netl 2014 multiphase conference
TRANSCRIPT
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Non-intrusive Uncertainty Quantification for
Reacting Multiphase Flows in Coal Gasifiers
Performance Measures x.x, x.x, and x.x
Aytekin Gel1! Mehr"a" #hahna$1
Arun %& #u'ra$aniyan( )or"an Musser 1
)ean-Fran*ois +ietiker 1,
1. National /nergy 0echnology a'oratory Morgantown 23 U&A&
!. A4/M5 Consulting C 4hoeni6 A7
(. G/ Glo'al Research Center
N8 ,. 2est 3irginia University Research Corporation 23
!91, N/0 Multiphase Flow #cience Conference
Morgantown 23
August :-; !91,
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Outline
Motivation and Objective Brief review of Gasification
Overview of Uncertainty
Quantification Frameworks Used
Preliminary Findings from Nonintrusive UQ !nalysis"
• #$emically %eacting case
Observations and #oncluding
%emarks
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Motivation an"
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Gasification
Gasification is t$e &rocess w$ere asolid fuel- suc$ as coal reacts wit$
steam- carbon dio+ide or $ydrogen
in a $ig$ &ressure- $ig$ tem&erature
reactor to &roduce a fuel gas- or
synt$esis gas '12- #O- #O2 *
(team is added to t$e fuel gas andsent t$roug$ a watergas s$iftreactor- w$ere #O and steam areconverted to 12 and #O2
!fter removal of #O2- $ydrogen ric$syngas can be utili3ed in a gasturbine or steam turbine for&roducing electricity or used togenerate c$emicals http://www.netl.doe.gov/File%20Library/Research/Coal/energy
%20systems/gasification/gasifipedia/
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Uncertaininputs
Quick Overview of
Uncertainty Quantification (UQ) Methods Employed
.ntrusive UQ
(everal !vailable Met$ods"
Polynomial #$aos )+&ansions
'P#)*
(toc$astic )+&ansion
Pro"
Quick &rediction
#on"
(urgery in t$e code and long
develo&ment time
Non.ntrusive UQ
(everal !vailable Met$ods"
(urrogate Model 4 Monte #arlo
Polynomial #$aos )+&ansions
Bayesian 5ec$ni/ues
Pro"
($ort develo&ment time
#on"
(am&ling error
Stochastic simulation(UQ embedded in the model)
UncertaintyinformationModel
Uncertaininputs
UQ Toolbox
Model
UQ achieved by samplingmany deterministic simulations
(ource" !n .ntroduction to Uncertainty Quantification Met$odologies and Met$ods- #, 5ong '2672* 8 #omå Uncertainty Quantification Met$ods Under
Practical .ndustry %e/uirements- 9ang '2672*
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0emonstration of a&&licability of UQ met$ods in
answering /uestions t$roug$ re&resentative &roblems"
#ase !" Nonreacting :0 5ransient Fluidi3ed Bed %iser
(imulation1 • #irculating Fluidi3ed Bed riser at N)5; wit$ e+&erimental data
from 2676 N)5;? Che$ically Reacting 0ransient Flui"i@e"
>e" Gasifier #i$ulation 'work in &rogress*•
)+&erimental data available for labscale setu&,• 20 8 :0 reacting multi&$ase flow simulation
• Bayesian #alibration for reaction rates wit$ available
e+&erimental data,
Non-5ntrusive UQ Metho"ology
0est 4ro'le$s
1 Gel et al& 3ali"ation an" Uncertainty Quantification of a Multiphase CF+ Mo"elB& 5n"ustrial /ngineering Che$istry Research !91(. :!((.
pp 11,!,-11,(: +
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Schematic diagram of the lab-scale fluidized-bed gasifier used for experiments
Coalinlet
Outlet
Air inlet
Uncertainty Quantification
#tu"y 4roperties?5nput para$eters with Uncertainty
$in-$a6range? '7* #oal Flow %ate 'g6 @ C66A
':* 12O < O2 ratio " >6,C @ 7,6A
Quantities of 5nterest"'7*#arbon #onversion 'D*'2*Gas Eield 'D*':*Gasification )fficiency 'D*
'*12
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
&hysical E'periments
Reference:(1) Shayan Karimipour, Regan Gerspacher, RajenderGupta, Raymond J. Spiteri, “Study of factors affectingsyngas quality and their interactions in fluidized bedgasification of lignite coal”, Fuel, Vol. 103, January 2013,Pages 308-320, ISSN 0016-2361,http://dx.doi.org/10.1016/j.fuel.2012.06.052.(http://www.sciencedirect.com/science/article/pii/S0016236112004723)
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#F0 simulations &erformed wit$
!N(E( F;U)N5 for same set of in&ut&arameters,
#oal &yrolysis- combustion- steam 8#O2 gasification along wit$ 12- #O
and #1 combustion are modeledusing 77 c$emical reactions,
5otal of :: trans&ort e/uations aresimultaneously solved for trans&ort of27 s&ecies and multi&le &$ases,
#om&utational cost &er simulation"
• 20 " 2: weeks on 7? cores
• :0 " H weeks on ? cores
Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
Computational "luid ynamics $imulations
3D CFD Model ofFluidized Bed Gasifier
Coal inlet
Air inlet
Outlet
Contour plot ofcoal volume fraction
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0ue to in$erently com&le+ nature oftransient reacting multi&$ase flows and t$e
e+&ensive com&utational cost- several
different strategies were investigated, 20
and :0 simulations at multi&le gridresolutions 'coarse- medium 8 fine* were
initiated,
0ifferent sam&ling strategies were
em&loyed"
• O&timal ;atin 1y&ercube (am&ling
'e,g, :6 sam&les for 20 runs*
• #entral #om&osite 0esign '2C sam&les*
Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
Computational "luid ynamics $imulations
Contour plot ofCO mole fraction
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
llustration of e'periment and C" samplin% in the parameter space
Scatter plot of the sampling locations in the parameter space for the physicalexperiments (14 samples based on Central Composite Design) and CFDsimulations (30 samples based on Optimal Latin Hypercube sampling)
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
Computational "luid ynamics $imulations* +eview of initial results
Individual comparison of initial Fluent simulation result with the correspondingreplicated experiment data (Runs 8-13) show good agreement for that sampleHowever, review of the full picture with scatter plot matrix tells a different story…
Discrepancy < 1 %
Comparison of Fluent simulation (Run # 1) with the corresponding experiments (Run # 8-13)
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
Computational "luid ynamics $imulations* +eview of initial results
ExperimentsInitial Fluent 2D Simulations (v.1)
Scatter Plot Comparison of Secondary Quantities of Interest
Opposite trendsobservedbetween
experiments andsimulations
triggered furtherinquiry andrevisions inseveral aspectsof the model
such as reactions
Individual CO mole fractioncompared in the previous slide
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
Computational "luid ynamics $imulations , +eview of results v-.
Experiments
New Fluent 2D Simulations (v.2)
Same trendsobservedbetween
experiments andnew 2Dsimulations
Scatter Plot Comparison of Secondary Quantities of Interest
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
$urro%ate model for /. mole fraction at the e'it monitor location
3D plot of the surrogate model for
H2 mole fraction
2D plot of H2 mole fraction surrogate modelat Coal Flow Rate = 0.05 g/s
Cross-validation errors to assess quality ofthe surrogate model
I PSUADE UQ toolbox from LLNL employed in
surrogate model construction.I Several surrogate models tested with theavailable simulation data (e.g., 1st, 2nd and 3rd order polynomial, MARS, etc.)
I Gaussian Process Model (GPM) provided thebest fitted surrogate model for H2 molefraction at the exit monitor location as shown.
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
terative &rocess to Construct the Best $urro%ate model for each Qo
3D plot of the surrogate model for
CO mole fraction
2D plot of CO mole fraction surrogate modelat Coal Flow Rate = 0.05 g/s
Cross-validation errors to assess quality ofthe surrogate model
I PSUADE UQ toolbox from LLNL employed in
surrogate model construction.I Several surrogate models tested with theavailable simulation data (e.g., 1st, 2nd and 3rd order polynomial, MARS, etc.)
I Gaussian Process Model (GPM) provided thebest fitted surrogate model for CO molefraction at the exit monitor location as shown.
Surrogatemodel isperforming
poorly for COmole fraction> 0.14
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Case B !ransient "luidi#ed Bed Gasifier $imulation (work in pro%ress)
nput uncertainty forward propa%ation for /. , Mi'ed Uncertainty
Forward propagation of input uncertaintiesI Deciding on the proper treatment of uncertainties with adequate characterization is quitechallenging.
I For demonstration purposes, some of the input parameters treated as epistemicuncertainty and the rest as aleatory.
I Coal flow rate treated as epistemic uncertainty between interval of [3.47e-2,6.56e-2]
Enlarged view of the region marked with circle:
0.795
0.761
76 % < Prob (H2 mole fraction ≤ 0.14) < 80 %
Prob (H2 mole fraction ≤ 0.14) ≈ 78 %
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• !nalysis of t$e simulation and e+&erimental results wit$ Bayesian
framework &erformed J Global sensitivity analysis for #O mole fraction
Case B !ransient "luidi#ed Bed Gasifier $imulation
Glo0al $ensitivity 1nalysis with Bayesian "ramework
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• !nalysis of t$e simulation and e+&erimental results wit$ Bayesian
framework &erformed J Global sensitivity analysis for 12 mole fraction
Case B !ransient "luidi#ed Bed Gasifier $imulation
Glo0al $ensitivity 1nalysis with Bayesian "ramework (continued)
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• !nalysis of t$e simulation and e+&erimental results wit$ Bayesian
framework &erformed J Global sensitivity analysis for gasification efficiency
Case B !ransient "luidi#ed Bed Gasifier $imulation
Glo0al $ensitivity 1nalysis with Bayesian "ramework
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Case B !ransient "luidi#ed Bed Gasifier $imulation
/. mole fraction surro%ate model with discrepancy ad2ustment
Predictions of ExperimentSample # 4
+
=
P r e d i c t i o
n o f
t h e e m u l a
t o r
c o n s t r u c t
e d f r o m
b o t h s i m u l a t i
o n &
e x p e r i m e
n t s
Gaussian processmodel based modeldiscrepancy
Model discrepancy corrected emulator prediction of # 4
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Case B !ransient "luidi#ed Bed Gasifier $imulation
CO mole fraction surro%ate model with discrepancy ad2ustment
Predictions of ExperimentSample # 14
+
=
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#o$e o'servations an" conclu"ing re$arks
Our goal continues to be e+&loring different nonintrusive UQ tec$ni/ues to identify t$ose t$at are best
suited for reacting multi&$ase flows,
;arge &art of t$e effort is s&ent on constructing
ade/uate surrogate models, Bayesian met$ods a&&ear to offer various favorable
features suc$ as /uantification of model discre&ancy
and inclusion of &rior information- w$ic$ can be used
effectively to alleviate lack of data,
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"uture 3ork
Bayesian calibration for t$e most
uncertain model &arameter"
KJ kinetic reaction rates
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