Dynamic Data-Driven Application Simulation (DDDAS)

Clay HarrisJay Hatcher

Cindy Burklow

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

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• 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

CPU

Initial Conditions

Graphical Output

Initial Algorithm

DDDAS Infrastructure

CPUReal-Time

SensorsGraphical

Output

Initial Algorithm

ExperimentMeasurements

Field-DataUser

Theory

(First P

rincip

les)

Simula

tions

(Math

.Modeli

ng

Phenomenol

ogy) ExperimentMeasurements

Field-DataUser

Theory

(First P

rincip

les)

Simula

tions

(Math

.Modelin

g

Phenomenolo

gy

Observ

ation M

odeling

Design)

OLD

(serialized and static)

NEW PARADIGM

(Dynamic Data-Driven Simulation Systems)

Challenges:Application Simulations DevelopmentAlgorithms Computing Systems Support

Dynam

ic

Feed

back

& C

ontro

l

Loop

What is DDDAS

Frederica Darema, NSF

A DDDAS Model(Dynamic, Data-Driven Application Systems)

Spectrum of Physical SystemsSpectrum of Physical Systems

Humans:3 Hz.

Cosmological:10e-20 Hz.

Subatomic:10e+20 Hz.

ComputationalInfrastructure(grids, perhaps?)

ModelsModels

ComputationsComputations

Discover, Ingest, Interact

sensors & actuators sensors & actuators sensors & actuators

Loads a Loads a behaviorbehavior into intothe infrastructurethe infrastructure

Craig Lee, IPDPS panel, 2003

Discover,Ingest,Interact

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

• 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 experiments4. Combine experiment and simulation data

together to build Surrogate Model5. Optimize SM and obtain the Pareto Set

DDDOM Application – Cooling of Electronic Components

• Two Optimization methods used:1. Epsilon constraint method

2. Multi-Objective Switching Genetic Algorithm (OSGA)

• 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:• 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

• 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 time-varying 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 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

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

• 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

Why use DDDAS…

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 large-scale 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 problemin 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 real-

time & 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)

A class of observing systems that are…

• Embedded into the environment• Consist of stationary & mobile sensors• React to collected observations

What is ROS?

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• 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

• 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

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