Characterization of the bubbling fluidization of nanoparticles

The dynamic features of an agglomerate bubbling fluidization of nanoparticles were investigated through the analysis of pressure fluctuations. Experiments were carried out in a lab-scale fluidized bed at ambient conditions using 10-15 nm silica nanoparticles without any surface modification. Pressure fluctuation signals were processed in both frequency and time-frequency domains to characterize the behavior of various scales of phenomena （i.e.. macro-, meso-, and micro-structures） during fluidization. Due to the aggregation of nanoparticles, three separate broad peaks were observed in the frequency spectra of the pressure signals measured in the bubbling fluidized bed of nanoparticles. A non-intrusive method based on the decoupling of pressure fluctuations recorded simultaneously in the plenum and in the bed was used to determine the approximate size of the bubbles in the bed.     

Nonlinear dynamical analysis of noisy time series

Empirical time series in the life sciences are often nonstationary and have small signal-to-noise ratios, making it difficult to accurately detect and characterize dynamical structure. The usual response to high noise is averaging, but time domain averaging is inappropriate, especially when the dynamics are nonlinear. We review alternative delay-space averaging methods based on the topology and short-term predictability of nonlinear dynamics and illustrate their application using the TISEAN software (Hegger, Kantz & Schreiber, 1999). The methods were applied to a Lorenz series, which resembles the dynamics found by Kelly, Heathcote, Heath and Longstaff (2001) in series of decision times. The Lorenz series was corrupted with up to 80% additive Gaussian noise, a lower signal-to-noise ratio than has been used in any previous test of these methods, but consistent with Kelly et al.'s data. Prediction methods performed the best for detecting nonstationarity and nonlinear dynamics, and optimal predictability provided an objective criterion for setting the parameters required by the analyses. Local linear filtering methods performed best for characterization, producing informative plots that revealed the nature of the underlying dynamics. These results suggest that a methodology based on delay-space averaging and prediction could be useful with noisy empirical data series.     

Weighted multifractal analysis of financial time series

In this paper, we propose the weighted multifractal analysis based on variance-weighted partition function (WMA), to evaluate the fractals of nonlinear time series containing amplitude information. The reliability of the proposed WMA is supported by simulations on generated and real-world financial volatility data. Numerical simulations with synthesized data show that WMA is comparative to the classic partition function-based standard multifractal analysis (SMA) and the multifractal detrending moving average analysis. More importantly, empirical analyses of Chinese stock indices illustrate that WMA outperforms SMA in distinguishing Hang Seng Index form SSE Composite Index and SZSE Component Index qualitatively and quantitatively, showing the power of WMA in discriminating series with distinctive amplitudes.     

Nonlinear analysis of near-wall turbulence time series

We present the results of the nonlinear analysis of a near-wall turbulence signal, measured using a Laser Doppler Anemometer. Despite being preliminary in part, the results highlight some interesting aspects of the dynamics of coherent structures. Careful reconstruction of the dynamics from the scalar time series highlight particular phases of the bursting cycle, corresponding to the VITA detections, immersed in the overall high-dimension dynamics: the possibility to distinguish these trajectories in the embedding space provides indications for conditional sampling techniques on the reconstructed attractor. The application of nonlinear prediction to a system of high-dimension produces forecasts of rapidly decreasing quality over time, with no consequences for practical applications. Much more interesting instead is the analysis of the forecast errors for very short forecast intervals, where we observed a greater difficulty to predict the beginning of coherent structure instability probably due to the quasi-random forcing of the outer flow.     

Process pathway inference via time series analysis

Motivated by recent experimental developments in functional genomics, we construct and test a numerical technique for inferring process pathways, in which one process calls another process, from time series data. We validate using a case in which data are readily available and we formulate an extension, appropriate for genetic regulatory networks, which exploits Bayesian inference and in which the present-day undersampling is compensated for by prior understanding of genetic regulation.     

Multiscale time irreversibility analysis of financial time series based on segmentation

Time irreversibility is a subject of increasing interest in an unbalanced system of various time series. Taking into account dynamic basic concepts, we provide multiscale time irreversibility analysis of financial time series based on segmentation which quantifies the time asymmetry in multiscales and is applied to several different forms of financial time series. Specifically, we adopt four distinct time irreversibility indices—Porta’s, Guzik’s and Ehler’s indices (P%, G% and E) and \(\gamma _{2,1} (k)\), respectively, derived from data segments on various timescales. We investigate the performance of our statistical tests for local financial time series from segmented series system with known time reversal properties and find out that it can help classify the partially representative financial markets finally. Particularly, the smaller the scale factor L is the better the ability to distinguish data. Statistical analysis shows a close relationship between G% and E. On the contrary, the connection between P% and G% or P% and E is not proven. In addition, we define a new metric \(\gamma _{2,1} (k)\) to measure the degree of time irreversibility. By further observing the results of the proposed method for computing the degree of irreversibility of the time series, we confirm that the asymmetry is an inherent property of the financial time series, which can be extended to a wide range of scales. Finally, we apply this method to the recurrence plot and multiscale recurrence quantification analysis, to compare effectiveness of the segmentation method.     

Drag models for simulating gas-solid flow in the turbulent fluidization of FCC particles

in the turbulent fiuidization of FCC particles, and was validated by satisfactory agreement between prediction and experiment.     

Counter-current three-phase fluidization in a turbulent contact absorber: A CFD simulation

A computational fluid dynamics study of three-phase counter-current fluidization occurring in a turbulent contact absorber was performed. A two-dimensional, transient Eulerian multi-fluid model was used, in which the dispersed solid phase was modeled employing a kinetic theory of granular flow. The grid independence of the model, the effect of wall boundary conditions, the choice of granular temperature model, the effects of order of discretization scheme and drag models were studied for a base case setting. The results of simulations were validated against experimental results obtained from the literature. Once the model settings were finalized, simulations were performed for different gas and liquid velocities to predict the hydrodynamics of the absorber. Computed bed expansion and pressure drop were compared with experimental data. Good agreement between the two was found for low velocities of gas and liquid.     

Experimental characterization and CFD simulation of gas maldistribution in turbulent fluidization of Group A particles

The study presented hereby investigates experimentally and with CFD simulations the gas distribution effect on the hydrodynamic of a Geldart Group A turbulent fluidized bed. Experiments were carried out on a cold flow fluidized bed column with an even and uneven gas distribution. Local solid volume fraction profiles were measured using optical probes at different bed heights and along two radial directions. Optical probe measurements allow catching a clear hydrodynamic difference between both even and uneven gas distributions. These results were then used to assess CFD simulations with the code Barracuda™ (MP-PIC approach). It is noteworthy that the choice of drag correlation and boundary conditions strongly influences the agreement between the experimental and CFD results. Once the correct parameters are chosen, CFD simulations captured the effect of gas distribution changes.     

基于时间序列分析的自适应联邦滤波算法

以信息源的视角审视联邦滤波信息分配系数的问题,提出基于时间序列分析的自适应联邦滤波算法。通过引入导航传感器历史数据作为信息基础,以自回归滑动平均模型对其进行分析,使信息分配系数与导航传感器工作状态和环境状态相适应,提高系统性能。将此算法应用于组合导航系统,通过仿真实验对比,新的算法比经典联邦滤波算法定位精度提高40%,比其他的自适应联邦滤波算法位置误差标准差、速度误差标准差分别平均提高19.74%和30.67%,说明该算法具有良好性能。     

Complexity analysis of the time series using inverse dispersion entropy

Nonlinear Dynamics - The primary object of this study is to measure the complexity of different types of signals. We undertake the experiment to support the hypothesis of inverse dispersion entropy...     

Circulating turbulent fluidization—A new fluidization regime or just a transitional phenomenon

While circulating fluidized bed （CFB） reactor has many advantages over the more conventional turbulent fluidized bed （TFB） reactor, it does at least have one significant shortcoming-the rather dilute solids volume concentration in CFB reactor gives rise to less ideal reaction intensity. On the other hand, while having higher reaction intensity, TFB reactor has one fatal drawback of particle back-mixing, making it not suitable for certain reactions such as catalytic reaction where the catalyst requires frequent regeneration. This paper describes some key issues in the development of a circulating turbulent fluidized bed （CTFB） reactor that combines the advantages of both TFB and CFB, that is, to have the high reaction intensity as in TFB but and also to have a suppressed solids back-mixing as in CFB due to a continuous net upflow of solids flux through the bed. Experimental results show enough evidence to suggest that a new fluidization regime is formed, the characteristics of which appears to be distinct from those observed in a regular TFB and from those in either the bottom or the upper sections of regular CFB and/or high-density CFB （HDCFB）. Fundamentally, the difference is that particle-particle interaction （collision） dominates the motion of particles in CTFB and TFB, while gas-particle interaction （drag force） is the key element that determines the two phase flow in CFB including HDCFB.     

液态金属熔池中气泡-液体两相湍流的数值模拟研究

应用欧拉-欧拉双流体模型,以及分别描述气泡和液体湍流的两相湍流模型,描述液态金属中气体射流发泡过程的两相流动及气泡分布。与实验结果的对比表明,本模型的预测能力优于前人未考虑气相湍流的模型。考查了液体物性和气泡尺寸对流场特性的影响,结果发现,液体密度越低,气泡尺寸越小,气泡分布就越均匀。     

Numerical simulation of turbulent flow through series stenoses

The flow fields in the neighbourhoods of series vascular stenoses are studied numerically for the Reynolds numbers from 100 to 4000, diameter constriction ratios of 0.2–0.6 and spacing ratios of 1, 2, 3, 4 and ∞. In this study, it has been further verified that in the laminar flow region, the numerical predictions by k–ω turbulence model matched those by the laminar‐flow modelling very well. This suggests that the k–ω turbulence model is capable of the prediction of the laminar flow as well as the prediction of the turbulent stenotic flow with good accuracy. The extent of the spreading of the recirculation region from the first stenosis and its effects on the flow field downstream of the second stenosis depend on the stenosis spacing ratio, constriction ratio and the Reynolds number. For c₁ = 0.5 with c₂ ≤ c₁, the peak value of wall vorticity generated by the second stenosis is always less than that generated by the first stenosis. However, the maximum centreline velocity and turbulence intensity at the second stenosis are higher than those at the first stenosis. In contrast, for c₁ = 0.5 with c₂ = 0.6, the maximum values at the second stenosis are much higher than those at the first stenosis whether for centreline velocity and turbulence intensity or for wall vorticity. The peak values of the wall vorticity and the centreline disturbance intensity both grow up with the Reynolds number increasing. The present study shows that the more stenoses can result in a lower critical Reynolds number that means an earlier occurrence of turbulence for the stenotic flows. Copyright © 2003 John Wiley & Sons, Ltd.     

The effects of the irregular sample and missing data in time series analysis

Human self-report time series data are typically marked by irregularities in sampling rates; furthermore, these irregularities are typically natural outcomes of the data generation process. Relatively little has been published to assist the analysis of irregularly sampled data. We report the results of a series of computational experiments on synthetic data sets designed to assess the utility of techniques for handling irregular time series data. The behavior of a conservative quasiperiodic, a dissipative chaotic, and a self-organized critical dynamics were sampled regularly in time and the regular sampling was disrupted by data point removal or by stochastic shifts in time. Missing data segments were then patched by means of segment concatenation, by segment filling with average data values, or by local interpolation in phase space. We compared results of nonlinear analytical tools such as autocorrelations and correlation dimensions using complete and patched sets, as well as power spectra with Lomb periodograms of the decimated sets. Local interpolation in phase space was particularly successful at preserving key features of the original data, but required potentially impractical quantities of intact data as a primer. While the other patching methods are not limited by the need for intact data, they distort results relative to the intact series. We conclude that irregularly sampled data sets with as much as 15 percent missing data can potentially be re-sampled or repaired for analysis with techniques that assume regular sampling without introducing substantial errors.