Papers

268 Pages·2012·24.65 MB·English

Int'l Conf. Data Mining | DMIN'12 | 1
SESSION
REAL-WORLD DATA MINING APPLICATIONS,
CHALLENGES, AND PERSPECTIVES
Chair(s)
Dr. Mahmoud Abou-Nasr
Dr. Robert Stahlbock
Dr. Gary M. Weiss
2 Int'l Conf. Data Mining | DMIN'12 |
Int'l Conf. Data Mining | DMIN'12 | 3
Results of Mining Data Features During Computational Fluid
Dynamics Simulations
Michael R. Gosnell , Robert S. Woodley , and Steven E. Gorrell
† † ‡
21st Century Systems, Inc., 6825 Pine Street, Suite 141, Omaha, NE 68106, USA
†
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
‡
Abstract—Computational Fluid Dynamics (CFD) simula- Navier-Stokes (URANS) simulations of gas turbine engine
tionsprovideavarietyofdataminingchallengesandpresent transonic fan stages with 166 million grid points and entire
an opportunity for novel solutions. A core challenge is that fans with over 300 million grid points respectively. The
CFD computations can require weeks of computation on complementary challenge in CFD work is the amount of
expensive high performance clusters, delaying investigation resulting data and associated time for analyses.
of results until a fully converged solution is obtained. 21st The obvious data mining application is with processing
Century Systems, Inc. and Brigham Young University have the terabytes of raw data following computation of the
been collaborating on a concurrent agent-enabled feature numerical solution. Many disciplines incorporating CFD
extraction project designed to mine feature data while a research utilize software such as Evita, Intelligent Light’s
CFD simulation is executing. This paper summarizes the FieldView, and Kitware’s ParaView to assist with post-
work and presents the combined results from a unified processing and data mining of physical features within the
Industry/Applicationperspective.Empiricalresultsshowthe CFDsolutionspace.Thesetypesofprogramsaredesignedto
concept validity and capability of obtaining CFD feature post-process and visualize massive data sets and commonly
information much earlier than waiting for complete sim- include techniques such as feature extraction, construction
ulation convergence, which may ultimately save extensive of iso-surfaces, and automated visualization.
computational resources and provide much quicker turn- Amoreillusiveopportunityfordataminingwashypothe-
around in development requiring CFD modeling. sizedtoexistwithinthecomputationalperiodofdetermining
aCFD’snumericalsolution.Thenumericalsolutionachieved
Keywords: Computational Fluid Dynamics (CFD), Subjective in the penultimate iteration is virtually identical to the
Logic, Feature Extraction, Decision Support, Concurrent Analysis final solution, so detection of flow features would be just
as possible as detecting them within the final solution.
1. Introduction
Conceptually, detecting flow features in prior iterations of
ComputationalFluidDynamics(CFD)simulationsnumer- the solution would be attainable, but with increasing error.
icallysolvethegoverningequationsoffluidmotiontomodel The risk is that before traditional convergence, features
and simulate a variety of systems and machines including may not exist or conform to their accepted mathematical
oceancurrents,atmosphericturbulence,combustion,aircraft, definitions. However, the tradeoff is that if certain features
rotorcraft, and ship hydrodynamics. With increasing com- could be detected with appropriate levels of confidence, the
putational capabilities and the use of parallel codes, CFD CFD researcher might be able to obtain enough information
simulations have increased in grid resolution and numerical to forego the continuation of the solution, saving CPU-
accuracy to a point of correctly simulating highly complex hours. Figure 1 shows a conceptual view of the Concurrent
fluidflowproblems.Withoutappropriatedataminingefforts Agent-Enabled Feature Extraction (CAFE´) concept, trading
in place, these large, time-accurate, three-dimensional com- off additional expense of concurrent feature detection with
putational models risk concealing rather than revealing the potential benefit of not requiring the CFD simulation to
physics of interest. completely converge before items of interest are identified.
Undertaking CFD efforts requires a few core steps:
creation of a computer model, generation of a computa-
tional grid, computing a numerical solution, and data post-
processing. Initial model creation and entry, along with
construction of the computational grid, are pre-processing
steps which are not typically suited for any data mining
capabilities. A core challenge in CFD-related research is
the computational resources required to obtain solutions,
which can take days to months to reach full convergence.
Fig. 1: CAFE´ concept showing concurrent feature mining
List [1] and Yao [2] have run unsteady Reynolds-averaged
4 Int'l Conf. Data Mining | DMIN'12 |
The CAFE´ concept was originally proposed as a part- 2.1 Feature Extraction
nership between 21st Century Systems, Inc. (21CSi) and
Feature extraction algorithms form the core of the data
Brigham Young University (BYU) and subsequently funded
mining approach by examining the raw data. In order to
through Phase I and Phase II Small Business Technology
provide reasoning and decision support to the CFD users,
Transfer(STTR)contracts.Followinganinitialinvestigation CAFE´ uses an opinion space which captures characteristics
of feasibility, the concept was prototyped and expanded
of the system and allows for mathematical manipulation
to include a variety of CFD features and data. The major
aboutmultiplefeatureopinions.Thetoolemployedtoquan-
findings of this work are summarized in this paper with an
tify the believability of a feature is encapsulated within
eye toward applications to external work.
subjectivelogic,developedbyJøsang[3].Thisternarylogic
captures belief (b), disbelief (d), and uncertainty (u) as
2. CAFE´ Overview
an opinion, and intrinsically handles these in an algebraic
The ultimate vision of CAFE´ spans CFD pre-processing, space. These three elements are defined in [3] along with
concurrent feature extraction and analysis, through to so- relativeatomicityatoformanopinionorbelieftupleω (see
lution post-processing. The approach gains innovation as Eq. 1). Operators within subjective logic allow consensus
the solution was framed around an agent-based structure anddiscountingofopinionsinsuchawaythatcombinations
designed for decision support software applications. This of opinions relating to features can be aggregated.
structure allows all aspects of the CFD solution process to
ω =(b(x),d(x),u(x),a(x)) b+d+u=1 (1)
be included within the scope of CAFE´, with the following x |
high-level goals: To form an opinion, each component of the belief tu-
1) Provide concurrent feature extraction ple is given a numerical value, allowing the opinion to
2) Provide intelligent reasoning about extracted features have an exact representation. To maintain uniformity and
provide for mathematical constructs, the summation of an
a) Incorporate multiple extraction algorithms
opinion’s belief, disbelief, and uncertainty components is
b) Determine the believability of features
always equal to one with belief, disbelief, and uncertainty
3) Utilize detected features and results
only taking on values between zero and one. Subjective
a) Hone search space to reduce resource waste
logic is extremely attractive for incorporating the inherent
b) Incorporate machine learning to generalize solu-
uncertainty present during CFD execution as opinions are
tions and provide intelligent initial conditions
not forced to identify belief or disbelief. An agent can find,
Goal3focusesprimarilyonCFDpre-andpost-processing based on given information, how probable an outcome is
aspects and is beyond the realm of this discussion. Goal 1 rather than simply reducing the outcome to a binary TRUE
focuses on one of the two main elements of the prototyp- or FALSE. In addition to the initial opinion formulation
ing, concurrent feature extraction. Typical CFD simulations for feature detection, missing or incomplete data can also
largelyignoretheiterativesolutiondataoccurringbeforethe be incorporated within subjective logic by adjusting belief,
required convergence. Concurrent aspects of CAFE´ utilize disbelief, and uncertainty accordingly.
some of this intermediate data for analysis which is investi- Each individual feature extraction algorithm in CAFE´
gatedwhiletheCFDsimulationcontinuestowardasolution. is tuned to generate an opinion based on the algorithm’s
Unlike final post-processing and analysis, CAFE´ must be strengths, weaknesses, and the relationship of the potential
able to make decisions on the feature extractions without feature to the solution space. Specific details on each algo-
the assistance of user input. This aspect is addressed in rithm’sopinionscanbefoundinsubsequentdiscussionsand
Goal2whichutilizesresultsfrommultiplefeatureextraction related work.
algorithms, along with knowledge of the solution space, to
2.2 Feature Aggregation
provide intelligence about the detected features.
The two key CAFE´ architectural components for this As mentioned previously, the results of each feature
presentation are the feature extraction and feature aggrega- extractionalgorithmwillberelatedtothesolutionspaceofa
tion. Feature extraction takes the form of traditional CFD given situation. The focus of feature aggregation is to allow
feature detection with additional analyses of uncertainty the strengths of individual feature detection, encapsulated
due to the feature space. Feature aggregation incorporates throughopinions,toprovideintelligentfeedbacktotheCFD
multipleextractionsofthesameidentifiedfeaturetoprovide user. Two subjective logic operators are fundamental in this
a single analysis of the feature space. In this manner, aspect: the discounting operator and the consensus operator.
multiple algorithms can be incorporated with each being The discounting operator, defined by Jøsang [3], uses
favored per its given strengths. CAFE´ performs extraction the symbol written as ωAB = ωA ωB where the
⊗ x B ⊗ x
and aggregation using mathematically rigorous methods to superscripts represent the agent holding the opinion and
determine when a feature is true or simply an artifact of an the subscripts represent an agent, or piece of information,
unconverged simulation. on which the opinion is based. In the above equation, a
Int'l Conf. Data Mining | DMIN'12 | 5
discounted opinion of x is formed for A by A’s opinion 3. Vortex Core Extraction
of B and B’s opinion of x. Conceptually, the discounting
Vortices are common occurrences in many types of en-
of opinions allows individual, independent beliefs to be
gineering flows. They arise where there are large amounts
transferred along a chain of agents.
of vorticity, or flow rotation. A vortex contains two in-
Thecounterparttothediscountingoperatoristheconsen-
terdependent parts: the vortex core line and the swirling
sus operator. The consensus operator is used when multiple
fluid motion around the core. Many feature extraction al-
opinionsareheldaboutthesameagent,orpieceofinforma-
gorithms have been developed to locate vortex core lines.
tion,andasingleopinionisdesired.Theconsensusoperator,
Unfortunately, when extracting vortex core lines, there is
defined by Jøsang [4], uses the symbol written as ωAB =
⊕ x not one markedly superior algorithm that correctly extracts
ωA ωB following the same syntax of the discounting
x ⊕ x all features within the spatiotemporal flow domain. Rather,
operator. With supporting opinions, the consensus operator
there are multiple algorithms per feature that have been
has the effect of reducing uncertainty.
optimizedforspecificflowconditions.Roth[7]states,“none
CAFE´’s feature aggregation provides analysis through
of the [vortex extraction] methods is clearly superior in
trust networks, built from opinions. A graphical representa-
all the tested data sets.” This leaves a researcher with the
tionofatrustnetworkwithtwofeaturedetectionalgorithms
significant problem of having to run a data set through
isshowninFig.2.ThealgorithmagentAAcontainsfeature
multiple extraction algorithms and parse through the data
extractionalgorithmswithsubscripts1and2denotingsepa-
output to find relevant features—which is exactly where
ratealgorithms.ThemasteragentMAcombinesinformation CAFE´’s feature aggregation is paramount.
from multiple AAs to form its opinion on feature R.
The initial CAFE´ work implemented two vortex core
extraction algorithms. The first vortex extraction algorithm
selectedwastheSujudi-Haimes(SH)algorithm[8].TheSH
algorithmwasdesignedasarobustvortexcorelinedetection
algorithm for use in large 3D transient problems. It is
commonly used in CFD post-processing software packages
suchasEnSightandpV3.Thesecondvortexcoreextraction
algorithm is the Roth-Peikert (RP) algorithm [7], [9]. The
RPalgorithmisspecificallydesignedtoextractfluidvortices
in turbomachine simulations. What makes the RP algorithm
uniqueandwellsuitedforcomplexflowfieldsisthefactthat
Fig. 2: Graphical representation of a two algorithm trust itisdesignedtolocatecurvedratherthanstraightvortexcore
network. lines. Thus, each algorithm is strongly suited to a different
domain.
Each AA forms its own opinion on R denoted by ωAA1 Table 1 gives the strengths, weaknesses and feature char-
R
and ωAA2. The MA forms an opinion on each AA in use acteristics used for opinion generation using the SH vortex
R
given by ωMA and ωMA. Once the initial opinions are core extraction algorithm. The SH algorithm is specifically
AA1 AA2
formed, they can be combined into a final opinion, ωMA, designed to extract straight vortex cores which is why a
R
on the existence of a feature in R as straightcorefactorsintobelief.Strengthreferstotheamount
of flow rotation about the core and quality is a vortex
ωMA = ωMA ωAA1 ωMA ωAA2 . (2)
R AA1 ⊗ R ⊕ AA2 ⊗ R characteristic defined by Roth in [7] (in this research, the
(cid:16) (cid:17) (cid:16) (cid:17) angle between a vortex core line and its velocity vector).
2.3 Decision Support Feedback
With an overarching goal of providing decision support Table 1: SH vortex core opinion generation components
to the CFD user, CAFE´ utilizes multiple feature extraction
OpinionComponent ContributingFactors
algorithmsalongwithfeatureaggregationcapabilitiesutiliz-
b straightcore,highstrength,lowquality
ing subjective logic opinions and the trust network frame- d curvedcore,lowstrength,highquality
work. These key components allow for extracting features u distancefrompossibletrippoint
concurrent with CFD simulations and providing intelligent
analysis of the feature space prior to convergence. While Thestrengths,weaknessesandfeaturecharacteristicsused
early feature extraction may contain large variations in the for opinion generation using the RP vortex core extraction
solution space, multiple sets of the solution space, taken algorithm are given in Table 2. Setting a straight core as a
many iterations apart, can also be incorporated within the weaknesscharacteristicmightbemisleadingbecausetheRP
trustnetworkapproach.AdditionalbackgroundontheCAFE´ algorithm does not extraneously extract straight vortex core
architecture can be found in [5], [6] and a more thorough lines. A straight core is incorporated as a weakness because
presentation of subjective logic is available in [4]. whenitcomestostraightcorelinesthereismorebeliefthat
6 Int'l Conf. Data Mining | DMIN'12 |
the SH algorithm will extract them correctly than the RP
algorithm. Using the SH and the RP algorithms together in
thisfashionhelpsustomatcheachalgorithmsstrengthswith
the flow situations for which they were designed.
Table 2: RP vortex core opinion generation components
OpinionComponent ContributingFactors
b curvedcore,lowstrength,lowquality (a) 30%converged (b) 40%converged
d straightcore,nearzerostrength,highquality
u distancefrompossibletrippoint
The feature characteristic used for the RP algorithm un-
certaintyisthesameasthefeaturecharacteristicusedforthe
SH algorithm which is distance from a possible vortex trip
point.Whenusingmultiplefeatureextractionalgorithms,the
samefeaturecharacteristicsareusedforallalgorithmssince
feature characteristics are not algorithm dependent. (c) 50%converged (d) 60%converged
A blunt fin geometry [10] was selected as one illustra-
tive test case with clear, known vortex cores. Concurrent Fig. 3: Comparison of RP extracted vortex core lines from
feature extraction was replicated by exporting and saving theconvergeddataset(black)andconvergingdatasets(red)
the entire flow field data set every 45 iterations from start
to convergence at 900 iterations. Each of these saved data
algorithm.Thestartpointisdefinedasthefarthestupstream
sets was input into the vortex core extraction method where
pointandtheendpointisdefinedasthefarthestdownstream
vortex core lines were extracted using the RP and the SH
point.At60%converged,allbuttheendpointofthefinline
algorithms—resultingintwofeatureextractionsetspersaved
has a non-negligible feature displacement. This shows that
data set. Agents then produced final opinions on all vortex
at 60% converged the entirety of the horseshoe vortex core
features and a final aggregated feature set was produced.
line is very close to the same position it will be in at full
One crucial piece of information needs to be clear for
solution convergence.
proper interpretation of results. When agents form opinions
on extracted cores, they have information from the current
iterationofthesimulationandpreviousiterationsonly.They
do not use information from the fully converged simula-
tion, or any iterations beyond the current iteration, to form
opinions on extracted cores. Belief, disbelief, uncertainty,
and expected probability of vortex cores can be determined
without requiring a final converged solution giving informa-
tion about a final simulation’s expected vortex cores before
a simulation is 100% converged. However, Fig. 3 uses the
finalconvergedsolutiondatatoshowthedifferencebetween
concurrent extractions and the final solution.
Figure 3 compares concurrent vortex core extraction re-
sults obtained from the RP algorithm where the percent Fig.4:Percentfeaturedisplacementfortheendpointsofthe
convergence is based on the number of iterations. The two horseshoelineandthefinlineextractedbytheRPalgorithm.
blunt fin core lines are referred to as the “horseshoe” line,
wrapping around the blunt fin, and the much shorter “fin” Asmentioned,behaviorforSHcoreextractionwassimilar
line.At30%converged(Fig.3(a))thehorseshoelinebegins to RP results and resulting feature aggregation worked as
to take shape upstream. At 40% converged (Fig. 3(b)) the expected.Additionalinvestigationwithvortexcoreswasper-
horseshoelineandthefinlinearealmostcorrectlyresolved. formed including examination of more complex flow fields
At 50% converged (Fig. 3(c)) the end point of the fin line exhibited on a delta wing. This simulation was designed
moves downstream. Already at 60% converged (Fig. 3(d)) to match the experimental results of Kjelgaard [11] and
thehorseshoelineisspatiallycorrect(butthefinlineisnot). the numerical results of Ekaterinaris [12]. The delta wing
Figure4showsagraphofthefeaturedisplacementforthe data revealed some distinct differences and advantage of
endpoints of the horseshoe core line and the fin core line extracting cores with multiple algorithms. Additional results
extracted by the RP algorithm. The two vortex core lines and discussion of vortex core extraction are available in [6],
exhibit similar behavior when they are extracted by the SH [13].
Int'l Conf. Data Mining | DMIN'12 | 7
4. Separation/Attachment Extraction
Separation and attachment lines are lines on the surface
of physical bodies where the fluid flow abruptly moves
away from, or returns to, the surface of a body. Two
algorithms developed by Kenwright were included within
CAFE´ prototyping:thePhasePlane(PP)algorithm[14]and
the Parallel Vector (PV) algorithm [15]. The PP algorithm
works by first finding the eigenvectors of the Jacobian
matrix, then calculating and projecting critical points onto
thephaseplane.Dependingonwhetherthelocalphaseplane
flowfieldisasaddle,repellingnode,oranattractingnode,a
zero-crossingisdeterminedandthatpointismarkedaseither
aseparationoranattachmentline.ThePVmethodcompares
theeigenvectorsofthelocalvelocitygradienttensorwiththe
local velocity vector. Separation or attachment lines exist in
this method when the local streamline curvature is zero.
The PP algorithm is better suited for unstructured grids,
and it extracts disjointed line segments. The PV algorithm Fig. 5: Probability expectation of attachment lines from PV
works well with curvilinear grids and extracts continu- (left) and PP (right) algorithms at iterations 100, 1000, and
ous line segments. Both algorithms are designed to detect 4000
straightlinesandfailindetectingcurvedlines.Additionally,
both methods work well when the extracted points reside in
anareaofhighseparationorattachmentstrength,commonly expectation, along with experience, can be used within
referred to as the pressure difference across the separation CAFE´ to determine the likelihood of features concurrent
or attachment line. They also work best when the extracted with the simulation. Additional information on separation
points display a low velocity magnitude. and attachment line extraction is available in [17].
The selected algorithms appear to extract true separa-
tion and attachment lines accurately, but both suffer (to
5. Shock Wave Extraction
varying degrees) from extracting false lines. The original
authors mention that this problem occurs “when flow sep- Shock waves occur in fluid flow when the velocity of
aration/attachment is relatively weak and becomes diffused the fluid exceeds the speed of sound. Shock waves are
over several cells. This causes the phase plane algorithm characterized by sudden discontinuities in pressure, density,
to either detect multiple ghost lines or leave gaps.” One and velocity. Detection of a shock wave in CFD data is
cannottypicallytaketheseresultsaloneasbeingcompletely comparable to edge detection in image processing applica-
accurate because of all the false extractions. However, with tions. Two shock wave extraction algorithms have been im-
the incorporation of opinions to the feature extraction and plemented in CAFE´: the Lovely-Haimes algorithm [18] and
aggregation, CAFE´ is able to provide feedback as to which the Ma-Rosendale-Vermeer (MRV) algorithm [19]. These
lines are most likely to be correct. twoshockalgorithmshaveoutputsthatareslightlydifferent.
Onemathematicalfeatureofworkingwithsubjectivelogic The output of the Lovely-Haimes algorithm is a volume
opinions is the ability to convert opinions into probability that encompasses a shock, while the output of the MRV
expectations.Beingabletooperatewithintheopinionspace algorithm is a surface designed to locate the shock exactly.
and convert results to a probabilistic space provides the Both of these algorithms are enhanced in CAFE´ through
opportunity for CFD users to quickly visualize the results the use of multiple scalar values to compute derivatives, i.e.
with one common picture (as opposed to individually rea- using both density and pressure instead of one or the other.
soning about the opinion components of belief, disbelief, In addition to concurrent feature reasoning, CAFE´ shows
and uncertainty). Figure 5 shows the probability expectation additional strength in that the subjective-logic-based feature
applied to both algorithms (showing the attachment lines aggregation can be used on existing, processed data sets.
only in this case) applied to a simulation of the swept This is illustrated by looking at an example converged
ONERAM6wing[16].Bothalgorithms’probabilityexpec- CFD simulation of an ONERA M6 wing [16]. When the
tationincreasesthroughthesolutionconvergencewithsome MRV algorithm is applied, false shock waves are detected
of the more questionable areas (not true attachment lines) as shown in Fig. 6. However, applying CAFE´’s intelligent
showing up with very low probability expectation. This feature extraction and applying opinions to the extraction
method of analysis provides the researcher critical feedback basedonalgorithmicstrengthsandthesimulationconditions
on how the feature space is developing. The probability allows the calculation of probability expectation. As with
8 Int'l Conf. Data Mining | DMIN'12 |
the separation and attachment lines, probability expectation swirling particles and pathlines, which follow a particle in
is correctly able to identify these false extractions as seen time instead of streamlines, in order to extract vortices in
in Fig. 7. Once identified, thresholding could be applied to time-dependent simulations [22]. Each of these modifica-
declutter the visualization, providing dynamic feedback to tionshasbeenshowntocorrectlyextractfeaturesinunsteady
the researcher. This approach can leverage CAFE´’s capabil- data sets while the steady-state algorithms failed to reliably
ities onto previously executed simulations, providing better work in time-dependent simulations.
insight of detected features when traditional analyses might CAFE´ workincludedinvestigationofextendingthemeth-
be misleading. Additional information on detecting shock odstounsteadyflowsandimplementationofunsteadyvortex
waves is available in [17]. coreextractionusingthemethodpresentedin[20].Unsteady
vortex core extraction was implemented with RP and SH
algorithms previously discussed. An additional aspect of
featuretrackingwasrequiredforanalysisoffeaturesthrough
time. The feature tracking method implemented during pro-
totypingwastheattribute-basedmethodcreatedbyReinders
et. al. [23].
ThestrengthsandweaknessesofRoth-PeikertandSujudi-
Haimes algorithms were clearly displayed in the cylinder
dataset.Extractedcoresandprobabilityexpectationforboth
algorithms is shown for a single representative time slice in
Fig. 8, showing the cores originating at the cylinder and
moving downstream over time. Roth-Peikert performs well
Fig. 6: Shock waves detected on a converted solution using
when extracting weaker cores, and as the vortex strength
MRV
in most of the cores was quite low, it performed better,
especially nearer to the cylinder. Some of the cores further
downstream were also closer in agreement to the particles
traced through time. However, both RP and SH failed to
correctly extract cores as the cores broke up. An area
where both algorithms fail is when the acceleration along
a vortex core is not constant. As the cores were convected
downstream,theywereincreasinglystretched,whichcaused
anon-constantacceleration,sobothRPandSHwereshifted
away from the vortex cell centers. Both algorithms also
extracted cores that were less than half the height of the
cylinder, a phenomenon which was observed by Zhang
et. al. [24]. Additional information on extraction within
unsteady flows is available in [25].
Fig. 7: Probability expectation of shock waves using MRV
7. Conclusion
We have presented an overview of the CAFE´ concept
6. Unsteady Vortex Core Extraction
along with validation of feature extraction capabilities both
Previousdiscussionoffeatureextractionhasbeenentirely within steady state and unsteady CFD flows simulated in
steadyflows.Inotherwords,calculationofthefluidflowwas Fluent and OVERFLOW. Various aspects of CAFE´ were
performed at a given snapshot in time. However, unsteady illustratedthroughoutthediscussion.Withinthiswork,BYU
or transient flows—modeling the fluid flow over time— generatedmanydatasetsusedtoexploreCAFE´’scapabilities
provides a much more accurate analysis of complicated for detection of steady and unsteady vortex cores, shock
systems such as turbomachinery. waves, and separation and attachment lines. Data sets gen-
Researchers have made modifications to the steady-state eratedforvortexcoreextractionincludedthebluntfin,delta
extraction algorithms in order to account for transient flow wing, cylinder in cross flow, lid driven cavity, and NREL
situations. Fuchs et. al. suggested the addition of time Phase VI wind turbine. Data sets generated for shock wave
derivatives when extracting vortex core lines [20]. Lovely extraction were a supersonic ramp and swept ONERA M6
and Haimes derived a transient correction factor from the wing.Datasetsgeneratedforseparationandattachmentline
governing equations for their shock extraction algorithm extraction were cylinder in a cross flow and a delta wing.
whichcorrectlyextractsmovingshockwaves[21].Weinkauf We illustrated where CAFE´ could assist both with con-
et.al.approachedtheextractionofmovingvorticesbyusing verged solutions as well as the original intent of concurrent
Int'l Conf. Data Mining | DMIN'12 | 9
[4] A.Josang,“Theconsensusoperatorforcombiningbeliefs,”Artificial
IntelligenceJournal,vol.141,no.1-2,pp.157–170,October2002.
[5] C.Mortensen,R.Woodley,andS.Gorrell,“Concurrentagent-enabled
extractionofcomputationalfluiddynamics(CFD)featuresinsimula-
tion,”inProceedingsofThe2009InternationalConferenceonData
Mining,July2009,pp.90–96.
[6] C. Mortensen, “A computational fluid dynamics feature extraction
method using subjective logic,” Master’s Thesis, Brigham Young
University,2010.
[7] M.Roth,“Automaticextractionofvortexcorelinesandotherline-type
(a) RothPeikert featuresforscientificvisualization,”PhDDissertation,SwissFederal
InstituteofTechnology,2000.
[8] D.SujudiandR.Haimes,“Identificationofswirlingflowin3-Dvector
fields,”AIAA95-1715,June1995.
[9] M. Roth and R. Peikert, “A higher-order method for finding vortex
corelines,”inProceedingsofIEEEVisualization,October1998,pp.
143–150.
[10] C.HungandP.Buning,“Simulationofblunt-fin-inducedshock-wave
andturbulentboundary-layerinteraction,”JournalofFluidMechanics,
vol.154,pp.163–185,1985.
[11] S.KjelgaardandW.Sellers,“Detailedflow-fieldmeasurementsover
a75◦ sweptdeltawing,”NASATP2997,1990.
[12] J. Ekaterinaris and L. Schiff, “Vortical flows over delta wings and
numericalpredictionofvortexbreakdown,”AIAA90-0102,1990.
(b) SujudiHaimes [13] C. H. Mortensen, S. E. Gorrell, R. S. Woodley, and M. R. Gos-
nell, “Data mining vortex cores concurrent with computational fluid
Fig. 8: Probability expectation of unsteady vortex core dynamics simulations,” in Real World Data Mining Applications,
ser. Annals of Information Systems, M. Abou-Nasr, S. Lessmann,
extractions
R.Stahlbock,andG.M.Weiss,Eds. Springer,UnderReview.
[14] D.Kenwright,“Automaticdetectionofopenandclosedseparationand
attachmentlines,”inProceedingsoftheConferenceonVisualization,
feature detection. Using CAFE´ to monitor the developing ser. VIS ’98. Los Alamitos, CA, USA: IEEE Computer Society
feature space during a lengthy simulation can alert the Press,1998,pp.151–158.
[15] D. Kenwright, C. Henze, and C. Levit, “Feature extraction of sepa-
research to impending problem, saving time and resources.
rationandattachmentlines,” IEETVCG,vol.5,no.2,pp.135–144,
Applying multiple extraction algorithms with the capability 1999.
of individual or aggregated visualization aids in understand- [16] J. W. Slater, “ONERA M6 Wing,” http://www.grc.nasa.gov/WWW/
wind/valid/m6wing/m6wing.html,2008.
ing the detected feature space which can be applied to de-
[17] M.C.Lively,S.E.Gorrell,K.M.Hoopes,R.S.Woodley,andM.R.
velopingaswellasconvergedsolutionsets.Ourhopeisthat Gosnell, “Extraction of shock waves and separation and attachment
researchersincorporatingCFDfeaturedetectionmaybeable linesfromcomputationalfluiddynamicssimulationsusingsubjective
logic,”AIAAPaper2012-1263,January2012.
to utilize concurrent feature detection and intelligent feature
[18] D. Lovely and R. Haimes, “Shock detection from computational
extraction and aggregation to operate more effectively. fluiddynamicsresults,”in14thAIAAComputationalFluidDynamics
Conference,vol.1,2000,pp.296–304.
Acknowledgement [19] K.-L. Ma, J. van Rosendale, and W. Vermeer, “3D shock wave
visualization on unstructured grids,” in Proceedings of the 1996
ThismaterialisbaseduponworksupportedbytheUnited Symposium on Volume Visualization, San Francisco, 1996, pp. 87–
StatesAirForceunderContractNo.FA9550-10-C-0035and 94,104.
[20] R. Fuchs, R. Peikert, H. Hauser, F. Sadlo, and P. Muigg, “Parallel
issponsoredbytheAirForceResearchLaboratory(AFRL).
vectors criteria for unsteady flow vortices,” IEEE Transactions on
Anyopinions,findingsandconclusionsorrecommendations Visualization and Computer Graphics, vol. 14, no. 3, pp. 615–626,
expressed in this material are those of the author(s) and do May/June2008.
not necessarily reflect the views of the United States Air [21] D.LovelyandR.Haimes,“Shockdetectionfromcomputationalfluid
dynamicsresults,”AIAAPaper99-3285,June1999.
Force.
[22] T. Weinkauf, J. Sahner, H. Theisel, and H.-C. Hege, “Cores of
swirling particle motion in unsteady flows,” IEEE Transactions on
References VisualizationandComputerGraphics,vol.13,no.6,pp.1759–1766,
November/December2007.
[1] M.List,S.Gorrell,andM.Turner,“Investigationoflossgeneration [23] F. Reinders, F. H. Post, and H. J. Spoelder, “Visualization of time-
inanembeddedtransonicfanstageatseveralgapsusinghighfidelity, dependentdatawithfeaturetrackingandeventdetection,”TheVisual
time-accurate CFD,” ASME Journal of Turbomachinery, vol. 132, Computer,vol.17,pp.55–71,2001.
no.1,p.011014,January2010.
[24] H.-Q. Zhang, U. Fey, B. R. Noack, M. Ko¨nig, and H. Eckelmann,
[2] J.Yao,A.Wadia,andS.Gorrell,“High-fidelitynumericalanalysisof
“On the transition of the cylinder wake,” Physics of Fluids, vol. 7,
per-rev-typeinletdistortiontransferinmultistagefans–PartII:Entire
no.4,pp.779–794,April1995.
componentsimulationandinvestigation,”JournalofTurbomachinery,
[25] R.P.Shaw,S.E.Gorrell,R.S.Woodley,andM.R.Gosnell,“Vortex
vol.132,no.4,p.041015,2010.
core line extraction and tracking from unsteady computational fluid
[3] A.Jøsang,“Alogicforuncertainprobabilites,”InternationalJournal
dynamicssimulationsusingsubjectivelogic,”AIAAPaper2012-1261,
ofUncertainty,FuzzinessandKnowledge-BasedSystems,vol.9,no.3,
January2012.
pp.279–311,June2001.
10 Int'l Conf. Data Mining | DMIN'12 |

Prediction of Pull-out capacity of Suction Caissons Using Self-Evolving
Neural Networks
Abdussamad Ismail and Dong-Sheng Jeng
Abstract— A self-evolving neural network is developed observed capacities and FEM based predictions. The
using a combination of PSO and JPSO algorithms to downside of developing models using such a conventional
predict the pull-out capacity of suction caissons in clay. neural network design procedure is that there is tendency to
The algorithm is proposed with the aim of reducing the end up with a sub-optimal network with undesirably large
network complexity without compromising accuracy. A network size, which could undermine its ability to
database consisting of experiments performed on suction generalize.
caissons is used to construct and validate the network In this the present work, an approach to
model. The performance comparisons indicate that the simultaneous optimization of network topology and
proposed self-evolving neural network predicts more the parameters is proposed. The aim is to minimize the size of
capacity of suction caissons accurately than neural the network while still maintaining accuracy and
networks developed using conventional methods. generalization capability. The proposed algorithm is used to
develop an empirical model to predict the pull-out capacity
I. INTRODUCTION of suction anchors. The model depends on parameters such
SUCTION Suction caissons serve as cost effective as the caisson geometry (depth and width), un-drained shear
strength of soil around the caisson tip, the depth of the load
alternatives to conventional offshore foundations such as
application point, direction of the pull-out force and loading
driven piles. They are favorably used in deep ocean oil
rate.
and gas developments due to construction difficulty
associated with the installation of foundation in such
P
environment. By virtue of their larger diameter, suction D
caissons give a better capacity to withstand lateral loads than
θ
piles. The construction of caissons involves allowing the
L
caisson to sink into the sea bed under its own weight, and
then subsequently undergo an assisted penetration through
pumping out of water from inside the caisson. Suction
caissons usually function as anchors to hold the offshore
installations, subject to severe environmental conditions, in
place. Thus, there is a tendency for pullout movement to
occur due to tensile forces exerted by the chain attached to
Figure 1: Suction caisson
the caisson (see Figure 1). Accurate evaluation of the pull-
.
out capacity of suction caissons is therefore necessary for a
reliable geotechnical design of this kind of foundation.
Various attempts to improve the understanding of the II. NEURAL NETWORK MODELLING
behaviour of suction anchors through physical and numerical Design of neural networks is a complex multi-dimensional
modelling have been reported in the literature [1]-[2]-[2]-[4]- optimization problem, involving not only choosing the
[5]. However, due to the limited information about the optimum synaptic weights but also choosing a suitable
complex nature of failure mechanism involved the reliability processing function as well as an optimum network topology.
of conventional methods of analysis in accurately predicting The discrete, complex and multi-modal nature of the
the capacity of suction anchors is challenged. In an attempt topology space, it is extremely challenging to optimize
to improve the accuracy of pull out capacity estimation, network architecture and the network parameters at the same
Rahman et al. [6] developed an empirical model using BPN time [7]. The classical topology optimization techniques
networks. Based on their finding, neural network models include Network pruning [8][9], a top to bottom approach to
gave reasonably accurate results in comparison with network development, where the learning process begins
with a large network, then subsequently trimmed to a smaller

size by deleting redundant nodes and connections.
This work was supported by Nigerian Petroleum Technology
Incremental learning algorithm [10][11] is a more convenient
Development Fund (PTDF).
Abdussamad Ismail is a research student with the Division of Civil method in which the network size is increased by a gradual
Engineering, University of Dundee DD1 4HN, UK (phone: addition of nodes as training goes on. Near zero values are
[phone] e-mail: [email protected]).
initially assigned to the synaptic weights associated with the
Dong-Sheng Jeng is a Professor in the Division of Civil Engineering,
newly added node to minimize the loss of knowledge. The
University of Dundee DD1 4HN, UK