Dynamic Data-Driven Application
Simulation (DDDAS)
Clay Harris
Jay Hatcher
Cindy Burklow
General Simulation
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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
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
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
Dynamic Predictions
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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
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
Traditional Simulation Infrastructure
Graphical
Output
CPU
Initial
Algorithm
Initial
Conditions
DDDAS Infrastructure
Graphical
Output
CPU
Initial
Algorithm
Real-Time
Sensors
What is DDDAS
Experiment
Measurements
Field-Data
User
Experiment
Measurements
Field-Data
User
Challenges:
Application Simulations Development
Frederica Darema, NSF Algorithms
Computing Systems Support
A DDDAS Model
(Dynamic, Data-Driven Application Systems)
Discover,
Ingest,
Discover, Ingest, Interact Models
Interact
Computations
Loads a behavior into
the infrastructure
sensors & actuators
sensors & actuators
Cosmological: Humans:
10e-20 Hz.
3 Hz.
sensors & actuators
Computational
Infrastructure
(grids, perhaps?)
Spectrum of Physical Systems
Craig Lee, IPDPS panel, 2003
Subatomic:
10e+20 Hz.
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
Data Determines Everything
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The algorithm used
Additional data collected
Simulation restart (cold or warm)
Output correction
Communications with people
The Result!
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
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
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
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
DDDOM Architecture
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
DDDOM Application – Cooling of
Electronic Components
1. Select 25 sampling points from design
space
2. Perform computations at these 25 points
on supercomputers
3. Get experiment data from experiments
4. Combine experiment and simulation data
together to build Surrogate Model
5. Optimize SM and obtain the Pareto Set
DDDOM Application – Cooling of
Electronic Components
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Two Optimization methods used:
1. Epsilon constraint method
2. Multi-Objective Switching Genetic Algorithm
(OSGA)
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Results are comparable, with OSGA
giving more data points
DDDOM Application – Cooling of
Electronic Components
O’SOAP
• A web services framework for DDDAS
applications.
• Geographically distributed set of
application components
• Reduces the effort required to develop
DDDAS applications
O’SOAP – 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
O’SOAP
• 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
O’SOAP
• DDDAS developers must consider:
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Generating Interface Documentation
Data management concerns
Asynchronous Interactions
Authentication, Authorization and Accounting
(AAA)
• Job Scheduling
• Performance
O’SOAP
• Current web service technologies present
the developer with a blank slate
• For a novice, developing a web serviced
DDDAS application is a difficult
undertaking
• O’SOAP provides a framework for
designing DDDAS applications with
minimal interface code and developer effort
O’SOAP - 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 application’s status
O’SOAP - 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)
O’SOAP – Implementation
• Performance measured with the Pipe
Problem
• Simulates an idealized segment of rocket
engine modeled after one of NASA’s
experimental rocket designs
• Three different sizes of the Pipe Problem
used to evaluate how performance scales
O’SOAP – Implementation
Some Characteristics of
DDDAS Projects
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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
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
Contamination Threat Management
of drinking H20 involves….
• Real-time characterization of
contaminant source & plume
• Identification of control
strategies
• Design of incremental data
sampling schedules.
Why use DDDAS…
• Requires dynamic
integration of timevarying measurements
of flow, pressure and
contaminant
concentration
• Uses analytical
modules are highly
compute-intensive,
requiring multi-level
parallel processing via
computer clusters
Project’s 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
Fault Diagnosis of
Wind Turbine Systems
Current Situation…
• Current practices are nondynamic & 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 costeffective generation of wind
electricity
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
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
Project’s 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
Production Planning & Operational
Control for Distributed Enterprise
• Society depends upon
many interacting largescale dynamic systems
• Too complex for
mathematical analysis
• Behavior of system
networks depends on
their linkages and the
environment
Involves…
• Focus on hierarchical production
• Logistics planning
• Control in highly capitalized discrete
manufacturing system networks
Why use DDDAS…
• Requires complex simulations
• Needs dynamic reaction to various
situations
• Utilizes centralized control
• High cost & financial risk involved
Project’s 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
Brain-Machine Interfaces (BMI)
• Brain receives & uses sensory feedback to
learn & generate signals to produce
purposeful motion.
• Address chief problem
in current BMI research:
paraplegics cannot train
their own network models
because they cannot
move their limbs.
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.
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
Project’s DDDAS infrastructure
• Develop models
• Implement algorithms
• Deploy computational architecture
All the above will utilize recently proposed
advanced brain models of motor control.
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
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
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
Project’s DDDAS Infrastructure
• Network of wireless sensors on trees
• Environmental models that provide realtime & approximate answers.
• In-network controls that
schedule new measurements
• Communication system to transfer data
to server
Coast & Environment
Modeling Applications
• Urgent scientific
ecological problems:
Ocean circulation,
storm surge, and
wave generation
• Coastal Modeling of
Louisiana’s coastal
& Mississippi Delta
region
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
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
Project’s 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
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
Goals of ROS
• Verify or falsify
hypotheses with samples
taken via sensor devices
• Analyze data
autonomously to detect
trends or to alert
problematic conditions
Applications
• Resource Management
• Environmental Protection
• Public Health
• Any area that requires close environmental
monitoring would benefit from ROS.
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
Harmful Algae Bloom
Why considered DDDAS….
• Develop approach to optimized &
control sample sets of all possible
relevant data
• Secure sample at any time while
taking in account app’s objectives
& resource constraints
• Automatic validation & adaptation
• Includes distributed support
mechanism for locating relevant
data of interest
Other current DDDAS projects
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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
Useful Links
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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/robodemo.html
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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-lyon1.fr/~cpham/GDT/DOC/EventDrivenWorkflows_Lyon_0904.ppt
http://www.dddas.org/projects.html
http://www.cse.nd.edu/news/news.php?id=762
https://www.cs.duke.edu/ari/millywatt/funding.html
http://www.engr.uconn.edu/~jtang/research.htm
http://www.acis.ufl.edu/index.php?l=44
http://www.darpa.mil/baa/baa01-42mod1.htm
http://forestry.about.com/library/tree/blredwd.htm?pid=2820&cob=home
http://www.whoi.edu/science/B/redtide/whathabs/whathabs.html
http://www.whoi.edu/science/B/redtide/rtphotos
http://www.baldridge.unizh.ch/nsf/ITR_RTIGNS/
http://splweb.bwh.harvard.edu:8000/
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
http://www.bio-itworld.com
http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=13511&org=CISE&from=home
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540132
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540177
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540289
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540316
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540278
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540212
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540374
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540304
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540347
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540414
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540420
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0540076