Group Title: 7th International Conference on Multiphase Flow - ICMF 2010 Proceedings
Title: P3.55 - Spectral analysis of dynamic behaviour of the continuous phase in liquid fluidized bed
Full Citation
Permanent Link:
 Material Information
Title: P3.55 - Spectral analysis of dynamic behaviour of the continuous phase in liquid fluidized bed Fluidized and Circulating Fluidized Beds
Series Title: 7th International Conference on Multiphase Flow - ICMF 2010 Proceedings
Physical Description: Conference Papers
Creator: Kechroud, N.
Brahimi, M.
Djati, A.H.
Publisher: International Conference on Multiphase Flow (ICMF)
Publication Date: June 4, 2010
Subject: liquid fluidization
laser anemometry
spectral analysis
multi-resolution analysis
coherent structures
Abstract: The spectral analysis of dynamic behaviour of the continuous phase in liquid solid fluidized bed is characterized through velocity measurements by laser anemometry at the top of the bed. The experiments were conducted using glass particles of 2, 4 and 8 mm diameter fluidized by water. The spectra analysis of the velocity time series has revealed a specific spectral dynamic of liquid fluidized bed for the high frequencies range which does neither follow strictly the kolmogorov law nor a Brownian process power law. The continuous phase does not reach a fully developed turbulence as is the case for single phase high Reynolds number flow. A time frequency-scale decomposition combined to an autocorrelation analysis of velocity signal was pertinent to capture the impact of porosity waves and cooperative movements of particles on the liquid phase dynamic, and to characterize these coherent structures by low frequency-scales (below 1 Hz). The results compare well with the available data obtained directly from void propagation studies by light transmission techniques (el-kaissi and Homsy (1976), Ham et al. (1990), Poletto et al. (1995)).
General Note: The International Conference on Multiphase Flow (ICMF) first was held in Tsukuba, Japan in 1991 and the second ICMF took place in Kyoto, Japan in 1995. During this conference, it was decided to establish an International Governing Board which oversees the major aspects of the conference and makes decisions about future conference locations. Due to the great importance of the field, it was furthermore decided to hold the conference every three years successively in Asia including Australia, Europe including Africa, Russia and the Near East and America. Hence, ICMF 1998 was held in Lyon, France, ICMF 2001 in New Orleans, USA, ICMF 2004 in Yokohama, Japan, and ICMF 2007 in Leipzig, Germany. ICMF-2010 is devoted to all aspects of Multiphase Flow. Researchers from all over the world gathered in order to introduce their recent advances in the field and thereby promote the exchange of new ideas, results and techniques. The conference is a key event in Multiphase Flow and supports the advancement of science in this very important field. The major research topics relevant for the conference are as follows: Bio-Fluid Dynamics; Boiling; Bubbly Flows; Cavitation; Colloidal and Suspension Dynamics; Collision, Agglomeration and Breakup; Computational Techniques for Multiphase Flows; Droplet Flows; Environmental and Geophysical Flows; Experimental Methods for Multiphase Flows; Fluidized and Circulating Fluidized Beds; Fluid Structure Interactions; Granular Media; Industrial Applications; Instabilities; Interfacial Flows; Micro and Nano-Scale Multiphase Flows; Microgravity in Two-Phase Flow; Multiphase Flows with Heat and Mass Transfer; Non-Newtonian Multiphase Flows; Particle-Laden Flows; Particle, Bubble and Drop Dynamics; Reactive Multiphase Flows
 Record Information
Bibliographic ID: UF00102023
Volume ID: VID00539
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: P355-Kechroud-ICMF2010.pdf

Full Text

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Spectral analysis of dynamic behaviour of the continuous phase in
liquid fluidized bed

Kechroud Nassima, Brahimi Malek and Djati A.Halim
Department of process engineering, Faculty of Technology, University A.MIRA of Bejaia,
Bejaia, Bejaia, Algeria
Kechroud.nassima(, mk brahimi(, diatihalim@(

Keywords: Liquid fluidization; laser anemometry, spectral analysis; multi-resolution analysis,
Coherent structures

The spectral analysis of dynamic behaviour of the continuous phase in liquid solid fluidized bed is
characterized through velocity measurements by laser anemometry at the top of the bed. The experiments
were conducted using glass particles of 2, 4 and 8 mm diameter fluidized by water.
The spectra analysis of the velocity time series has revealed a specific spectral dynamic of liquid fluidized
bed for the high frequencies range which does neither follow strictly the kolmogorov law nor a Brownian
process power law. The continuous phase does not reach a fully developed turbulence as is the case for
single phase high Reynolds number flow. A time frequency-scale decomposition combined to an
autocorrelation analysis of velocity signal was pertinent to capture the impact of porosity waves and
cooperative movements of particles on the liquid phase dynamic, and to characterize these coherent
structures by low frequency-scales (below 1 Hz). The results compare well with the available data obtained
directly from void propagation studies by light transmission techniques (el-kaissi and Homsy (1976), Ham
et al. (1990), Poletto et al. (1995)).

1. Introduction

Fluidization is an operation which
involves the suspension of solids in contact with
gas, liquid or both. A large variety of processes of
interest to chemical industry and biotechnology is
concerned with fluidized bed operations.
Examples include crystallisation, ion exchange,
adsorption as well as chemical reaction. It is of
great interest to understand the dynamics of
fluidized beds for a good control and optimal
design of the process as well as for its realistic
simulation. To this end, experimental studies have
been conducted to highlight some of the physical
mechanisms of the unsteady behaviour of
fluidized beds and many of them have concerned
the dispersed phase dynamics. A detailed analysis
of the flow regimes and transition in liquid
fluidized beds has been conducted by Didwania
and Homsy (1981). Using an optical technique,
they characterized four distinct regimes, in terms
of the time and length scales of the particle
motion. The regimes include wavy flow, wavy
flow with transverse structure, fine scale turbulent

flow and bubbling states in the order of decreasing
solid fraction. Digital video recordings were used
by Poletto and al. (1995) to obtain voidage
distribution in a narrow rectangular fluidized bed.
From these recordings, the authors determined
temporal and spatial auto-correlations, the
corresponding spectra, and found that the porosity
waves are characterized by frequency-scales lower
than 1 Hz, and the dynamic becomes more chaotic
when the porosity increases. Zenit and Hunt
(2000) analysed the non-steady component of the
volume fraction signal obtained from an
impedance volume fraction meter. The root mean
square (RMS) of the void fluctuations and their
spectra have been analysed and show also that the
wave-like bands of low concentration are
confined in low frequency scales while high
frequency fluctuations are more random in nature.
To realise a finer analysis and to further
exhibit some other aspects related to the unsteady
behaviour of fluidized beds, researchers have
introduced multi-resolution analysis (Percival and

Walden (2000) and quantitative methods of
dynamical systems (Abarbanel ( 1996), Kantz and
Schreiber (1997)).
Till now, these tools have been used to
study gas fluidized beds dynamics from
essentially pressure fluctuation recordings (Ellis
and al. (2003), Sasic and al. (2006), Kulkami and
al., (2001); Lu and al. (1999); Guo and al. (2002))
and rarely used for liquid fluidized beds. Despite
their valuable contribution to improve our
understanding of the bubbles dynamic and to give
a finer characterization of the fluidization
regimes, there is a consensus about the need to
investigate further the origin and nature of large
and small scale motions in both phases as pointed
out by Johnson and al. (2000) and by Ellis and al.
(2003), among others.
The best experimental approach to have
access to the different significant scales of motion
is to do local measurements capturing, with
enough resolution, either the continuous phase
dynamic or the particles dynamic. We observe a
severe lack of experimental studies on the
continuous phase dynamic probably because of
the practical difficulties to do measurements
inside the bed with a good reliability.
Nevertheless, we can mention the work of
Handley and al. (1966) who made measurements
inside a liquid fluidized bed with a micro Pitot
tube, but recognize the inaccurate estimation of
turbulence characteristics, and the work of
Bernard and al. (1981) who used laser
anemometry in a completely transparent liquid
fluidized bed. These latter authors has considered
two porosities and limited the turbulence study
only to the RMS of the velocity fluctuations of the
liquid phase and did not conduct a spectral
analysis of the dynamic.
This paper reports the results of a spectral
analysis of liquid velocity fluctuations obtained by
laser anemometry. The measurements have been
done just above the top of the fluidized bed
considering, by the frozen velocity field
hypothesis of Taylor (Monin and Yaglom (1975)),
that they represent with a good approximation the
liquid phase dynamic inside the bed. Fourier
spectra are studied in different operating
conditions and the high frequency decaying range
is compared to characteristic power laws of
equilibrium inertial sub-range (Kolmogorov) and
of Brownian type process. To precise and
determine the origin and nature of low and high
frequency scales, we have proceeded to multi-

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

resolution analysis of the velocity time series.
Coherent and random motions have been
identified and characterized by their frequency
scales. When possible, we have found useful to
reveal the similarity between the liquid and
dispersed phase dynamics.


Archimedes number
particle diameter
superficial velocity
minimal fluidisation velocity
terminal velocity

Greek letters

particle density
fluide density
porosity or void fraction

2. Experimental set-up and analysis methods

2.1 Experiments

A schematic diagram of the experimental
set-up of fluidized bed is shown in Fig 1. The
column was made of Plexiglas with an inner
diameter of 9.3 cm and a height of 2m. It was
marked with a graph paper along its length for
measuring the height of the bed, a parameter
which is used for determining the bed void
porosity (s). Particular care was taken to obtain
uniform fluid distribution at the base, for this,
several assembly distributors were tested, and we
found that a fine wire mesh preceded by an empty
calming section provide uniform distribution of
water (Kechroud (2000)).
Water was pumped from a storage tank
through calibrated rotameters and admitted at the
bottom of the column through a distributor
assembly. A pair of rotameters was used to cover
wide range of liquid flow rate. The water exited
from the top of the column and recirculated back
to the storage tank through an overflow weir. All
piping was made in PVC and had 28 / 32 mm
Table 1 gives the properties of the beads under
investigation as well as the range of flow rates
covered for each bead size, in addition to
experimentally obtained values of bed voidage

and liquid velocity at minimum fluidization
The velocity of the liquid phase has been
measured by Laser Doppler Anemometer (LDA).
The LDA set up include a Dantec 55 X modular
series along with instrumentation such as an
electronic frequency shifter connected to the Brag
cell and a counter which delivers the validated
data. The light source is an argon-ion laser and the
power of emitted beam can be regulated up to 5W.
Beams from the splitter optics passed the focusing
optics to intersect and form a measuring volume,
just above the top of the fluidized bed, on the axis
of the column. For each superficial velocity, the
measuring volume was displaced upward as the
bed height increases in order to avoid crossing the
volume by the particles. In all the experiments, tap
water was used as working fluid. No additional
tracer particles were added. The natural seeding of
tap water was sufficient to our purposes. Two
modes of sampling frequency allowed by the
LDA technique can be used to generate time
series of the velocity: random and fixed sampling
frequencies. We have investigated the effect of
signal sampling rate and sample size on the
reproducibility of the mean velocity and the root
mean square of the velocity fluctuations values in
different operating conditions (Kechroud (2000)).
The results presented in this paper are obtained
from time series of 10240 data points with a data
rate of 20 Hz for the fixed sampling frequency
mode, the only mode used for the spectral and
multiresolution analysis because time spacing of
the data must be constant as required by the
algorithms (Percival and Walden (2000),
Nievergelt (2001)). Several authors have observed
that the significant frequency content is below 10
Hz and then, a sampling frequency of 20 Hz is
sufficient (Didwania and Homsy (1981); Poletto
and al. (1995), El-Kaissy and Homsy (1976);
Johnson (2000)).

2.2 Analysis methods

A power spectral density estimation of the
fluctuations has been conducted to reveal the
energy distribution among the different
frequencies, and the spectral shape of the liquid
phase dynamic. The results of the Fourier spectra
have been obtained using Welch's method, in
order to reduce the variance of power spectral
density estimation (Condy (1988). The estimation
is based on an average of several subspectra.

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Therefore, the whole time series is divided into
segments of equal duration and the power
spectrum of each segment is calculated. The
averaged power spectrum is given by
P,(f) = Pd(f)
where N is the number of segments and P' sdO) is
the power spectrum of segment i.
In the present study, N = 20, then one segment
contains 512 data points of 25.5 s duration, and
the frequency resolution is 0.04 Hz.
In addition to Fourier spectral analysis
presented in this paper, we have also conducted a
multiresolution analysis of the velocity signal.
Our purpose is to quantify the frequency scales of
the coherent structures which appear in the
unsteady behaviour of liquid fluidized beds. This
structures may be the porosity wave like
movements, clusters of particles with cooperative
movements and voids as observed by several
authors with increasing porosity (Didwania and
Homsy (1981), Poletto and al. (1995) ; El-Kaissy
and Homsy (1976). To this end, we need a time
frequency scale decomposition of the velocity
time series combined to an autocorrelation
analysis (Percival and Walden (2000), Li (1998)).
The wavelet transform allows localization,
both in the time and frequency-scale domains via
translation and dilation of the wavelet (Mallat
Such a representation gives a multi-resolution
framework for analyzing the phenomena present
in a time series. In practice, the discrete wavelet
transform is a pair of digital filters, which
decompose a signal into a low frequency
component A1 (called the approximation) and a
high frequency part Di (called the detail) (Percival
and Walden (2000)). In the next step, the
approximation A1 is used as an input, and by
performing this operation recursively up to a level
k, a hierarchical representation of the signal is
u(t)= DJ +Ak

The detail Dj contains frequency information in
the band [f,/2'+1,f,/2'] where fs is the
sampling frequency and j is an integer. Each of
these frequency bands defines a frequency-scale.
Finally, the original signal can be reconstructed
from wavelet coefficients by the inverse wavelet

transform without losing information (Percival
and Walden (2000). In this study, we have used
orthogonal Daubechies wavelets of order five (Db5)
as the mother wavelet, following some authors such
as Ellis and al. (2003).
The autocorrelation function, which
expresses the relation of earlier to later values in
the time series, is applied to different levels of
signal decomposition in order to show the
coherence of the corresponding frequency-scale
movements. The correlation between two points
separated by a time lag kAt is defined by:
DD (k) = Z(D (n) D )(D (n k) Dj )

This becomes, when normalized with the value at
zero lag, cDD (O)

CDD (k) CDD (k)

We note that the calculations of the above
quantitative characteristics were made using
Matlab toolboxes.

3. Results and discussion

3.1 Spectral analysis

Power spectra of time fluctuations of local
liquid velocity have been determined for different
flow conditions and the results for three particle
diameters are reported on figures 2-a)-b)-c). The
shape of the spectra shows two distinct ranges of
energy distribution; an energy containing range
with no distinguishable peak, and a decaying
energy range. We observe, when the flow rate
grows, that the energy containing range includes
progressively higher frequencies. But in all cases,
the most energetic movement scales of the liquid
phase are confined in low frequencies, less than 2
Hz. We note also a slight change in the slope of
the decaying range, more clear for the 2 mm
particles, with increasing superficial velocity or,
equivalently, increasing particle Reynolds
number. This dynamic change is not noticeable
beyond U/Umf = 2 and the spectra are similar. For
comparison, we have reported on the figures,
some characteristic power laws, -2 for some
stochastic processes and the famous -5/3 power
law of Kolmogorov.

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

The Kolmogorov slope characterizes the so
called inertial sub-range of the spectra in single
phase high Reynolds number. For the inertial sub-
range to appear, even in single phase turbulence,
there must be a large separation of scales, several
decades, between the low and high frequencies,
which is obtained at high Reynolds numbers. This
is necessary to claim that the flow has reached a
fully developed turbulent state. The spectra of the
liquid phase fluctuations obtained in the present
study exhibit only one decade of separation
between low and high frequencies because of the
low and moderate values of the Reynolds number.
It is not sure th'i the continuous phase
fluctuations of fluidized bed could reach a
universal equilibrium state in the decaying range
of the spectra.
In the system under investigation in the present
work, the Reynolds numbers based on the
superficial velocity varies from 49 to 250, 175 to
700 and%820 to 1750 for the particles of 2 mm, 4
mm and 8 mm respectively. These values are far
from representing those encountered in developed
turbulent flows and, in the case of the flow around
a single sphere, the flow structure shows different
dynamics at moderate Reynolds numbers (Clift
and al. (1978), Qiang and al. (1994), Tighzert,
(2002), Benabbas and al. (2003), Veldhuis and al.
(2004)). If we increase the flow rate to obtain
higher Reynolds numbers, the fluidized state will
rapidly reach its limit and hydraulic transport of
particles will take place (Hadinoto and
Curtis,(2009)). So, the fluidization state will
almost always be characterized by low and
moderate Reynolds numbers. We believe that the
multiple hydrodynamic interactions between the
particles and the liquid phase with additional
mechanisms of generating fluctuations, other than
the classical interaction between Reynolds
stresses and the mean velocity gradients, and a
specific energy transfer between scales prevent
probably from a rigorous observation of the
Kolmogorov law. Thus, the -5/3 power law does
not apply strictly in the decaying range of the
spectra although it is approached in some
The -2 power law is observed for some stochastic
processes as those approached by an Omstein-
Uhlenbeck model (Pope (2000)). This type of
process includes, in addition to a deterministic
contribution, a Brownian motion dynamic which
is a completely uncorrelated movement at small
scales. The spectra of the liquid phase dynamic

show that this power law is not strictly followed
too. The particles when observed carefully
suggest effectively a stochastic approach for the
mathematical modelling of their dynamics and, by
coupling, the continuous phase too. This
comparison with two characteristic dynamics at
high frequencies reveals the complex specific
behaviour of fluidized bed. We face a non-
equilibrium system in statistical mechanical sense.
Unfortunately, there is a severe lack of
investigations on spectral analysis of liquid phase
fluctuations and we cannot compare directly our
results with others, even obtained in different
operating conditions (kechroud and al. (2000)).
But, for void fluctuations analysis, several works
have been conducted (Didwania and Homsy
(1981), Polleto and al. (1995), Zenit and Hunt,
2000; Ham and al., 1990; El-Kaissy and Homsy
(1976)). It is interesting to show the similarities
and differences in the frequency domain between
the liquid velocity and void fluctuations in the
spectral representation.
Zenit and Hunt (2000) have presented spectra in
the same form as those reported on figures 2, but
have not smoothed them by averaging. Their
results show an energy containing range which
becomes wider with the superficial velocity, and a
decaying range, for different sphere diameters and
densities. The low frequency events do not exceed
3 Hz. These results are qualitatively similar to
ours. But the slope of the decaying range where
the high frequency random fluctuations are
confined, as indicated by the authors, seems to be
higher than -2 in practically all the operating
conditions. This power law is quite different from
those we have obtained. Poletto and al. (1995)
who also analysed void fluctuations and spectra,
observe qualitatively the same frequency range
subdivision and confine the coherent dynamic,
which is related to porosity wave composed of
different wave lengths, in the frequency range
below 1 Hz. The decaying range of high
frequencies present a slope rather lower than -2,
different from that obtained by Zenit and Hunt
(2000). We would like to note that the
measurements done by Zenit and Hunt (2000) are
cross sectional averages and seem to produce a
screening effect for high frequencies and so,
observes a more rapid decreasing frequency
range. In contrary, Poletto and al.(1995) have
done the measurements in a column of three
particle diameter depth and so resolve more
accurately the high frequencies.

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Didwania and Homsy (1981) performed a detailed
study of the spectral behaviour of void
fluctuations using a light transmission technique.
The range of the significant frequencies does not
exceed 10 Hz. The spectra at low porosities are
dominated by a single peak at a frequency less
than 1.5 Hz, characterizing a porosity wave
propagating through the bed. Peaks tend to flatten
at intermediate and higher voidage indicating a
change in the dynamic and the presence of
different wave scales. The spectra of the liquid
phase fluctuations do not contain a clear peak at
low frequencies but this does not mean that there
is no impact of porosity wave on the liquid phase.
We need a time frequency-scale decomposition of
the fluctuations combined to an autocorrelation
analysis, to exhibit the coherent character of the
dynamic and to specify its frequency-scale (Li,
1998). This is what we present and discuss in the
following section.

3.2 Multi-resolution analysis

We can consider the fluid phase dynamic as
the result of more or less coherent low frequency
movements, but deterministic, combined to
random fluctuations of higher frequencies
characterising stochastic processes. There is
actually a growing interest for stochastic
modelling in fluid mechanics (Pope (2000),
Bamdorf-Nielsen and Schmiegel (2005),
Lamorgese and al. (2007), Balachandar (2009))
but we need further experimental guidance to
precise this new approach of modelling for
fluidized beds. Careful visual observations and
reported quantitative video measurements of the
behaviour of fluidized beds (Didwania and Homsy
(1981), Poletto and al. (1995)), suggest that the
coherent part of the dynamic has essentially two
origins: a porosity wave, from planar to complex
one, and a large scale coherent movement of
clusters of particles and voids (called bubbles),
which are observed at high porosities. These
movements may be called cooperative movements
of the particles and are characterized effectively
by low frequencies as reported by several authors
who analysed the fluctuations of the porosity in
different operating conditions (Didwania and
Homsy (1981), Poletto and al. (1995), Ham and
al. (1990), El-Kaissy and Homsy (1976)). The
random part of the dynamic is to be related to the
small scale motions of the particles. This section
is intended to give some experimental support to

the above representation of the fluidized bed
dynamic through the multi-resolution analysis of
the liquid phase dynamic.
To this end, the original time series of the axial
velocity is decomposed into several time series of
fixed frequency-scales by discrete wavelet
transform (Kechroud and Brahimi, 2005). Figure
3 shows an example of the decomposition
obtained for the particle of 4mm and s=0.7. This
time frequency-scale representation reveals the
presence of structures with high amplitude
regularly separated in time. These structures are
seemingly the wave like coherent structures. As
criteria, the frequency-scale of the coherent
movement is determined when the corresponding
autocorrelation presents a cyclic behaviour with
the largest amplitude. The cyclic feature
corresponds to the repeating occurrence of the
coherent structure. Figure 4 presents a sample of
the autocorrelation observed for the coherent
component of 0.469 Hz frequency-scale in the
operating conditions of figure 3. For higher
frequency (3.75 Hz), in the decaying range, and
same operating conditions, the autocorrelation
decreases rapidly as showed by the sample in
figure 4, indicating the random nature of the
corresponding small scale movement. The whole
multi-resolution analysis is summarized in figure
5, were we have reported the frequency-scales of
the coherent components, according to the
retained criteria, as a function of porosity. For
comparison, we have also represented the
valuable data of Ham and al. (1990), El-Kaissy
and Homsy (1976) and Poletto and al. (1995)
obtained from void propagation analysis directly.
The results show clearly that the propagating
coherent movement in the liquid phase is
characterized by low frequencies. Almost all the
frequency-scales are below 1 Hz. Thus, this
phenomenon is buried in the energy containing
range of the spectra presented above. We observe
also from the beginning of fluidization, and for
the lowest porosities, a slight increase of the
frequency-scale. Beyond e=0.5, the frequencies
are rather distributed around 0.4 Hz and fall
within the segments representing the results of
Poletto and al. (1995). In our knowledge, only
Poletto and al. (1995) have considered moderate
and high porosities (e=0.59, 0.66, 0.73) in the
investigation of propagation of voidage waves.
Ham and al.(1990) and El-Kaissy and Homsy
(1976) have limited their analysis to low
porosities (E < 0.5) for which a clear peak appears

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

in the spectra and consider that there is no wave at
higher porosities for which bubbles appear. Our
results compare well with their data only in a
narrow range of porosities, just after the
fluidization of the particles. When approaching
e=0.5, they observe much higher values of the
frequency-scales. May be this difference is to be
related to the small dimensions of the particles
and the apparatus used by the authors. We cannot
draw a clear conclusion about the effect of particle
diameters on the frequency-scale of the coherent
structures because of the dispersion of the data.
The procedure followed in the present study to
determine the frequency-scale of the coherent
movement of the liquid phase has proved to be
pertinent. We have shown that the voidage wave
has an effect on the liquid phase dynamic and its
trace can be captured by multi-resolution analysis.
The absence of peaks in the spectra should not be
automatically associated to the non-existence of
coherent movements. So, the classical procedure
of Fourier analysis is not sufficient to detect and
evaluate the frequency-scales of coherent
structures. The random part of the dynamic is
confined in the high frequency range which
corresponds to rather small scale movements
induced by the particles. So, a dynamic model for
the two phases which does not take into account
these random small scale movements as those
based on a systematic averaged procedure, will
not reflect the real behaviour of the fluidized beds.

4. Conclusion

We have shown in the present study that the
local liquid velocity time series recorded at the top
of a liquid fluidized bed is representative of the
continuous phase behaviour inside the bed. The
procedure has allowed us to characterize
quantitatively spectral aspects of the dynamics.
The spectra of the liquid velocity fluctuations and
the multi-resolution analysis are complementary
and have revealed some new features of liquid
fluidized bed dynamics.
The continuous phase does not reach a fully
developed turbulence as is the case for single
phase high Reynolds number flows and thus does
not exhibit clearly the so called inertial
equilibrium sub-range of Kolmogorov. The high
frequency-scales are also found to have a rapid
decreasing auto-correlation function and then
qualified as random fluctuations, but their spectra
do not correspond strictly to the power law of an

Omstein-Uhlenbeck type process. So, fluidized
beds appear to follow a specific spectral dynamic
which nevertheless contains coherent structures
identified by cyclic large amplitude auto-
correlation function at low frequencies (below 1
Hz). This large scale coherent part has been linked
to cooperative movements of the particles while
the random part is related to the small scale of the
particle Brownian type motion. This spectral
characterization and subdivision of the liquid
phase dynamic constitute an experimental basis
for the development of advanced mathematical
modelling of liquid fluidized beds of coarse
particles such as stochastic processes based
We have clearly shown the analogy between the
void dynamic and liquid phase dynamic through
the comparison of the spectra and frequency-
scales of the coherent movements of both
dynamics. We have observed a good similarity
which is probably a consequence of an underlying
correlation between particle and continuous phase
fluctuations. It is of great interest to highlight the
details of the fluctuation dynamics of both phases
and their correlation. It is one of the scopes of our
investigations in connection with the present and
future quantitative experimental studies.


The authors are grateful for the financial
support of the Ministry of Higher Studies and
Scientific Research of Algeria.


Didwania, A.K., Homsy, G.M. Flow regimes and
flow transitions in liquid fluidized beds. Int. J.
Multphaseflow 7 563-580,(1981)

Poletto,M, Bai, R., Joseph,D. Propagation of
voidage waves in a two-dimensional liquid-
fluidized bed Int. J. Mult phase flow 21 223-

Zenit, R. Hunt, M.L. Solid fraction fluctuations in
solid liquid flows. Int. J. Mult flow 26 763-781,

Percival, B. D, Walden, A. W. Wavelet Methods
for Time Series Analysis. Cambridge university
press. (2000).

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Abarbanel, H.D.IAnalysis of Observed Chaotic
Data. Springer-Verlag, New York. .( 1996).

Kantz, H. Schreiber, T. Nonlinear time series
analysis. Cambridge university press. (1997).

Ellis,N., Briens,L.A,, Grace, J.R., Bi, H.T.,
Lim,C.J. Characterization of dynamic behavior in
gas-solid turbulent fluidized bed using chaos and
wavelet analyses, Chem. Eng. J. 96 105-116,

Srdjan Sasic, Bo Leckner, Filip Johnsson, Time-
frequency investigation of different modes of
bubble flow in a gas-solid fluidized bed,
Chemical Engineering Journal 121 27-35, (2006).

Kulkami, A.A. Joshi, J.B. Ravi Kumar, V.
Kulkami B.D.Application of multiresolution
analysis for simultaneous measurement of gas and
liquid velocities and fractional gas hold-up in
bubble column using LDA, Chem. Eng. Sci. 56
5037-5048, (2001).

Lu, X., Li, H. Wavelet analysis of pressure
fluctuation signals in a bubbling fluidized bed,
Chem. Eng. J. 75 113-119. (1999).

Guo, Q., Yue, Werther, G.J. Dynamics of pressure
fluctuation in a bubbling fluidized bed at high
temperature, Ind. Eng. Chem. Res. 41 3482-
3488. (2002).

Johnsson,F., Zijerveld,R.C., Schouten,J., van den
Bleek.C.M., Leckner.B., Characterization of
fluidization regimes by time-series analysis of
pressure fluctuations, International Journal of
Multi- phase Flow 26 4 663- 715.( 2000).

Handley, D., Doraisamy, A., Butcher, K.L.,
franklin, N.L. A study of the fluid and particle
mechanics in liquid-fluidized beds, Trans. Instn
Chem. Engrs. 44 260-273 .( 1966).

Monin A.S. and Yaglom A.M., Statistical fluid
mechanics, MIT Press, New York, (1975).

Bernard, J. M., Wang,C. P., Lee, R. H. C..
Measurement of fluid velocities in the interior of a
transparent fluidized bed with a Laser-Doppler
velocimeter. AIChE Symposium series. 205 37-
48. (1981).

Kechroud, N. These de Magister << Contribution i
l'6tude de la structure de l'6coulement a la sortie
d'un lit fluidis6 liquide-solide. Par v6locim6trie
laser >>. University de B6jaia, Alg6rie. (2000).

Nievergelt, Y. Wavelet Made Easy. Washington
University. (2001).

El-Kaissy, M. M., Homsy, G. M.,. Instability
waves and the origin of bubbles in fluidized
beds-I. Experiments. Int. J. Multiphase Flow 2,
379-395. (1976)

Condy, J.V.. Signal Processing, the modern
approach. McGrawhill. (1988)

Daubechies, I. Ten lectures on wavelets, Soc. for
Ind. and Applied Mathematics, Philadelphia, PA,
USA, (1992).

Mallat, S. A Wavelet Tour of Signal Processing,
Academic Press, London. (1999)

Al Dibouni, M.R., Garside, J., Particle mixing and
classification in liquid fluidized beds.
Transactions of the Institution of Chemical
Engineers 57, 94-103, (1979).

Buffiere, P., Moletta, R., Collision frequency and
collisional particle pressure in three-phase
fluidized beds. Chemical Engineering Science 55
5555-5563. (2000).

Renganathan, T. Krishnaiah, K. Voidage
characteristics and prediction of bed expansion in
liquid-solid inverse fluidized bed Chemical
Engineering Science 60 2545 2555.( 2005).

Batchelor, G.K., A new theory of the instability of
a uniform fluidized bed. J. Fluid Mech 193, 75-
110. (1988).

Jackson, R., The Dynamics of Fluidized Particles.
Cambridge university press. (2000).

Travis, V., Fred Ramirez, W.,. Modeling of Solid-
Liquid Fluidization in the Stokes Flow Regime
Using Two-Phase Flow Theory. AIChE Journal
45 708-722. (1999).

Buyevich, Y. A. A model for the distribution of
voidage around bubbles in a fluidized bed.

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Chemical Engineering Science 50 19 3155-3162.

Pope, S.B. Turbulent flows. Cambridge University
Press. (2000).

Clift, R., Grace, J.R. and Weber, M.E. Bubbles,
Drops and particles. Academic Press. (1978).

Qiang Lin, Boyer, D.L. and Fernando, H. J.S.
Flows generated by the periodic horizontal
oscillations of a sphere in a linearly stratified
fluid. J. Fluid Mech. 263 245-270.( 1994).

Tighzert, H. These de Magister. Reduction
dimensionnelle par decomposition orthogonale
propre d'un ph6nomene de diffusion-convection:
sillage d'une particule sph6rique. University de
B6jaia, Alg6rie. (2002).

Benabbas, F., Brahimi, M. and Tighzert, H.
Caract6risation du tres proche sillage d'une sphere
a des nombres de Reynolds mod6r6s et spectres
singuliers. Congres francais de m6canique, Nice,
France. (2003).

Veldhuis, C., Biesheuvel, A., Wijngaarden, L.V.
and Lohse, D. Motion and wake structure of
spherical particles. International Conference of
Multiphase flow, Japon. (2004).

Hadinoto,K. and Curtis J. S., Reynolds number
dependence of gas-phase turbulence in particle-
laden flows:
Effects of particle inertia and particle loading,
Powder Technology 195 ,119-127,(2009).

Kehcroud, N., Brahimi.,M., Legrand, J. and
Comiti, J. Hydrodynamic characteristics of the
liquid phase above a fluidized bed by laser
anemometry. 14th International Congress of
Chemical and Precess Prague. Czech Republic,

Li,H.; Identification of coherent structure in
turbulent shear flow with wavelet correlation
analysis; ASME Journal of fluid engineering. 120
4 778-785. (1998).

Ole E. Bamdorff-Nielsen and Jurgen Schmiegel.
A stochastic differential equation frame work for
the turbulent velocity field. Researcher Report.

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Thiele Center For applied Mathematics in Natural
Science, Denmark., (2005).

Lamorgese, A.G, Pope, S.B., Yeung, P.K. and
Sawford, B.L. A conditionally Cubic-Gaussien
stochastic lagrangian model for acceleration in
isotropic turbulence. J.Fluid Mech. 582 423-448.

Balachandar S., A scaling analysis for point-
particle approaches to turbulent multiphase flows,
Int. J. of Multiphase Flow 35 ,801-810,(2009)

Kechroud, N.,Brahimi, M. Analyse
multiresolution de la dynamique des lits fluidises
liquide-solide. 17eme Congres Francais de
Mecanique), Troyes, France. (2005).

Ham, J.M., Thomas, S., Guazelli, E., Homsy,
G.M., Anselmet, M.C., An experimental study of
the stability of liquid fluidized beds. Int. J.
Multiphase Flow 16 171-185. (1990).

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

1-Fluidized bed
2- Rotameters
3- Pump
4- Low water reservoir
5- Upper water reservoir
6- Laser source
7- Bragg cell
8- Photomultiplier
9- Frequency shift
10- Counter
11- Data acquisition system

Figure 1: Schematic of experimental set-up

Table 1: properties of particles used in experiments

dp Umf Ut
Material Pp/Pf U(cm/s) 8 Ar. 10
(mm) (cm/s) (cm/s)

2 2.554 2.45 21.07 0.88 12.25 0.4 0.81 1.22
4 2.564 4.38 30.43 1,59- 19,7 0,43 0,81 9.82

8 2.595 7.8 42.18 2.8-19.5 0.44-0.7 80.1



7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010




0.01 0.10 1.00


0.01 0.10 1.00 10.00

) 1E

1 E-2



s U/Umf Re
0.45 1 624
0.55 1.5 922
0.62 2 1232
0.7 2.5 1560
0.73 2.8 1747

0.10 1.00


Figure 2: Effect of superficial liquid velocity (or bed voidage s) on power spectral density functions: a):

dp=2mm, b): dp=4mm, c): dp=8mm





s U/U Re
0.42 1 49
0.48 1.5 74
0.55 2 98
0.6 2.5 123
0.64 3 147
0.81 5 245

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Uecomposition at level 9: u = aUd + db + dt + d b + d d + cB + d4 + d l + d2 + 01 .

d ,
a 0.13 i-i-i i i-- i

9 -o .oi . ..__ i ___I_ I ___ I ___ __ L
9 -0.0

d8 -0: 0
42 0.02 . ..
10 -0 02

6 -00

d 0 A.. 1Z L1.A A.-j ^I l-
d, .1:LA L-J o^tAj j

d .--

d2 -0.
d 0 1 11 Il 11
d^ .| ^|^^t^^^it|>^4il~lii~llt*llli||*l~l

bands (Hz)

[0.04 0.02]

[0.04 0.02]

[0.08 0.04]

[0.156 0.08]

[0.313 0.156]

[0.625 0.313]

[1.25 0.625]

[2.5 1.25]

[5 2.5]


Frequency scale











1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Figure 3: Discrete wavelet decomposition of liquid velocity fluctuations
for dp =4mm, U/Unf=3 (s = 0.7)

08 0.469 Hz

o 04


8 -0 2
-0 4
-0 6
-0 8

Time delay, s


Time delay, s

Figure 4: Multi-resolution autocorrelation coefficients of velocity components

dp=4mm, U/Umf=3 (s = 0.7)

3.75 Hz

C rJ

7th International Conference on Multiphase Flow
ICMF 2010, Tampa, FL USA, May 30-June 4, 2010

Our experimental results

Sdp 2 mm

Sdp= 4mm

Sdp 8 mm

+ f

06 0.8
bed void fraction, e

Experimental results of
El-kaissy and Homsy,(1976)

dp=0.59 mm

S dp 0.83 mm

A dp 1.1 mm

A dp 1.56 mm

Experimental results
of Ham and al.,(1990)

dp=0.325 mm

Experimental results
of Poletto and al., (1995)

+ dp =6.35 mm

Figure 5: Frequency-scales of the coherent structures versus s







* A



University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - Version 2.9.7 - mvs