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Dynamic DataDriven Application Simulation DDDAS

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Calculations may change depending upon the incoming data ... (Math.Modeling. Phenomenology. Observation Modeling. Design) ... Utilizes historical & online signals ... – PowerPoint PPT presentation

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Title: Dynamic DataDriven Application Simulation DDDAS


1
Dynamic Data-Driven Application Simulation (DDDAS)
Clay Harris Jay Hatcher Cindy Burklow
2
General Simulation
  • Calculations are predefined
  • Boundary conditions are predefined
  • Initial data is given
  • Time step is predefined
  • Additional data input at predetermined times
  • Results are recorded and often studied later

3
DDDAS
  • Calculations may change depending upon the
    incoming data
  • Boundary conditions may be updated during the
    simulation
  • Initial data is given, but may be corrected at a
    later time
  • Time step may change depending upon incoming data
    values
  • Additional data comes in anytime and out of order
  • Frequently the results are monitored in real time

4
What is DDDAS?
  • A DDDAS is one where data is fed into an
    executing application either as the data is
    collected or from a data archive 1, p. 662.
  • The data is then used to influence the
    measurements for additional data the simulation
    may require.

1 Frederica Darema. Dynamic Data Driven
Applications Systems A New Paradigm for
Application Simulations and Measurements.
International Conference on Computational
Science. 662-669. 2004
5
Dynamic Predictions
  • Wildfire Forecasting
  • Tsunami Forecasting
  • Traffic Jam Forecasting
  • Weather Forecasting
  • Global Warming El Nino
  • Ocean Modeling
  • Cyclone Movement Prediction
  • Threat Management in Urban Water Supplies
  • Fault Diagnosis of Wind Turbine System
  • Operational Control for Manufacturing
  • Brain Machine Interface
  • Landscape Biophysical Change

6
Keep in mind with DDDAS
  • Typically approximating a nonlinear time
    dependent partial differential equation
    nontraditional convergence
  • Perturbations from incoming data
  • Inaccurate data
  • Propagation of error
  • Boundary conditions are rarely known

7
Traditional Simulation Infrastructure
CPU
Graphical Output
Initial Conditions
Initial Algorithm
8
DDDAS Infrastructure
CPU
Real-Time Sensors
Graphical Output
Initial Algorithm
9
What is DDDAS
Simulations (Math.Modeling Phenomenology Observati
on Modeling Design)
Theory (First Principles)
Simulations (Math.Modeling Phenomenology)
Theory (First Principles)
Experiment Measurements Field-Data User
Experiment Measurements Field-Data User
Dynamic Feedback Control Loop
Challenges Application Simulations
Development Algorithms Computing Systems Support
Frederica Darema, NSF
10
A DDDAS Model(Dynamic, Data-Driven Application
Systems)
Discover, Ingest, Interact
Models
Discover, Ingest, Interact
Computations
Loads a behavior into the infrastructure
sensors actuators sensors actuators
sensors actuators
Humans 3 Hz.
Cosmological 10e-20 Hz.
Subatomic 10e20 Hz.
Computational Infrastructure (grids, perhaps?)
Spectrum of Physical Systems
Craig Lee, IPDPS panel, 2003
11
DDDAS Research
  • Data injection methods
  • 2-way communication with sensors
  • Quick methods for static simulation conversion to
    DDDAS
  • Infrastructure support for dynamic methods
    including communications support, data driven
    technologies, and OS software

12
Data Determines Everything
  • The algorithm used
  • Additional data collected
  • Simulation restart (cold or warm)
  • Output correction
  • Communications with people
  • The Result!

13
Dynamic Work Flows
  • Flexible event handling system notifies
    appropriate recipients of relevant events
  • Dynamic workflow handling system coordinates and
    schedules actions in response to known events

14
Dynamic Work Flows
  • Events delivered using a publish/subscribe model
    or based on content
  • Decision makers receive event notification and
    make an appropriate response decision
  • Responses are executed by a workflow engine that
    schedules data transfer and process execution

15
Dynamic Work Flows
  • Besides events causing an initial response,
    subsequent events may alter an existing workflow
  • Current amount of workflow completed must be
    determined
  • Current tasks on the leading edge of the
    workflow must be terminated or allowed to
    complete
  • Status and disposition of data referenced by data
    handles must be determined
  • Storage management issues
  • Dangling references to no data or stale data
  • Inaccessible data referenced by no one

16
Data Driven Design Optimization Methodology
(DDDOM)
  • DDDOM uses DDDAS to find an optimal solution to
    an engineering design problem
  • Used in Multi-criteria Design Optimization (MDO)
    problems
  • Uses Rapid Prototyping, Grid Computing, and other
    advanced technologies to perform simultaneous
    experimentation and simulation to achieve optimal
    designs

17
(No Transcript)
18
DDDOM Architecture
19
DDDOM Application Cooling of Electronic
Components
  • Optimize design for cooling system
  • Maximize heat transfer and minimize pressure drop
  • Increasing heat transfer also increases pressure
    drop, so there is no specific solution, but
    rather a set of good solutions
  • Problem is a MDO problem

20
DDDOM Application Cooling of Electronic
Components
  • Select 25 sampling points from design space
  • Perform computations at these 25 points on
    supercomputers
  • Get experiment data from experiments
  • Combine experiment and simulation data together
    to build Surrogate Model
  • Optimize SM and obtain the Pareto Set

21
DDDOM Application Cooling of Electronic
Components
  • Two Optimization methods used
  • Epsilon constraint method
  • Multi-Objective Switching Genetic Algorithm
    (OSGA)
  • Results are comparable, with OSGA giving more
    data points

22
DDDOM Application Cooling of Electronic
Components
23
OSOAP
  • A web services framework for DDDAS applications.
  • Geographically distributed set of application
    components
  • Reduces the effort required to develop DDDAS
    applications

24
OSOAP Advantages over traditional monolithic
applications
  • Developer only needs to implement a program
    component on a single local platform
  • Loosely-coupled nature of the components
    facilitates reuse for new simulations
  • Allows simultaneous use for multiple research
    projects

25
OSOAP
  • Current web service technologies are inadequate
    for DDDAS applications
  • They are generally geared for more interactive
    applications
  • Often have a learning curve that is steep enough
    to discourage computational scientists from
    experimenting with a remote DDDAS system

26
OSOAP
  • DDDAS developers must consider
  • Generating Interface Documentation
  • Data management concerns
  • Asynchronous Interactions
  • Authentication, Authorization and Accounting
    (AAA)
  • Job Scheduling
  • Performance

27
OSOAP
  • Current web service technologies present the
    developer with a blank slate
  • For a novice, developing a web serviced DDDAS
    application is a difficult undertaking
  • OSOAP provides a framework for designing DDDAS
    applications with minimal interface code and
    developer effort

28
OSOAP - Implementation
  • Applications deployed as distributed components
  • Services automatically documented with WSDL
  • Asynchronous communication supported by sending a
    job ID for the remote application back to the
    client, which periodically checks the remote
    applications status

29
OSOAP - Implementation
  • Supports small and large data sizes
  • If data size is small or programmer requests the
    data is included in a SOAP envelope as XML (pass
    by value)
  • If data is large or programmer requests a URL is
    sent in the envelope pointing to the data (pass
    by reference)

30
OSOAP Implementation
  • Performance measured with the Pipe Problem
  • Simulates an idealized segment of rocket engine
    modeled after one of NASAs experimental rocket
    designs
  • Three different sizes of the Pipe Problem used to
    evaluate how performance scales

31
OSOAP Implementation
32
Some Characteristics of DDDAS Projects
  • Managing complex scenarios
  • Predicting high risk areas safety
  • Effects large population of people
  • Involves natural environment
  • Impact on the overall economy
  • Needs multi-disciplined team
  • Real-time analysis is critical

33
Threat Management in Urban Water Distribution
Systems
  • Situation
  • Highly interconnected water transport system
  • Frequent flow fluctuations
  • Highly dynamic transport paths
  • Single point of contamination can quickly spread

34
Contamination Threat Management of drinking H20
involves.
  • Real-time characterization of contaminant source
    plume
  • Identification of control strategies
  • Design of incremental data sampling schedules.

35
Why use DDDAS
  • Requires dynamic integration of time-varying
    measurements of flow, pressure and contaminant
    concentration
  • Uses analytical modules are highly
    compute-intensive, requiring multi-level parallel
    processing via computer clusters

36
Projects DDDAS infrastructure
  • Develop cyber-infrastructure system that will
    both adapt to and control changing needs in data,
    models, computer resources and management choices
    facilitated by a dynamic workflow design
  • Virtual Simulations
  • Field Studies

37
Fault Diagnosis ofWind Turbine Systems
  • Current Situation
  • Current practices are non-dynamic non-robust
    for modeling, data collection, processing
    strategies
  • Clean wind energy cannot compete with traditional
    energy source
  • High financial cost compared to other energy
    sources
  • High maintenance cost
  • Low confidence in the diagnosis technology
  • Need for enabling a cost-effective generation of
    wind electricity

38
Involves
  • Development of diagnosis system for wind
    turbines
  • Fault diagnosis of blades and gearboxes
  • Utilizes historical online signals
  • Employs novel de-noising sensor anomaly removal
    algorithms

39
Why use DDDAS
  • Involves collaborative research that is
    multidisciplinary
  • Benefits a larger range of industries such as
    power generation, automobile, aerospace, and
    engine industries.
  • Effects the overall general population with clean
    air issues
  • Effects energy economic costs

40
Projects DDDAS infrastructure
  • 2 robust data pre-processing modules for
    highlighting fault features and removing sensor
    anomaly
  • 3 interrelated, multi-level models that describe
    different details of the system behaviors
  • 1 dynamic strategy for the robust local
    interrogation that allows for measurements to be
    adaptively taken according to specific physical
    conditions and the associated risk level.
  • Overall incorporates both historical data and
    on-line signals into the system modeling

41
Production Planning Operational Control for
Distributed Enterprise
  • Society depends upon many interacting large-scale
    dynamic systems
  • Too complex for mathematical analysis
  • Behavior of system networks depends on their
    linkages and the environment

42
Involves
  • Focus on hierarchical production
  • Logistics planning
  • Control in highly capitalized discrete
    manufacturing system networks

43
Why use DDDAS
  • Requires complex simulations
  • Needs dynamic reaction to various situations
  • Utilizes centralized control
  • High cost financial risk involved

44
Projects DDDAS infrastructure
  • Multi-scale federation of interwoven simulations
  • Decisions models for planning
  • Control with capability for dynamic updating
    through sensors
  • Capacity to use off-line performance testing
  • Integrated architecture for distributed computing
  • Utilizes sensors, transducers, and actuators
  • Web service technology

45
Brain-Machine Interfaces (BMI)
  • Brain receives uses sensory feedback to learn
    generate signals to produce purposeful motion.
  • Address chief problemin current BMI research
    paraplegics cannot train their own network models
    because they cannot move their limbs.

46
Involves
  • Cognitive brain modeling from experiments with
    live subjects
  • Design of brain-inspired assistive systems to
    help human beings with severe motor behavior
    limitations (e.g. paraplegics) through
    brain-machine Interfaces (BMIs).
  • BMI uses brain signals to directly control
    devices such as computers and robots.

47
Why use DDDAS
  • Complexity of relationship between the brain
    nervous system
  • Learning occurs simultaneously for the subject
    and the control models in a synergistic manner
  • Selective use of many computational models
  • Interdisciplinary team

48
Projects DDDAS infrastructure
  • Develop models
  • Implement algorithms
  • Deploy computational architecture

All the above will utilize recently proposed
advanced brain models of motor control.
49
Sensor Networks Enabling Measurement, Modeling
Prediction of Biophysical Change in a Landscape
  • Collecting environmental data is challenging
  • Deployed in remote locations
  • No access to infrastructure (e.g. power)
  • Wide range of sampling time variables

50
Involves
  • Understanding how biodiversity carbon storage
    are influenced by global change
  • Wireless sensor network
  • Models of tree growth resource allocation
  • Adaptive sampling across diverse time space
    scales

51
Why use DDDAS
  • Integrates sensors with modeling in adaptive
    framework
  • Requires network controls that must be dynamic
  • Driven by models capable of learning adapting
    to both environment network

52
Projects DDDAS Infrastructure
  • Network of wireless sensors on trees
  • Environmental models that provide real-time
    approximate answers.
  • In-network controls that schedule new
    measurements
  • Communication system to transfer data to server

53
Coast Environment Modeling Applications
  • Urgent scientific ecological problems Ocean
    circulation, storm surge, and wave generation
  • Coastal Modeling of Louisianas coastal
    Mississippi Delta region

54
Involves
  • Modeling ecological, hydrodynamic, sediment
    transport in the Delta
  • Develop new infrastructure algorithms to
    address issues for ocean circulation, storm surge
    wave generation
  • Collect data via external wireless sensors from
    both water wind

55
Why use DDDAS
  • Real-time coupled with data input complex
    workflows
  • Complex simulations
  • Huge impact on human animal quality of life
  • Economical environmental devastation of
    Hurricanes Coastal Flooding

56
Projects DDDAS infrastructure
  • Develop system called DynaCode.
  • Utilize emerging standards for Cactus, Triana,
    and Grid Services
  • Wrapping legacy codes
  • Integrating framework for new advanced code
  • Running multi-scale simulations

57
Reactive Observing Systems (ROS)
What is ROS?
  • A class of observing systems that are
  • Embedded into the environment
  • Consist of stationary mobile sensors
  • React to collected observations

58
Goals of ROS
  • Verify or falsify hypotheses with samples taken
    via sensor devices
  • Analyze data autonomously to detect trends or to
    alert problematic conditions

59
Applications
  • Resource Management
  • Environmental Protection
  • Public Health
  • Any area that requires close environmental
    monitoring would benefit from ROS.

60
Current NSF Grant for ROS
  • Focus on marine biology application monitoring
    the concentration of algae micro-organisms
  • Stationary mobile sensors
  • Wireless wired links
  • Collects data in real time

61
Harmful Algae Bloom
62
Why considered DDDAS.
  • Develop approach to optimized control sample
    sets of all possible relevant data
  • Secure sample at any time while taking in account
    apps objectives resource constraints
  • Automatic validation adaptation
  • Includes distributed support mechanism for
    locating relevant data of interest

63
Other current DDDAS projects
  • Integrated Wireless Phone Based Emergency
    Response System (WIPER)
  • Integrating Real-Time Data and Intervention
    During Image Guided Therapy
  • Real-Time Order Promising and Fulfillment for
    Global Make-to-Order Supply Chains
  • Optimal interlaced distributed control and
    distributed measurement with networked mobile
    actuators and sensors
  • Dynamic, Simulation-Based Management of Surface
    Transportation Systems
  • Interactive Data-driven Flow-Simulation Parameter
    Refinement for Understanding the Evolution of Bat
    Flight
  • Planet-in-a-Bottle A Numerical Fluid-Laboratory
    System
  • Integrating Multipath Measurements with Site
    Specific RF Propagation Simulations
  • Auto-Steered Information-Decision Processes for
    Electric System Asset Management
  • Measuring and Controlling Turbulence and Particle
    Populations
  • Robustness and Performance in Data-Driven Revenue

64
Useful Links
  • http//www.dddas.org
  • http//www.nsf.gov/cise/cns/dddas/index.jsp
  • http//www.iccs-meeting.org
  • http//www.teragrid.org

Reinforcement Learning in Robotics
http//www.fe.dis.titech.ac.jp/gen/robot/robodem
o.html
65
References
  • Frederica Darema. Dynamic Data Driven
    Applications Systems A New Paradigm for
    Application Simulations and Measurements.
    International Conference on Computational
    Science. 662-669. 2004
  • Craig Lee, IPDPS panel, 2003 http//www710.univ-ly
    on1.fr/cpham/GDT/DOC/EventDrivenWorkflows_Lyon_09
    04.ppt
  • http//www.dddas.org/projects.html
  • http//www.cse.nd.edu/news/news.php?id762
  • https//www.cs.duke.edu/ari/millywatt/funding.html
  • http//www.engr.uconn.edu/jtang/research.htm
  • http//www.acis.ufl.edu/index.php?l44
  • http//www.darpa.mil/baa/baa01-42mod1.htm
  • http//forestry.about.com/library/tree/blredwd.htm
    ?pid2820cobhome
  • http//www.whoi.edu/science/B/redtide/whathabs/wha
    thabs.html
  • http//www.whoi.edu/science/B/redtide/rtphotos
  • http//www.baldridge.unizh.ch/nsf/ITR_RTIGNS/
  • http//splweb.bwh.harvard.edu8000/
  • http//citeseer.ist.psu.edu/656858.html
  • http//www.cs.cornell.edu/stodghil/paper...iccs04.
    pdf
  • http//coewww.rutgers.edu/knight/dddom/main/deopt.
    php
  • http//www.iccs-meeting.org/iccs2006/
  • http//phsi.mgmt.purdue.edu/dddas/project.html
  • http//www.teragrid.org
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