Wavelet analysis of dynamic behavior in fluidized beds

Ren, Jinqiang; Mao, Qiming; Li, Jinghai; Lin, Weigang

doi:10.1016/s0009-2509(00)00313-4

Cited by 91 publications

(60 citation statements)

References 16 publications

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“…Using the wavelet analysis, the decomposition level was set to 8, thus the signals were decomposed into nine sub-signals, where approximation levels a1-a8 represent low frequency components and detailed coefficients D1-D8 represent high frequency components [14]. By recombining the sub-signals, three different scales belonging to different flow structures were extracted from the original signals [8]. The micro-scale, represented by the D1 to D4 sub-signals with frequency more than 10 Hz is related to particle interactions.…”

Section: Methods Of Data Analysesmentioning

confidence: 99%

“…[3][4][5][6]. Various analysis methods of the pressure signals have been developed, for both time and frequency domains, such as wavelet analysis, Fourier transform, chaos analysis etc., to extract the necessary information about the hydrodynamic behavior of the fluidized bed, namely minimum fluidization velocity, regime transition velocities, bubble characteristics or to estimate the dominant phenomena in the bed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Effect of internal tubes on the flow structures in gas-solid fluidized beds

Gubis

Norouzi

Mostoufi

et al. 2013

J. Phys.: Conf. Ser.

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Abstract. The influence of internal tubes present in the fluidized bed on its hydrodynamic behavior was examined in this study. One tube was completely immersed in the bed of 650 m glass beads at 10 cm above the air distributor in the 15 cm inner diameter fluidized bed column of aspect ratio 1. The pressure fluctuations signal was collected over 300 s and was consequently analyzed by the both statistical and wavelet analyses. The results showed that the presence of tube within the bed suppresses the occurrence of macro-scale phenomena like formation of large bubbles and its stability and supports the meso-structures such smaller bubbles, voids and clusters. It leads to an earlier onset of bubbling flow regime as well as turbulent flow regime. The observed minimum bubbling velocities were compared with data in literature and were found in a good agreement with them.

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Section: Methods Of Data Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of internal tubes on the flow structures in gas-solid fluidized beds

Gubis

Norouzi

Mostoufi

et al. 2013

J. Phys.: Conf. Ser.

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“…To use the fiber-optic measurements to distinguish between dense and dilute conditions, a method known as wavelet decomposition (Mallat 1998;Ren and Li 1998;Yang and Leu 2009) is implemented here via the wavelet toolbox in Matlab (Misiti, Misiti et al 2002). More specifically, wavelet decomposition provides a means of representing different frequencies of the raw voltage signal by repeatedly breaking down the signal into higherfrequency details (D) and lower-frequency approximations (A), as illustrated in Figure 133.…”

Section: Pitot Tube and Extraction Probementioning

confidence: 99%

“…Therefore, a critical drawback of a Fourier transform of such signals is that the time (dynamic) component is lost during analysis, and hence deficient when signal properties continuously change with time as in a fluidized bed system(Ren and Li 1998; Ellis, Bi et al 2004). In the past decade, wavelet decomposition(Mallat 1989; Mallat 1998) has been acknowledged to be useful in its ability to extract different frequency ranges while retaining the timestamp of signals, thereby enabling classification of fluidized-bed measurement data into noise (micro-scale), flow structures like clusters or bubbles (meso-scale), and equipment (macro-scale) (Ren and Li 1998;Ellis, Briens et al 2003;Zhao and Yang 2003;Yang and Leu 2009).More specifically, wavelet decomposition(Mallat 1989; Mallat 1998) provides a means of extracting different frequency ranges of data signals by repeatedly breaking down the signal into higher-frequency details (D) and lower-frequency approximations (A), as illustrated in Figure 133. At the first scale of decomposition (Scale 1), the signal of N Hz is divided into the first scale of approximation (A 1 ) and the first scale of detail (D 1 ), whereby A 1 contains the lower half of the frequency range and D 1 contains the higher half.…”

mentioning

confidence: 99%

“…Experiments have been carried out for two binary mixtures -one with only a material density, ρ s , difference between the constituent components (density-difference binary), and one with only a particle size, d ave , difference between the constituent components (size-difference binary) -as well as a continuous PSD, all belonging to Geldart Group B. Local cluster behavior is measured using a fiber optic probe and subsequent cluster identification and characterization is based on wavelet decomposition (Mallat 1989;Mallat 1998), which provides a means of delineating the different (micro, meso-, and macro) scales in fluidized bed systems (Ren and Li 1998;Ellis, Briens et al 2003;Zhao and Yang 2003;Yang and Leu 2009). In particular, the cluster characteristics extracted from fiber optic measurements include cluster appearance probability (fraction of time that clusters are observed), cluster duration (indicator of cluster size), and frequency (number of clusters per unit time).…”

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confidence: 99%

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Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

Hrenya¹,

Cocco²,

Fox³

et al. 2011

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This report describes in detail the technical findings of the DOE Award entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." The focus was on high-velocity, gas-solid flows with a range of particle sizes. A complete mathematical model was developed based on first principles and incorporated into MFIX. The solid-phase description took two forms: the Kinetic Theory of Granular Flows (KTGF) and Discrete Quadrature Method of Moments (DQMOM). The gas-solid drag law for polydisperse flows was developed over a range of flow conditions using Discrete Numerical Simulations (DNS). These models were verified via examination of a range of limiting cases and comparison with Discrete Element Method (DEM) data. Validation took the form of comparison with both DEM and experimental data. Experiments were conducted in three separate circulating fluidized beds (CFB's), with emphasis on the riser section. Measurements included bulk quantities like pressure drop and elutriation, as well as axial and radial measurements of bubble characteristics, cluster characteristics, solids flux, and differential pressure drops (axial only). Monodisperse systems were compared to their binary and continuous particle size distribution (PSD) counterparts. The continuous distributions examined included Gaussian, lognormal, and NETLprovided data for a coal gasifier.FINAL TECHNICAL 4 DE-FC26-07NT43098 5 EXECUTIVE SUMMARYThis report is the final technical report for the award DE-FC26-07NT43098 entitled "Development, Verification, and Validation of Multiphase Models for Polydisperse Flows." Below is a summary of work completed under the grant for each of the major goals. Goal I: Continuum Theory for Solid PhaseTwo separate and complementary continuum theories were derived to model polydisperse solids: the kinetic theory of granular flow (KTGF) and the discrete quadrature method of moments (DQMOM). Both theories are based on first principles with no adjustable parameters. The polydisperse KTGF and DQMOM were then encoded in MFIX, and underwent a wide range of verification testing to ensure, to the best possible extent, that no coding errors were present. These verification tests included both granular and gas-solid flows, including cases where an analytical solution and/or DEM (discrete element method) data and/or simple test cases. For the case of KTGF, another set of constitutive equations were derived which rigorously incorporated the gas phase for a simplified case of monodisperse systems at low Reynolds numbers. This derivation used the acceleration model developed in Task 2 from direct numerical simulations (DNS) in the starting kinetic equation, and indicated the effect of the fluid phase on the resulting constitutive relations. Goal II: Improved Gas-Particle Drag Laws -effect of particle size distributionIn this portion of the effort, two types of direct numerical simulations (DNS) were used to extract the drag force experienced by particles in a polydisperse suspension. These two methods, na...

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Dynamic Multiscale Modeling – An Application to Granulation Processes

Ingram

2010

Process Systems Engineering

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Wavelet analysis of dynamic behavior in fluidized beds

Cited by 91 publications

References 16 publications

Effect of internal tubes on the flow structures in gas-solid fluidized beds

Effect of internal tubes on the flow structures in gas-solid fluidized beds

Development, Verification, and Validation of Multiphase Models for Polydisperse Flows

Dynamic Multiscale Modeling – An Application to Granulation Processes

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