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  • Computational Engineering

    DrRynoLaubscher

    PG studies: Mechanical and Mechatronic Engineering Department

    Research area:

  • What is computational engineering? Relativelynewresearchdisciplinecomparedtomorefocussedresearchareassuchasturbomachinery,controlsystemsandbiomechanicalresearch

    Focusesonthedevelopmentandapplicationofcomputationalmodelsandsimulationsforscientific,socialandfinancialcomputing

    Scientific CFD,FEM,DEMetc. Social criminalbehaviourprediction,sentimentanalysis,etc. Financial volatilitypredictionsandmodelling

  • Relevant fields: Computationalfluiddynamics:Simulationandnumericalanalysisofcomplexfluidflowproblemsusingnextgenerationnumericaltechniquessuchas:1. Reducedordermodelling(reducingDOFs)forinsitusimand

    optimisation2. AIbasedturbulenceandchemistrypredictionalgorithms3. Investigationandsimulationoflargescale(industrial)fluid

    flow/heattransfer/reactingproblems4. Modellingusinghighfidelity

    models(LES,etc.)

  • Relevant fields: Finiteelementanalysis computationalstructuralmechanics:Numericalanalysisofstructuralcomponents/materialsandsystems:1. Linearandnonlinearresponsefiniteelementanalysis2. Studyparametricdesignconsiderationsofmechanicalcomponents

    throughsimulation(bicycleframes,aircraftcomponents,laminateconfigurations,etc.)

    3. Studyheattransferinporous/solidcomponentsandmaterialpropertyeffects

    4. Investigatingmechanicalresponsesofnewmaterialsthroughcomputation

  • Relevant fields: Discreteelementanalysis:Computingthemotionandinteractingbetweenlargenumberofdiscreteparticles:

    1. Agricultural/postharvesttechnologies2. Materialshandling

  • Relevant fields: BigdataanalyticsandAI(machinelearning)appliedtoengineering:AItechniquesarenowbeingusedbypractisingengineerstosolveawholerangeofhithertointractableproblems:1. Realtimeintelligentautomationandconditionmonitoring(2.)2. Architectures,algorithmsandtechniquesfordistributedAIsystems3. Deeplearningandrealworldapplications failureprediction,etc.4. Computerperception/interpretation crossbetweenCVandML5. Bigdataanalytics understandingcomplexsystems,IoT andCPS.6. AIappliedtosimulation:CFD,etc.=>ROM

  • Relevant fields: Optimisation(structural,fluidandsystemswide)andsimulationNumericaldesignoptimisation(structural,generalpurposeandmultidisciplinary)usinggeneralandmetaheuristicalgorithms.Developmentandinvestigationsintonewalgorithmsandparallelcomputing.System(social,financialandscientific)simulationusingstatisticalmethods:1. MonteCarlosimulationstudies(eg.percolationstatistics)2. Combinationwithdeeplearningpredictivemodelling3. GPUaccelerationofoptimisationalgorithms(concurrency)4. Parametricdesignoptimisation

  • Relevant fields: Financialandsocialmodelling(engineering):Similartostatisticalmodellingofscientificproblemsbutnowfocussedonfinancialmodelsofbusinesses/plants/etc.alongwithAIpredictive/classificationmodelling:1. Useofstatisticaltechniquesandmachinelearning2. Deepgroundinginpartialdifferentialequationtheory(Black

    Scholesequation)3. Useofoptimisationtechniques(portfoliooptimisation)4. Criminalbehaviourprediction(baggagescreening,etc.)

  • Relevant lecturers:

    Computationalfluiddynamics ProfHarms,ProfMeyer,DrHoffmann,DrLaubscher,Profvd Spuy,ProfVonBackstrom

    Finiteelementanalysis DrVenter,ProfVenter,ProfGroenwald MachinelearningandBigDataanalysis DrLaubscher,ProfVenterandDrVenter

    Optimisationandsimulation ProfGroenwald,ProfVenter,DrVenterandDrLaubscher

    Financialandsocialmodelling ProfHarms,ProfGroenwald,DrLaubscher

    Discreteelementanalysis:ProfCoetzeeandDrEls

  • Example: Merging of CFD and AI ProblemstatementAdvancedturbulencechemistryinteractionCFDmodellingrequiresthesolutionofamassivesetofdifferentialequationswhichisusuallyverystiffandcanhavelongsimulationtimes.Notviableforindustrialsimulations

  • Example: Merging of CFD and AI SolutionRatherthanusingmassivecomputerresourcestosolvethefinereactorsforeverycellinthecomputationaldomain,useaAIalgorithmthatpredictsthereactorperformancebasedonmemorybuiltfromasupervisedlearningalgorithm.Thusmodelwillalmostinstantaneouslypredictreactoryield.

    TurbulencefieldFinestructurereactors Deeplearningneuralnetwork

    Reactionrateprediction

  • Example: Merging of CFD and AI SelflearningUsestatisticstodevelopdistributionsofspecies.Usedistributionstocreaterandomspeciescompositionsanduseastrainingdataset.

    1Dreactorequations Deeplearningneuralnetwork

    Reactionrateprediction

    Use1Dreactorcodetodeveloptrainingdata

  • Example: Merging of CFD and AI ResultsMassivereductionincomputationalcostwithsmallerror:

  • END Thanksforlisteningandifthereareanyquestionspleasefeelfreetocomeandseemeandtheotherlecturers

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