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The summ of pressure
pattern ide studies ha
judgment o and back p
An ANN w characteris
predict the
2. Experim
Figure 1 sh compresso
individuall is released
of 24 mm
As shown transducer
transducer measuring
are as follo ms.
3. Results
3.1 Flow P Figure 2 sh
and c cor
net.orgmas
mary of experim e tap mm, SR
entification, di ave contributed
of researchers propagation ne
was designed, stics of the po
e flow regimes
ments Appara
hows a schema or and water
ly by flow me d into the atmo
inner diameter
n in the Figure r with 5D dista
r were sent thr g time of exper
ows: the range
and Discussi
Patterns hows the typic
rrespond to int ment condition
R is sampling r istance of pre
d to our unde in flow patter
eural network trained and te
ower spectrum s with good acc
atus and Proc
atic diagram o from pump
eters. The air-w osphere and wa
r and of 9 m to
Figure 1. Sc e 1, the press
ance of pressur rough amplifie
rimental run w e of superficia
ons
cal results of th terfacial behav
Modern
ns from the pre rate and N is s
essure tap, sam erstanding of f
rn identification was used to i
ested for the cl for one of the
curacy.
cedure
of the experime into an air-w
water mixture ater is measure
otal length is m
chematic diagr sure fluctuatio
re tap. The tran er into a comp
was 50 s. The w al air and wate
he flow pattern vior of the strat
n Applied Scienc
58
evious research size of data. It
mpling rate, si flow patterns
n. In this pape identify flow p
lassification of e normalized d
ental apparatus water mixing
flows through ed more accur
made of transpa
ram of the exp n was detecte
nsducer has ± puter via AD
working fluids er velocities; J
ns obtained fro tified, plug and
ce
her is shown i gave the infor
ize of data an of two-phase
er, the different pattern of two
f the flow regi differential pre
s used in the p section after
h the tube into rately by weigh
arent acrylic re
perimental app ed by Validyn
0.25 full sca converter. Sam
were air and w
G
=0.085-3.204
om the present d slug flows, r
in Table 1, wh rmation about t
nd flow pattern flows and los
tial pressure, t phase flow at
imes using as essure signals
present study. A their flow ra
an air-water s hing if necessa
esin to observe
aratus ne DP15-32 di
ale accuracy. O mpling rate w
water. The exp 4 ms, and tho
experiment. T respectively. In
Vol. 6, No. 9;
here X
DP
is dist the method of
n identified. T sing the subje
the analysis of t a horizontal
input some de and was show
Air supplied fr ates are meas
separator, wher ary. A smooth
e the flow patte
ifferential pre Output signals
was 400 Hz an periment condi
se J
L
= 0.016-1
The Figures, a n the stratified
2012
tance f flow
Those ective
f PSD
pipe. ensity
wn to
rom a sured
re air tube
ern.
ssure from
d the itions
1.255
, b flow,
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the gas ph interfacial
patterns. T of fluid. A
of the tube same velo
small diff magnitude
the upper w at the high
transported flow patter
with sudde
Finally, th et al. 197
in agreeme
net.orgmas
hase travels alo waves. As sh
The stratified fl As shown in th
e in a continuo ocity with the
ference betwee e of the waves
wall of the tub her velocity o
d by the liquid rn is highly un
en pressure pu
a Str
he obtained flo 4 as shown in
ent with those ong the upper h
hown in Figure flow pattern oc
he Figure 2b ous liquid flow
liquid, and th en the velocit
increases. Th be to form som
of the gas Fig d phase, in slug
ndesirable in p lses and severe
ratified flow
Figure 2. w pattern data
n Figure 3. Clo of Mandhane
Modern
half of the tub e 2a, this flo
ccurs at relative the plug flow
w. Next, it is n he gas-liquid in
ties of the ph his condition is
me liquid pack gure 2c. Un
g flow the liqu practical applic
e pressure osci
c . Typical resul
a is compared w ose observatio
et al. 1974.
n Applied Scienc
59
e while the liq ow has the sim
ely low gas an is characteriz
noticed that in nterface below
hases at the in s called slug f
kets, also liquid nlike plug flow
uid slugs are c cations. The fa
illations that c
Slug Flow lts of the obser
with the horizo on of this figur
ce
quid flows alon mplest configu
nd liquid mass ed by elongate
n the plug flow w the bubbles
nterface. With flow. Ultimatel
d slugs. These w, in which th
carried by the f aster moving li
an cause dama
b
rved flow patte ontal flow patt
re reveal that th ng the bottom
uration of all t flow rates and
ed bubbles flo w; elongated bu
is relatively s h the increase
ly, the waves liquid slugs a
he elongated b faster moving
iquid slugs are age to downstr
Plug Flow
erns tern map prop
he obtained flo
Vol. 6, No. 9;
with no signif the horizontal
d density differ wing along th
ubbles move a stable, indicati
e gas velocity build up and r
are then transp bubbles of ga
gas flow. The e usually assoc
ream equipmen
posed by Mand ow pattern dat
2012
ficant flow
rence e top
at the ing a
y, the reach
orted s are
e slug ciated
nt.
dhane ta are
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Figure 4 sh the system
unit and d hardware p
Here, the n
Where DP pressure d
points and 3.2 Typica
In this pap the differe
flow patter 3.2.1 Strat
Time serie difference
interface b flow has o
net.orgmas
hows the struc m of flow patte
data processin part of the syst
normalized pre
P is differenti different signal
d a Hanning W al Flow pattern
per, three typic ent of the flow
rns are shown tified Flow
es signal and with a small f
between water one peak and sp
cture of the int ern identificat
ng unit. Differ tem, while dat
Figure 4. S essure fluctuat
ial pressure l Pa. The po
Window of the s n and Power Sp
cal flow patter w patterns. Ex
in Figure 5 to PSD of strati
fluctuation. Th flow and air f
preads over wi
Modern
Figure 3. F telligent identi
tion is compos rential pressur
ta processing u
Structure of the tions in the tim
D DP
Pa,
DP
is a ower spectrum
same size. Spectral Densit
rns ware ident amples of tim
Figure 7. fied flow is s
his flow pattern flow is clear n
ide frequency
n Applied Scienc
60
Flow patterns ification system
sed of differen re measuring
unit constitutes
e intelligent id me series is defi
D DP
DP
average differ m was estimate
ty tified. The cha
me series press
shown at Figu n occurs at low
no bubble. Fro range from 8 t
ce
map m used in pres
ntial pressure unit and data
s the software p
dentification sy fined below:
2
DP DP
rential pressur
d using the se
aracteristics of sure different a
ure 5, in which w superficial v
om Figure 5b to 27 Hz.
sent study. As measuring un
a acquisition part of the syst
ystem
re Pa and D egments with
f their PSD we and power spe
h it shows a l velocities of wa
b, it is reveale
Vol. 6, No. 9;
shown in Figu nit, data acquis
unit constitute tem.
DP is norma a length of 16
ere used to ide ectra for the m
low mean pre ater and air an
ed that the strat
2012
ure 4, sition
e the
1 alized
6 834
entify major
ssure d the
tified
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Figure 5
3.2.2 Plug Figure 6 sh
difference due to the
divided int
Figure
3.2.3 Slug Figure 7 sh
when com peaks valu
range pres of peak va
net.orgmas
a Time se . Typical of th
Flow hows the time
of plug flow h e air bubble h
to two parts. T
a e 6. Typical of
Flow hows the time
mpared with the ue that it is ca
sent the bubble alue decreases
eries signal he time variatio
variation of p has a bigger fl
has a compres There are in the
Time series s f the time varia
e series signal e stratified flow
aused by the h es flow with ze
see Figure 7b
Modern
on of pressure J
G
=
pressure gradie luctuation than
sible effect th e range of 0-11
signal ation of pressur
J
G
=
and the PSD s w and plug flow
high water vel ero value. PSD
b.
n Applied Scienc
61
gradient and th =0.255 ms
ent and the PSD n that of stratif
hat cause large 1 Hz and 11-27
re gradient and =0.438 ms
slug flow. Slug w, as shown in
locity pushing D of slug flow h
ce
b Pow he PSD in stra
D of plug flow fied flow as sh
e fluctuations. 7 Hz see Figu
b Powe d the PSD in p
g flow time se n Figure 7a.
g the elongated has the frequen
wer spectral de atified flow at J
w. The time var hown in Figure
. The spread v ure 6b.
er spectral dens plug flow at J
L
=
ries signal has Slug flow time
d bubbles. Am ncy range in 2
Vol. 6, No. 9;
ensity J
L
=0.023 ms a
riation the pre e 6a. It is pos
values of PSD
sity =0.697 ms and
s a different pa e series signal
mong the top v -35 Hz with sp
2012
and
ssure ssible
D are
d
attern have
value pread
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Figure 7. 3.3 Flow P
3.3.1 Char Based on t
over 0-30 2005. Th
0-30 K2, K7 use a
a Strat
net.orgmas
a Typical of tim
Pattern Identif racteristic Para
the recorded p Hz is then di
he mean value , mean 0-3 Hz
as input of neur
tified flow at J Time series si
me series signal fication Using
ameters of PSD power spectra,
ivided into fiv e of power in
K3, mean 3 ral network tra
J
L
=0.023 ms a
c Slu Figu
Modern
ignal l pressure grad
Back Propaga D
this works fo ve bandwidths
each of the a -8 Hz K4, m
aining.
and J
G
=0.255 m
ug flow at J
L
= ure 8. Characte
n Applied Scienc
62
dient and PSD ation Neural N
ocus on a frequ : 0-3, 3-8, 8-1
forementioned mean 8-13 Hz
ms b Plu
=1.255 ms and eristic paramet
ce
b Power in slug flow at
Networks uency range up
13, 13-25, and d bands, denot
K5, mean 13-
ug flow at J
L
=
d J
G
=2.315 ms ters of a PSD
r spectral densi t J
L
=1.255 ms
p to 30 Hz. Th d 25-30 Hz as
ted as mean 0 -25 Hz K6, a
=0.697 ms and
s
Vol. 6, No. 9;
ity s and J
G
=2.315
he frequency r performed by
0-30 K1, vari and mean 25-3
d J
G
=0.438 m
2012
5 ms
range y Xie
iance 0 Hz
s
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Table 2 sh operating
flow were
Table 2. E
3.3.2 Ident Neural net
adding on train the n
also get ne during trai
generally i
Back prop layer and
componen three neura
pipe strat 0, 0, 1, th
net.orgmas
Figu hows part of
condition. Tho e used to train
Examples of the
tifying Flow P tworks with a
e or moremul network to get
etworking cap ining but not
it was begun b pagation neura
one hidden nts of the inpu
al cells, and co ified flow, plu
hat of plug flow ure 9. Back pro
characteristic ose parameter
the back propa e vector charac
Pattern Using A single layer ha
ltiple hidden l a balance the
pabilities to pro equal. The
by trying with o al network arch
layer. The in ut vectors. The
orresponds to ug flow and slu
w be 0, 1, 0,
Modern
opagation neural parameters w
rs and the outp agation ANN.
cteristic obtain
ANN ave a limitatio
layers between networks abil
ovide the corr use of more t
one hidden lay hitecture used
nput layer co e hidden layer
the three diffe ug flow. In th
and that of slu
n Applied Scienc
63
l network archit with seven par
put of the flow
ned from the pr
on in pattern re n input and ou
lity to recogni rect response
than one hidde yer.
d in the presen nsists of seve
r consists of th erent flow patte
his paper, let t ug flow be 1,
ce
tecture is used in rameter chara
w patterns str
resent study
ecognition. Th utput layers. B
ze patterns tha with the inpu
en layers have nt study is sho
en neural cel hree neural ce
erns of water- the expected o
0, 0. n this research
acteristics of P ratified flow, p
his drawback c Back propagati
at will be used ut pattern is sim
e advantages f wn in Figure
lls, correspond ells. The outpu
air two phase utput vector o
Vol. 6, No. 9;
PSD for differ plug flow and
an be overcom ion neural net
d during trainin milar patterns
for some cases 9. It has one
ding to the s ut layer consis
flows in horiz of stratified flo
2012
rence slug
me by work
ng. It used
s, but input
seven sts of
ontal w be
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Table 3. P
One hundr operating c
the identif
Table 4. R Number of
Number of Accuracy
4. Conclus
