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Dynamic Data Driven Application Systems (DDDAS) A new paradigm for applications/simulations and measurement methodology … and how it would impact CyberInfrastructure!. Dr. Frederica Darema Senior Science and Technology Advisor Director, Next Generation Software Program NSF.
DynamicData Driven Application Systems (DDDAS) A new paradigm for applications/simulations and measurement methodology … and how it would impact CyberInfrastructure! Dr. Frederica Darema Senior Science and Technology Advisor Director, Next Generation Software Program NSF
What is DDDAS OLD (serialized and static) NEW PARADIGM (Dynamic Data-Driven Simulation Systems) (Symbiotic Measurement&Simulation Systems) Simulations (Math.Modeling Phenomenology Observation Modeling Design) Theory (First Principles) Simulations (Math.Modeling Phenomenology) Theory (First Principles) Experiment Measurements Field-Data (on-line/archival) User Measurements Experiment Field-Data User Dynamic Feedback & Control Loop Challenges: Application Simulations Development Algorithms Computing Systems Support
Examplesof Applications benefiting from the new paradigm • Engineering (Design and Control) • aircraft design, oil exploration, semiconductor mfg, structural eng • computing systems hardware and software design (performance engineering) • Crisis Management and Environmental Systems • transportation systems (planning, accident response) • weather, hurricanes/tornadoes, floods, fire propagation • Medical • customized surgery, radiation treatment, etc • BioMechanics /BioEngineering • Manufacturing/Business/Finance • Supply Chain (Production Planning and Control) • Financial Trading (Stock Mkt, Portfolio Analysis) DDDAS has the potential to revolutionize science, engineering, & management systems
NSF March 2000 Workshop on DDDAS(Co-Chairs: Craig Douglas, UKy; Abhi Desmukh, UMass)Invited Presentations • New Directions on Model-Based Data Assimilation (Chemical Appl’s) Greg McRae, Professor, MIT • Coupled atmosphere-wildfire modeling Janice Coen, Scientist, NCAR • Data/Analysis Challenges in the Electronic Commerce Environment Howard Frank, Dean, Business School, UMD • Steered computing - A powerful new tool for molecular biology Klaus Schulten, Professor, UIUC, Beckman Institute • Interactive Control of Large-Scale Simulations Dick Ewing, Professor, Texas A&M University • Interactive Simulation and Visualization in Medicine: Applications to Cardiology, Neuroscience and Medical Imaging Chris Johnson, Professor, University of Utah • Injecting Simulations into Real Life Anita Jones, Professor, UVA Workshop Report: www.cise.nsf.gov/dddas
PETROLEUM APPLICATIONS SALT DOME GAS OIL WATER FAULT
Fire Model • Sensible and latent heat fluxes from ground and canopy fire -> heat fluxes in the atmospheric model. • Fire’s heat fluxes are absorbed by air over a specified extinction depth. • 56% fuel mass -> H20 vapor • 3% of sensible heat used to dry ground fuel. • Ground heat flux used to dry and ignite the canopy. Kirk Complex Fire. U.S.F.S. photo Slide Courtesy of Coen/NCAR
Coupled atmospheric and wildfire models Slide Courtesy of Coen/NCAR
Gas Phase Reactions SiCl3H HCl + SiCl2 SiCl2H2 SiCl2 + H2 SiCl2H2 HSiCl + HCl H2ClSiSiCl3 SiCl4 + SiH2 H2ClSiSiCl3 SiCl3H + HSiCl H2ClSiSiCl3 SiCl2H2 + SiCl2 Si2Cl5H SiCl4 + HSiCl Si2Cl5H SiCl3H + SiCl2 Si2Cl6 SiCl4 + SiCl2 Surface Reactions SiCl3H + 4s Si(B) + sH + 3sCl SiCl2H2 + 4s Si(B) + 2sH + 2sCl SiCl4 + 4s Si(B) + 4sCl HSiCl + 2s Si(B) + sH + sCl SiCl2 + 2s Si(B) + 2sCl 2sCl + Si(B) SiCl2 + 2s H2 + 2s 2sH 2sH 2s + H2 HCl + 2s sH + sCl sH + sCl 2s + HCl AMAT Centura Chemical Vapor Deposition Reactor Operating Conditions Reactor Pressure 1 atm Inlet Gas Temperature 698 K Surface Temperature 1173 K Inlet Gas-Phase Velocity 46.6 cm/sec Slide Courtesy of McRae/MIT
TREES H2O GRASS ROAD Target & Clutter Database ROI Hypothesis TREES TREES y GRASS BMP-2 Local Scene Map x ROI Hypothesis TREES Shadow (?) TREES y GRASS BMP-2 Local Scene Map x MSTAR (DARPA)(Moving and Stationary Target Acquisition and Recognition) Focus of Attention Index Database (created off-line) ... Search Tree Regions of Interest (ROI) Segmented Terrain Map SAR Image & Collateral Data - DTED, DFAD - Site Models - EOSAT imagery ... Indexing Target & Scene Model Database (created off line) Task Predict Task Extract Statistical Model Search Extract Predict Clutter Database CAD Match Results Tree Clutter Semantic Tree Form Associations Analyze Mismatch Refine Pose & Score Shadow Obscuration ? x2,y2, x1,y1, Score = 0.75 Ground Clutter Feature-to-Model Traceback Match
The e-Business / (CIM, CIE) Distributor Channel Order Processing Customer Service Sales Management Manufacturing Product DBs Inventory Shipping Application Integration Interoperability Process Coordination Management & Monitoring Business to Business Enterprise Messaging Data Integration Interoperability Mobile Workers Knowledge Workers Business Communications Business to Customer Web e-commerce
Compare withClassical (Old) Supply Chain Manufacturing Manufacturing Manufacturing Distribution Distribution Distribution Retail Retail Retail Customer Customer Customer Customer Customer Customer Parts Supplier Parts Supplier Transportation Supplier
Some Technology Challenges in Enabling DDDAS • Application development • interfaces of applications with measurement systems • dynamically select appropriate application components • ability to switch to different algorithms/components depending on streamed data • Algorithms • tolerant to perturbations of dynamic input data • handling data uncertainties • Systems supporting such dynamic environments • dynamic execution support on heterogeneous environments • Extended Spectrum of platforms: assemblies of Sensor Networks and Computational Grid platforms • GRID Computing, andBeyond!!!
What is Grid Computing? coordinated problem solving on dynamic and heterogeneous resource assemblies DATA ACQUISITION ADVANCEDVISUALIZATION ,ANALYSIS COMPUTATIONALRESOURCES IMAGING INSTRUMENTS LARGE-SCALE DATABASES Example:“Telescience Grid”, Courtesy of Ellisman & Berman /UCSD&NPACI
The NGS Program developsTechnology for integrated feedback & control Runtime Compiling System (RCS) and Dynamic Application Composition tac-com fire cntl alg accelerator …. data base data base fire cntl SAR MPP NOW SP Application Model Dynamic Analysis Situation Distributed Programming Model Application Program Compiler Front-End Application Intermediate Representation Compiler Back-End Launch Application (s) Performance Measuremetns & Models Dynamically Link & Execute Application Components & Frameworks Distributed Computing Resources Distributed Platform Adaptable computing Systems Infrastructure
Some more Challenges on Applications Development Issues • Handling Data Streams in addition to Data Sets • Handling different data structures – semantic information • Interfaces to Measurement Systems - Interactive Visualization and Steering • Standards for data exchange • Combining Local and Global Knowledge • Model Interactions • Application control of measurement systems • Dynamic Application Composition and Runtime support (Examples from ITR supported efforts)
Important Point: DDDAS is not just DATA ASSIMILATION!!! • Data Assimilation compares/corrects specific calculated points with experiments, rather than dynamically as need • Data Assimilation does not include the notion of the simulation/application controlling the measurement process Rather… Data Assimilation techniques can be used in certain DDDAS cases
Programming Environments • Procedural - > Model Based • Programming -> Composition • Custom Structures -> Customizable Structures (patterns, templates) • Libraries -> Frameworks -> Compositional Systems (Knowledge Based Systems) • Application Composition Frameworks and…. • Interoperability extended to include measurements • Data Models and Data Management • Extend the notion of Data Exchange Standards (Applications and Measurements)
Additional Considerations/Requirements on Hardware and Software Systems • Extended Spectrum of platforms • Assemblies of Computational Grid and Sensor Networks platforms • Systems Architectures including Measurement Systems • Programming Environments • Application, System, and Resource Management • Models of the Computational Infrastructure • Security and Fault Tolerance • DDDAS will accentuate and create the need for advances in such areas
Towards Enabling DDDAS Today’s Grid Environments: “Users shouldn’t Have to be Heroes to Achieve Grid Program Performance” and... because heroism is not enough Dynamic Data-Driven Application Systems -- Symbiotic Measurement&Simulation Systems Dynamic Compilers & Application Composition NGS Program Performance Engineering
Impact to CyberInfrastructure • The CyberInfrastructure that will result when thinks of the present paradigm of (disjoint) simulations and measurements will be different than the CyberInfrastructure needed to support DDDAS • For example, bandwidth requirements, resource allocation and other middleware and systems software policies, prioritization, security, fault tolerance, recovery, QoS, etc…, will be different when one needs to guarantee data streaming to an executing simulation or control of measurement process
Why Now is the Time for DDDAS • Technological progress has prompted advances in some of the challenges • Computing speeds advances (uni- and multi-processor systems), Grid Computing, Sensor Networks • Systems Software • Applications Advances (parallel & grid computing) • Algorithms advances (parallel &grid computing, numeric and non-numeric techniques: dynamic meshing, data assimilation) • Examples of efforts in: • Systems Software • Applications • Algorithms
Agency Efforts • NSF • NGS: The Next Generation Software Program (1998- ) • develops systems software supporting dynamic resource execution • Scalable Enterprise Systems Program (1999, 2000-2003) • geared towards “commercial” applications (Chaturvedi example) • ITR: Information Technology Research (NSF-wide,FY00-04) • has been used as an opportunity to support DDDAS related efforts • In FY00 1 NGS/DDDAS proposal received; deemed best, funded • In FY01, 46 ~DDDAS pre-proposals received; many meritorious; 24 proposals received; 8 were awarded • In FY02, 31 ~DDDAS proposals received; 8(10) awards • In FY02, so far: received 35 (“Small” ITR) proposals ~DDDAS; more expected in the “Medium ITR” category - • Gearing towards a DDDAS program • expect participation from other NSF Directorates • Looking for participation from other agencies!
“~DDDAS” proposals awarded in FY00 ITR Competition • Pingali, Adaptive Software for Field-Driven Simulations
“~DDDAS” proposals awarded in FY01 ITR Competition • Biegler – Real-Time Optimization for Data Assimilation and Control of Large Scale Dynamic Simulations • Car – Novel Scalable Simulation Techniques for Chemistry, Materials Science and Biology • Knight – Data Driven design Optimization in Engineering Using Concurrent Integrated Experiment and Simulation • Lonsdale – The Low Frequency Array (LOFAR) – A Digital Radio Telescope • McLaughlin – An Ensemble Approach for Data Assimilation in the Earth Sciences • Patrikalakis – Poseidon – Rapid Real-Time Interdisciplinary Ocean Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment • Pierrehumbert- Flexible Environments for Grand-Challenge Climate Simulation • Wheeler- Data Intense Challenge: The Instrumented Oil Field of the Future
“~DDDAS” proposals awarded in FY02 ITR Competition • Carmichael – Development of a general Computational Framework for the Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints • Douglas-Ewing-Johnson – Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation (DDDAS) Techniques • Evans – A Framework for Environment-Aware Massively Distributed Computing • Farhat – A Data Driven Environment for Multi-physics Applications • Guibas – Representations and Algorithms for Deformable Objects • Karniadakis – Generalized Polynomial Chaos: Parallel Algorithms for Modeling and Propagating Uncertainty in Physical and Biological Systems • Oden – Computational Infrastructure for Reliable Computer Simulations • Trafalis – A Real Time Mining of Integrated Weather Data
Measured Response A Homeland Security Simulation (Briefed WH 5/14/02) Alok Chaturvedi, Director Shailendra Mehta, co-Director Purdue e-Business Research Center Partners • Institute for Defense Analyses • Office of VP IT, Purdue University • Research and Academic Computing, Indiana University • Simulex, Inc
Parallel Worlds Simulation Loop Time Compression Decision Support Loop Near exact replica of the “real” world Real World Environment Synthetic Environment Behavior modeling, demographics, and calibration SCM ERP CRM Data Warehouse SEAS architecture Supports millions of Artificial agents Data collection, association, trends, and parameter estimation Explore, Experiment, Learn, Analyze, Test, & Anticipate Implement, Assess The user(s) can seamlessly switch between real and virtual worlds through an intuitive user interface.
Reproduction Model Get in contact with infected Infected w/o Symptoms Susceptible Exposed entering incubation period Uninfected Immunized end of incubation period mortality not due to infection Immune recovered Infected w/ Symptoms Mortality Succumb to the disease Interventions: Screen, Isolate (camp or shelter), Treat, Vaccinate
Mobility Models • Regular Movement • Event Traffic • Morning and Evening Rush • Evacuation • Panic Fleeing
New Infections T6 Intervention No Intervention T2 Intervention T4 Intervention
Towards a National Grid for HLS Data Fusion Bio sensor human MEMS The virtual world electronic Nano Sensor Real World
NSF ITR Project A Data Intense Challenge: The Instrumented Oilfield of the Future PI: Prof. Mary Wheeler, UT Austin Multi-Institutional/Multi-Researcher Collaboration Slide Courtesy of Wheeler/UTAustin
Highlights of Instrumented Oilfield Proposal • IT Technologies: • Data management, data visualization, parallel computing, and decision-making tools such as new wave propagation and multiphase, multi- component flow and transport computational portals, reservoir production: THE INSTRUMENTED OILFIELD • Major Outcome of Research: • Computing portals which will enable reservoir simulationand geophysical calculations to interact dynamically with the data and with each other and which will provide a variety of visual and quantitative tools. Test data provided by oil and service companies
Economic Modeling and Well Management Production Forecasting Well Management Reservoir Performance Simulation Models Visualization Data Analysis Multiple Realizations Field Measurements Data Management and Manipulation Reservoir Monitoring Field Implementation Data Collections from Simulations and Field Measurements
ITR Project • A Data Intense Challenge: • The Instrumented Oilfield of the Future • Industrial Support (Data): • British Petroleum (BP) • Chevron • International Business Machines (IBM) • Landmark • Shell • Schlumberger
Dynamic Contrast Enhanced Imaging • Dynamic image quantification techniques • Use combination of static and dynamic image information to determine anatomic microstructure and to characterize physiological behavior • Fit pharmacokinetic models (reaction-convection-diffusion equations) • Collaboration with Michael Knopp, MD
Dynamic Contrast Enhanced Imaging • Dynamic image registration • Correct for patient tissue motion during study • Register anatomic structures between studies and over time • Normalization • Images acquired with different patterns spatio-temporal resolutions • Images acquired using different imaging modalities (e.g. MR, CT, PET)
Clinical Studies using Dynamic Contrast Imaging • 1000s of dynamic images per research study • Iterative investigation of image quantification, image registration and image normalization techniques • Assess techniques’ ability to correctly characterize anatomy and pathophysiology • “Ground truth” assessed by • Biopsy results • Changes in tumor structure and activity over time with treatment
prior to therapy 1370 1370 after 2 cycles 1421 1421 1421 after 4 cycles 1438 1438 Knopp M, OSU Radiology / dkfz
Software Support • Component Framework for Combined Task/Data Parallelism • Use defines sequence of pipelined components -- “filter group” • User directive tells preprocessor/runtime system to generate and instantiate copies of filters • Many filter groups can be simultaneously active • Integration proceeding with Globus/Network Weather Service
Adaptive Software Project • Cornell University • CS department (Keshav Pingali) • Civil and Environmental Engineering (Tony Ingraffea) • Mississippi State University • University of Alabama, Birmingham • Mechanical and Aerospace (Bharat Soni) • College of William and Mary • Ohio State University • Clark-Atlanta University
SCOPE of ASP • Implement a system for multi-physics multi-scale adaptive CSE simulations • computational fracture mechanics • chemically-reacting flow simulation • Understand principles of implementing adaptive software systems Cracks: They’re Everywhere!
Problem description • Regenerative cooling nozzle from NASA • Simplified geometry • Chemically-reacting flow in interior of pipe • Nozzle is cooled by fluid-flow in eight smaller channels at periphery of pipe • Problem: • simulate flows • determine crack growth • couple the multi-physics models • When successful add the ability to inject monitoring measurements
Understanding fracture • Wide range of length and time scales • Macro-scale (1in- ) • components used in engineering practice • Meso-scale (1-1000 microns) • poly-crystals • Micro-scale(1-1000 Angstroms) • collections of atoms 10-6 m 10-9 10-3
Chemically-reacting flows • MSU/UAB expertise in chemically-reacting flows • LOCI: system for automatic synthesis of multi-disciplinary simulations
Pipe Workflow MiniCAD SurfaceMesher SurfaceMesht GeneralizedMesher JMesh Modelt T4 SolidMesht FluidMesht Mechanical Tst/Pst Fluid/Thermo T4T10 Client: CrackInitiation T10 SolidMesht Initial FlawParams CrackInsertion Dispst Modelt+1 FractureMechanics GrowthParams1 CrackExtension Viz