Inst. Chem. Eng. 38 (1960) 335-342] equation, originally developed for gas-liquid systems.     

Effect of cohesive powders on pressure fluctuation characteristics of a binary gas-solid fluidized bed

The effect of cohesive particles on the pressure fluctuations was experimentally investigated in a binary gas-solid fluidized bed. The pressure fluctuation signals were measured by differential pressure sensors under conditions of various weight percentages of cohesive particles. The cohesive particles increased the fixed bed pressure drop per unit height and decreased the minimum fluidization velocity. The Wen & Yu equation well predicts the minimum fluidization velocity of the binary system. The addition of cohesive particles slightly decreased the bubble size in bubbling flow regime when the cohesive particles and the coarse particles mixed well, while the bubble size greatly decreased when the cohesive particles agglomerated on the bed surface. The time series of pressure fluctuations was analyzed by using the methods of time domain, frequency domain and wavelet transformation. The normalized standard deviation of pressure fluctuations decreased with increasing weight percentages of cohesive particles. A wide bandwidth frequency of 0 to 1Hz got narrower with a single peak around 0.6Hz with an increase in proportion of the cohesive particles. The meso-energy and micro-energy of pressure fluctuations were decreasing with increasing cohesive particles proportions, which indicated that adding cohesive particles could reduce the energy dissipation of bubble and particle fluctuations.     

气固流化床压力脉动信号的相关结构模型与分析

建立了气固流化床压力脉动信号的相关结构模型。基于小波变换确定了模型中的自相似参数H和白噪声强度σ_w~2,从而确证该压力脉动信号可以分解成分形布朗运动和白噪声的叠加,并定量计算出压力脉动信号中白噪声所占的百分比,在此基础上,研究了模型参数在不同操作条件下的变化规律,并给出物理解释。