气液两相流流型

chemical engineering research and design 9 0 ( 2 0 1 2 ) 507–513 Contents lists available at ScienceDirect Chemical Engineering Resear r .co A me an level -ph predic E. Perey Kou a McDougall 741 b Thermal S ela c Departmen 04, U d Chevron E a A l in t p colle d ons o model/method can be used), and the confidence level in the predictions is quantified. Also, gaps in the data base are identified and future studies required in this are discussed. © 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Flow pattern; Two phase flow; Mechanistic modeling; Confidence level 1. Int The term “ the phases pipes. Whe two phases figurations the interfa teristics. Determi in two-pha namely, ph and mass and rate of the existin flow patter of two-pha tion of pre effective er ical additiv Also, segre ∗ Correspon E-mail a Received 0263-8762/$ doi:10.1016/ roduction flow pattern” refers to the spatial distribution of , which occur during gas–liquid two-phase flow in n gas and liquid flow simultaneously in a pipe, the can distribute themselves in a variety of flow con- . The flow configurations differ from each other in ce distribution, resulting in different flow charac- nation of flow patterns is a fundamental problem se flow analysis. Indeed all the design variables, ase velocity, pressure drop, liquid holdup, heat transfer coefficients, residence time distribution, chemical reaction, are all strongly dependent on g flow pattern. Thus, knowledge of the existing n can help the industry carry out a better design se flow systems. These include accurate predic- ssure drop and liquid inventory in pipe flow, and osion corrosion planning, utilizing properly chem- es, such as corrosion inhibitors and demulsifies. gated flow patterns are often desired for phase ding author. Tel.: +1 918 740 8543; fax: +1 918 631 2059. ddress: ep@utulsa.edu (E. Pereyra). 9 February 2010; Received in revised form 6 August 2011; Accepted 9 August 2011 separation efficiency improvement. Nowadays, a downward inclined inlet section may be installed upstream of the sep- arator, for promoting stratification and pre-separation of the phases. This can be designed utilizing flow pattern prediction to ensure stratified flow at the inlet section. Finally, the trans- port and deposition of solid particles, e.g., hydrates, paraffins and waxes, is an important flow assurance issue, which is strongly affected by the different flow patterns. In designing the above applications risky decisions can be made based on the predicted flow pattern, which can result in severe economical losses. Thus, it is very important to deter- mine the confidence level in the prediction of the existing flow pattern. However, no past studies have attempted to address the confidence level in such predictions. Fig. 1 presents the different transition boundaries occur- ring in gas–liquid flow, as well as the different existing flow patterns. The physical mechanisms and respective mod- els of the different transition boundaries can be found in Shoham (2006). Following is a summary of the commonly accepted flow patterns, for the entire range of inclination angles. – see front matter © 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. j.cherd.2011.08.009 j ourna l ho me page: www.elsev ie thodology and database to qu of methods for gas–liquid two tion raa,∗, C. Torresb, R. Mohanc, L. Gomeza, G. School of Petroleum Engineering, The University of Tulsa, Tulsa, OK cience Department, University of Los Andes, Merida, 5101 - Venezu t of Mechanical Engineering, The University of Tulsa, Tulsa, OK 741 nergy Technology Company, Houston, TX 77002, United States b s t r a c t novel methodology is presented to quantify the confidence leve atterns in pipes. An experimental flow pattern data base has been ata points). The experimental data are compared with the predicti ch and Design m/locate /cherd tify the confidence ase flow pattern bad, O. Shohama 04, United States nited States he prediction of gas–liquid two-phase flow cted, consisting of 12 studies (a total of 9029 f the unified Barnea (1987) model (any other 508 chemical engineering research and design 9 0 ( 2 0 1 2 ) 507–513 A 0.01 0.1 1 10 0.01 0.1 1 10 100 VSG, m/s VS L, m /s Annu lar (A ) Slug Flow (SL) Dispersed Bubble (DB ) A A J F G Stratifi ed Smoth (SS) Stratifi ed Wavy (SW) C B 0.01 0.1 1 10 0.01 0.1 1 10 10 0 VSG, m/s VS L, m /s Bubble (B) Annula r (A)Slug Flo w (SL) Dispe rsed Bubble (DB) E J J F G Fig. 1 – Transition boundaries and existing flow patterns in gas–liquid two-phase flow. 1.1. Ho The existin sified as st stratified-w and elonga flow (DB). 1.2. Ver In this rang disappears churn flow around the considered flow (CH), a 1.3. Do For downw stratified-w inclination As observe tical flow, d all inclinat uid and hig the annula f fal ow i ually pipe ding . Ex erim mos by Ta was air w over o +9 flow g the (198 onta 1987 orizo m, w out ta fo Table 1 – Authors Shoham Lin Kouba Kokal Wilkens Meng Manabe Van Dresar Mata et al. Abduvayt e Gokcal Omebere-I rizontal and near-horizontal flow g flow patterns in these configurations are clas- ratified flow, including stratified-smooth (ST) and avy (SW), intermittent flow (I), which includes slug ted-bubble, annular flow (A) and dispersed-bubble tical and sharply inclined flow e of inclination angles, the stratified flow regime and a different flow pattern is observed, namely, . Usually, the flow patterns are more symmetric pipe axis, and are less dominated by gravity. The flow patterns are bubble flow (B), slug flow (I), churn nnular flow (A), and dispersed-bubble flow (DB). wnward inclined and vertical flow ard inclined flow, the dominant flow pattern is avy flow, occurring over a wide range of downward angles, namely, from horizontal flow up to −80◦. d in horizontal, upward inclined, and upward ver- ispersed-bubble flow and annular flow occur for ion angles (including downward flow) at high liq- form o ward fl that us off the depen phases 2. An exp of the given which lizing study c −90◦ t zontal varyin Kouba a horiz Kokal ( near h 76.3 m carried his da h gas flow rates, respectively. For downward flow, r regime exists also at low gas flow rates, in the present stu sure on flow Summary of studies in the data base. Year Variables range 1982 Air water, ID = 2 and 1 in., from � = −90◦ to +90 1982 Air water, ID = 25.4 and 95.4 mm, � = 0◦ 1986 Air-kerosene, � = 0◦, ID = 3 in.; �L = 814 kg/m3; � 1987 � = 0◦, ±1◦, ±5◦ and ±9◦; ID = 25.8; 51.2 and 76.3 �L = 858 kg/m3; �L = 7 cP 1997 Salty water and oil; P = 40 psi; � = 0◦, 1◦ and 90◦ 2001 Annular stratified flow transition for � = 0◦, ±1 2001 P = 209.3 psi and 464.8 psi; � = 0◦, 1◦ and 90◦ and Siegwarth 2001 Nitrogen and hydrogen, � = 1.5◦; ID = 8.43 mm 2002 ID = 2 in., � = 0◦, �L = 480 cP t al. 2003 P = 592 and 2060 kPa; � = 0◦, 1◦ and 3◦; ID = 54.9 nitrogen and water 2005 � = 0◦; ID = 50.8; �L = 889 kg/m3; �L = 181–587 cP yari et al. 2007 P = 2000 kPa, 9000 kPa; � = 90◦; ID = 189 mm; nit ling-film. The slug flow pattern in vertical down- s similar to that occurring in upward flow, except the Taylor bubble is unstable, located eccentrically axis. The Taylor bubble may either rise or descend, on the relative flow rates of the gas and liquid perimental data base ental data base has been collected, which consists t relevant studies on flow pattern prediction, as ble 1. The earliest set of data is Shoham (1982), acquired in 50.8 and 25.4 mm pipe diameters, uti- ater at atmospheric conditions. This was the first ing systematically all the inclinations angles, from 0◦. At the same time, Lin (1982) carried out hori- experiments in 25.4 and 95.4 mm diameter pipes, superficial gas velocity from 0.8 to 200 m/s. Later, 6) carried out an experimental study on slug flow in l 3 in. diameter pipe, using air-kerosene. Following, ) studied two-phase flow patterns in horizontal and ntal flow, utilizing pipe diameters of 25.8, 51.2 and ith air and light oil as working fluid. Wilkens (1997) studies on gas–liquid flow at 0◦, 1◦ and 90◦. Only r salty water and air have been considered in the dy. Later, Manabe (2001) studied the effect of pres- patterns for 0◦, 1◦ and 90◦, using oil and natural School Points ◦ TelAviv Univ. 5676 Univ. Illinois 141 L = 1.9 cP Univ. Tulsa 53 mm; Univ. Alberta, Canada 1668 Univ. Ohio 204 ◦, ±2◦ Univ. Tulsa 153 Univ. Tulsa 247 NASA 116 Intevep, Venezuela 80 and 106.4 mm; Waseda Univ., Japan 443 Univ. Tulsa 183 rogen and water SINTEF 98 chemical engineering research and design 9 0 ( 2 0 1 2 ) 507–513 509 Fig. 2 – Dat inclination gas. Van Dr gen and ni upward inc Mata et al. high viscos Recently, G 50.8 mm di uid viscosit (2007) prese flow patter The dat points. Fig. variables o the superfi resent the a base variables histograms: (A) superficial velocities, (B) densiti angle. esar and Siegwarth (2001) used a mixture of hydro- trogen to study gas–liquid flow patterns in a 1.5◦ lined pipe of 8.43 mm diameter. In a later study, (2002) presented a flow pattern map for oil with ity (480 mPa s) and air, in a 50.8 mm horizontal pipe. okcal (2005) carried out an experimental study in a ameter horizontal pipe, utilizing two different liq- ies (181 and 587 mPa s). Finally, Omebere-Iyari et al. nted an analysis on the effect of high pressure on ns at 90◦ using larger diameter pipe of 189 mm. a base consists of a total 9029 experimental data 2 presents the distributions of the most important f the data base. Fig. 2(A) shows the histogram of cial gas and liquid velocities, where the bars rep- percentage of the total number of experimental points. As are concen gas velocit 0.1 and 10 m carried out gas and liq the numbe intervals. F out mainly the liquid- water (6464 using oils a variation o of the data sponding to es, (C) liquid viscosity, (D) pipe diameter and (E) can be seen, the superficial liquid velocity points trated between 0.01 and 10 m/s. For the superficial y, the largest part of the experiments is between /s. Also, a significant number of tests have been in the interval of 10–100 m/s. Fig. 2(B) contains the uid density histograms, where the bars represent r of points with densities occurring between the or the gas density, experiments have been carried with air at atmospheric conditions (0–5 kg/m3). For phase, the most commonly used working fluid is points); the rest of the tests have been acquired nd kerosene with densities of 750–900 kg/m3. The f the liquid viscosity is presented in Fig. 2(C). Most points fall in the first and second intervals, corre- water and kerosene. There is a lack of information 510 chemical engineering research and design 9 0 ( 2 0 1 2 ) 507–513 Table 2 – Comparison of model prediction and entire data base. (A) Successful (B) Unsuccessful Total No. [%] DB SS SW A I B DB 523 409 78.20 409a 0 3 0 111 0 SS 420 232 55.24 0 232a 70 14 104 0 SW 1522 832 54.66 54 319 832a 99 218 0 A 1907 1315 68.96 4 15 150 1315a 423 0 I 4537 3860 85.08 332 16 38 236 3860a 55 B 120 98 81.67 17 0 0 0 5 98a Total 9029 6746 74.71 816 582 1093 1664 4721 153 a Diagonal elements are successful predictions. for the inte mention th transition s also includ liquid visco tal flow. Fig The majori 50.8 mm pi pipe diame ation of th tests were 3. Co A compute on the uni tern maps (−90◦ to +9 software p phase flow FLOPATN d different fl liquid supe culated tran flow-patter coordinate compiled a in an Excel following p The inp consists of cosities and inclination VBA subrou link library perature ar a gi ing s pe ge ser rpos turn noth atter and p Co ectio tter n pre ation tio b xper sults ted re gi f 902 redic itten h 81. ed-w le 2(B mns lum flow redic f the itten betw r ana ing Table 3 – DB ST A I B Total a Diagonal rval 7–30 mPa s and 40–170 mPa s. It is important to at there have been no experimental flow pattern tudies for high viscous liquids (� > 7 mPa s), which e the effect of inclination angle. All the studies for sities greater than 7 mPa s correspond to horizon- . 2(D) presents the histogram for the pipe diameter. ty of the studies have been carried out in 25.4 and pes, with only few experiments conducted in large ters (>150 mm). Finally, Fig. 2(E) presents the vari- e inclination angle, where the largest parts of the conducted between 0◦ and −5◦. mputer library r program, FLOPATN, has been developed based fied Barnea (1987) model, for generating flow pat- applicable for the entire range of inclination angle 0◦), as well as for flow pattern predictions. This rovides important information needed for two- design. For a given set of inlet flow conditions, etermines the transition boundaries between the ow-pattern regions, as a function of the gas and rficial velocities, vSG and vSL, respectively. The cal- sition boundaries are then plotted in the form of a n map using vSG and vSL as coordinates in a log–log system. The library is written in Fortran 90 and is s a dynamic link library (DDL). The user interface Visual Basic (VBA) worksheet “FLOPATN.xls”. The resents a description of the main program features. ut data interface is implemented in Excel, which : (1) gas and liquid fluid properties: densities, vis- surface tension; (2) pipe geometry: pipe diameter, and roughness. Once the input data are entered, a tine validates the input and executes the dynamic (DLL). Note that the operating pressure and tem- e implicit in the fluid properties. For operat and pi Excel u this pu and re tern. A flow p erties 4. This s flow pa patter observ the ra total e son re presen tions a total o fully p interm (B) wit stratifi Tab in colu first co bubble were p most o interm region Simila remain Comparison result for (ST = SW + SS). (A) Successful Total No. [%] DB 523 409 78.20 409a 1942 1453 74.82 54 1 1907 1315 68.96 4 4537 3860 85.08 332 120 98 81.67 17 9029 7135 79.02 816 1 elements are successful predictions. ven set of pipeline flow conditions, including the uperficial gas and liquid velocities, fluid properties, ometry, it is possible to predict the flow pattern. An defined subroutine (FPPredIn) has been created for e, which receives the input data described above, a code, which represents the predicted flow pat- er subroutine (FP) is capable to generates and plots n maps based on the given input data (fluid prop- ipe geometry). mparison study n describes a comparison between the predicted n and the experimental data base. A particular flow diction is successful if it agrees with experimental . Thus, the model performance is quantified by etween number of successful predictions and the imental data points. Table 2 presents the compari- for the entire data base. Successful predictions are in part (A) of the table, while unsuccessful predic- ven in part (B). As can be seen in Table 2(A), from the 9 experimental points, 6746 (74.7%) were success- ted. The flow pattern with the best success rate is t flow (I), at 85.1%, which is followed by bubble flow 6% success. The lowest success rate corresponds to avy flow (SW), at 54.7%. ) provides the unsuccessful predictions, presented for each of the flow pattern. For example, the n presents unsuccessful predictions for dispersed- (DB). As can be seen, 54 “DB” experimental points ted as “SW”, 4 as “A”, 332 as “I” and 17 as “B”. Thus, unsuccessful predictions of DB flow correspond to t flow “I”. These data points occur in the transition een “DB” and “I”, which is hard to identify visually. lysis is presented for the other flow pattern in the columns of Table 2(B). (B) Unsuccessful ST A I B 3 0 111 0 453a 113 322 0 165 1315a 423 0 54 236 3860a 55 0 0 5 98a 675 1664 4721 153 chemical engineering research and design 9 0 ( 2 0 1 2 ) 507–513 511 Table 4 – = SW + SS + A) and (dispersed = DB + B). (B) Unsuccessful ispersed Segregated Intermittent Dispersed 524a 3 116 Segregated 58 3046a 745 Intermitten 387 290 3860a Total 969 3339 4721 a Diagonal Table 2 and “SW” d For this cas most of th between an be attribut experimen Similarl the segrega patterns (D to 82.3%. Th forward di flow patter 5. Ide Tables 2–4 where the ried out in data base fluid prope analysis of map. When the transiti and presen different flo is presente 5.1. Dim For horizon namely, th and Martin pressure gr between st annular flo F = √ �G (�L − The second X2 = −(dp/−(dp/ Fig. 3 show the observe for horizon non-stratifi originally b transition “B”) corresp ig. 3, the empty squares represent the all the points stratified flow (“ST”) was predicted and a non-stratified ttern was observed. The filled squares represent all the where a non stratified flow pattern was predicted and as been observed. As can be seen in the figure, all the s cor arou dict emp tion visco �L > inter ed “I on p may roug w pa ht of is a f s liqu ect o Str rizon dicte 1 − h˜L) h˜L i ed fl sionl s are Results for Shoham (1982) flow pattern definition (segregate (A) Successful Total No. [%] D 643 524 81.49 3849 3046 79.14 t 4537 3860 85.08 9029 7430 82.29 elements are successful predictions. is transformed into Table 3 by combining the “SS” ata points into stratified flow “ST” (ST = SS + SW). e, the total success rate increases to 79.0%, whereby e failed predictions are located in the region nular and intermittent flow. This discrepancy can ed to the different criteria used by the different talists to classify the flow patterns. y, Table 3 is transformed into Table 4 by combining ted flow patterns (ST + A) and the dispersed flow B + B). The total success rate for this case increases is improvement is owing to the clear and straight- stinction between the two considered combined ns. ntification of discrepancy regions do not provide information about the conditions discrepancies occur. Thus, further analysis is car- this study to identify these regions. The acquired presents a wide range of operational conditions, rties and pipe geometry, which complicate the the data utilizing a simple vSG vs. vSL flow pattern the analysis is carried out in dimensionless form, on lines and discrepancy regions can be identified ted in a generalized form, which can be extended to w conditions with more confidence. This analysis d next. ensionless horizontal flow pattern map tal flow, two dimensionless groups (given below), e modified Froude number (F) and the Lockhart elli parameter (X) (which represents the superficial adient ratio) determine the transition boundaries ratified to non-stratified flow, and annular to non- w (originally proposed by Taitel and Dukler, 1976). �G) vSG√ d g cos(�) (1) In F where flow pa points “ST” h failure spread the pre The predic liquid cosity, while observ sificati points flow th this flo the rig of “A” viscou the eff 5.2. For ho be pre F2 [ (1 where stratifi dimen less ga 1 10 parameter is the Lockhart and Martinelli given by: dL)SL dL)SG , Y = (�L − �G) g sin(�)−(dp/dL)SG (2) s the discrepancy between the predicted (Pred) and d (Obv) flow pattern on a dimensionless F vs. X map tal flow. The solid line represents the stratified to ed transition boundary (transition “A”) proposed y Taitel and Dukler (1976). The dashed line is the between annular to non-annular flow (transition onding to Barnea (1987). 0.001 0.01 0.1 0.01 F "ST "A" "I" Tra Fig. 3 – Pre pattern ma responding to the stratified to non-stratified are nd transition “A”, which attests to the accuracy of ion of this transition. ty triangles correspond to observations of “I” and of “A”. On the other hand, the filled triangles (low sity, �L < 7 cP) and the filled rhombs (high liquid vis- 150 cP) correspond to observations of annular flow mittent flow was predicted. It is believed that the ” to the left of transition “B” is a flow pattern clas- roblem. As explained by Shoh