Transcript Geodise Dublin Intel

Grid Enabled Optimisation and
Design Search for Engineering
(GEODISE)
Expo
May 12th 2003
@ Southampton
Prof Simon Cox
Southampton University
http://www.geodise.org
© Geodise Project 2003
Thanks to …
• Nicola Reader for organisation
• … everyone for coming!
Grid Enabled Optimisation and Design
Search for Engineering (GEODISE)
Southampton, Oxford and Manchester
Simon Cox- Technical Director
Southampton e-Science Centre. Grid/
W3C Technologies and High
Performance Computing
Andy Keane- Director of Rolls Royce/
BAE Systems University Technology
Partnership in Design Search and
Optimisation
Mike Giles- Director of Rolls Royce
University Technology Centre for
Computational Fluid Dynamics
Carole Goble- Ontologies and DARPA
Agent Markup Language (DAML) /
Ontology Inference Language (OIL)
Nigel Shadbolt- Director of Advanced
Knowledge Technologies (AKT) IRC
BAE Systems- Engineering
Rolls-Royce- Engineering
Fluent- Computational Fluid Dynamics
Microsoft- Software/ Web Services
Intel- Hardware
Compusys- Systems Integration
Epistemics- Knowledge Technologies
Condor- Grid Middleware
The GEODISE Team ...
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Richard Boardman
Sergio Campobasso
Liming Chen
Mike Chrystall
Trevor Cooper-Chadwick
Simon Cox
Mihai Duta
Clive Emberey
Hakki Eres
Matt Fairman
Mike Giles
Carole Goble
Ian Hartney
Tracey Hunt
Zhuoan Jiao
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Andy Keane
Marc Molinari
Graeme Pound
Colin Puleston
Nicola Reader
Angus Roberts
Mark Scott
Nigel Shadbolt
Wenbin Song
Paul Smart
Barry Tao
Jasmin Wason
Fenglian Xu
Gang “Luke” Xue
Expo Objectives
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Demonstrate 18 month deliverables
Technology talks by RAs
Demos & Posters ‘deskside’ over lunch
Talks by industrial partners
Future plans
Design
Design Challenges
Modern engineering firms are global and distributed
How to … ?
… improve design environments
… cope with legacy code / systems
… produce optimized designs
CAD and analysis tools, user
interfaces, PSEs, and Visualization
Optimisation methods
… integrate large-scale systems in a
flexible way
Management of distributed compute
and data resources
… archive and re-use design history
Data archives (e.g. design/ system
usage)
… capture and re-use knowledge
Knowledge repositories &
knowledge capture and reuse tools.
“Not just a problem of using HPC”
23/7/2001
Gas Turbine Engine: Initial Design
Base Geometry
Secondary Kinetic Energy
Collaboration with Rolls-Royce
23/7/2001
Design of Experiment &
Response Surface Modelling
Initial
Geometry
RSM
Construct
DoE
RSM
Evaluate
CFD
CFD … CFD
CFD
CFD … CFD
CFD
CFD … CFD
CFD
CFD … CFD
Cluster
Parallel
Analysis
Search Using
RSM
CFD
Build
Data-Base
Adequate ?
Best
Design
RSM
Tuning
23/7/2001
Optimised Design
Geometry
Secondary Kinetic Energy
Distributed Systems 2003
Drivers = Building Blocks + Protocols
IP
Moore’s Law
HTTP
(HTML) XML
Web Services
(HTML)
Network
Compute/ Data
Software
(Proprietary, Open, Shared)
Grid Services
The Grid Problem
“Flexible and secure sharing of resources among
dynamic collections of individuals within and
across organisations”
• Resources = assets, capabilities, and knowledge
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Capabilities (e.g. application codes, analysis tools)
Compute Grids (PC cycles, commodity clusters, HPC)
Data Grids
Experimental Instruments
Knowledge Services
Virtual Organisations
Utility Services
Grid middleware mediates between these resources
GEODISE
Engineer
GEODISE
PORTAL
Knowledge
repository
Ontology for
Engineering,
Computation, &
Optimisation and
Design Search
Visualization
Session
database
Traceability
OPTIMISATION
OPTIONS
System
APPLICATION
SERVICE
PROVIDER
Intelligent
Application
Manager
Reliability
Security
QoS
CAD System
CADDS
IDEAS
ProE
CATIA, ICAD
Globus, Condor, OGSA
Optimisation
archive
COMPUTATION
Licenses
and code
Analysis
CFD
FEM
CEM
Design
archive
Parallel machines
Clusters
Internet Resource Providers
Pay-per-use
Intelligent
Resource
Provider
Geodise will provide grid-based seamless access to an intelligent knowledge
repository, a state-of-the-art collection of optimisation and search tools,
industrial strength analysis codes, and distributed computing & data resources
23/7/2001
Technologies (i) Grid Middleware
(To coordinate and authenticate use of components of Geodise)
• Globus (and GGF grid-computing protocols)
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Security Infrastructure (GSI)
Resource Allocation Mechanism (GRAM)
Resource Information System (GRIS)
Index Information Service (GIIS)
Grid-FTP
Metadirectory service (MDS 2.0+) coupled to LDAP server
• Condor (distributed high performance throughput system)
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Condor-G allows us to handle dispatching jobs to our Globus system
Active collaboration from with the Condor development team at
University of Wisconsin (Miron Livny)
23/7/2001
(ii) Data & Open W3C Standards
(To access and interchange data)
• SRB (Storage Resource Broker)
• XML and XML Schema
 Representing data
in a portable format
• WSDL (Web Service Description Language)
• UDDI (Universal Description, Discovery and
Integration)
 Publish
and discover information about web services
23/7/2001
(iii) Ontologies & Semantic Web
(conceptualisation of a community’s knowledge of a domain)
• DAML - OIL (DARPA Agent Markup Language/ Ontology
Inference Language)
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Genetics http://www.geneontology.org/
Virtual Enterprises
Product Specifications
Medicine
Encyclopaedic Knowledge
http://www.cyc.com/cyc-2-1/toc.html
23/7/2001
(iv) Knowledge Technologies
What we said… 28/06/2001
• “Consider the CFD based design optimisation of a typical aero-engine or wing
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component or system. For a single loop of the design process it is necessary to (1)
specify the geometry in a parametric form which defines the permitted operations and
constraints for the optimisation process (this goes beyond the STEP/ IGES interchange
standards), (2) decide which code to use for the analysis, (3) generate a mesh for the
problem (though this may be provided by the analysis code), (4) decide the
optimisation schedule, (5) execute the optimisation run coupled to the analysis code,
and finally (6) monitor and steer the search as it takes place, possibly stopping it mid
run to modify or rework the design process. Such loops are typically passed through
several times. In our Grid environment these operations are large-scale and physically
distributed computational steps: this will stretch the computation dimension in our
Grid and will be delivered out first by a wizard-based Grid demonstrator:
“Geodise-W” after 18 months.”
“Whilst this is being developed we will be working on the knowledge-based
demonstrator: “Geodise-K”, which we will deliver at 36 months. Here we seek to
enhance each of the components of our system by using databases, ontologies and
knowledge capture tools to provide intelligent guidance and assistance to the engineer
using our system. We will develop, refine and deploy knowledge bases for CAD,
commercial CFD code (Fluent), user supplied/ source-available CFD code (Oxford’s
Hydra), optimisation and computation services. The knowledge bases will be
physically distributed: integrating these large-scale distributed data sources will stretch
the data interchange dimension in our Grid beyond that already required to execute and
visualise our results. A conceptual architecture of our system is shown below.”
“18 month”
Geodise-W
Engineer
23/7/2001
GEODISE
PORTAL
Session
database
Traceability
OPTIMISATION
Globus, Condor, SRB
OPTIONS
System
Optimisation
archive
APPLICATION
SERVICE
PROVIDER
CAD System
CADDS
IDEAS
ProE
CATIA, ICAD
COMPUTATION
Licenses
and code
Analysis
CFD
FEM
CEM
Design
archive
Parallel machines
Clusters
Internet Resource Providers
Pay-per-use
18mth deliverable
Geodise
“Build complex things
from lots of simple things”
© Geodise Project 2003
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Geodise Workflow/ Integration
Requirements
Flexibility
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Customise the workflow and its components initially
 Compose a work flow via drag & drop activity node component into
an editor panel
 Link to knowledge and other services
Monitoring
 Interact with the workflow or its components during simulation
 Job status
 Resource usage
Maintainable
 Modify & re-use the workflow either in a GUI or in a human readable
file
Usability
 Easy to use by engineering users
Geodise Architecture
Intelligent
Support
Knowledge
Services
Matlab
(or Jython)
Integration
& Scripting
Java / C#
Interface
Web Service
Grid Service
Java / C#/
.NET
.EXE/ Fortran/
Matlab Code
Building
Blocks
CFD-based shape optimisation using Geodise toolkits
Nacelle Optimisation Problem – problem definition
The aim is to understand the effect of various geometry parameters on the
aerodynamic performance of engine nacelle, there is no attempt at this stage
to calculate the radiated noise from fan, it is simply assumed that the bigger the
scarf angle, more reduction in noise will be achieved.
Two parameters were first chosen: scarf angle and axial offset
Performance is measured using Total Pressure Recovery
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Conventional Inlet
Total Pressure Recovery (TPR) =
Negative Scarf Inlet
pt 2
pt1
CFD-based shape optimisation using Geodise toolkits
Parallel Grid-enabled evaluations of multiple design jobs
Within the Matlab hosting environment:
• Define the problem;
• Generate a proxy using user’s credentials;
• Retrieve the CAD definition file from repository;
• Retrieve the Gambit Journal file from repository;
• Retrieve the Fluent Journal file from repository;
• Submit CAD-Gambit-Fluent jobs sequentially or in parallel;
(gd_cfdone, gd_cfdanalysis)
a) Submit ProEngineer jobs to Windows Condor Pool via Webservice interface;
(grid_submit, grid_status)
b) Submit Gambit jobs to Grid-enabled Computing Servers;
(gd_jobsubmit, gd_job_status)
c) Submit Fluent jobs to Grid-enabled Computing Servers;
7. Postprocess results and archiving data files.
(gd_archive, gd_datagroupadd)
CFD-based shape optimisation using Geodise toolkits
Optimisation using Design of Experiment/ Response Surface Modelling
DoE using OPTIONS
Design of Experiment
Axial offset
Problem definition
Scarf angle
Response surface modelling
Optimisation on Response surface
Validation
CFD-based shape optimisation using Geodise toolkits
Optimisation using DoE/RSM models and two-stage approach
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Generate a proxy using user’s credentials;
Load in DoE data;
Create the RSM model;
Genetic Algorithms search on the RSM;
A further gradient-based search on GA result
Geodise Demo 1
© Geodise Project 2003
Arcadia-Options Demo
Matlab
Geodise
file archive
projectstruct.xml
gd_archive.m
optionsmatlab.dll
gd_objsubmit.m
x5
gd_jobpoll.m
x5
gd_objvalue.m
x5
Globus server
Matlab
arcadiaobjfun.m
Geodise Demo 2
© Geodise Project 2003
• Application
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CFD (Adjoint & Hydra, Fluent & RSF)
CAD (ProE)
Optimisation
 Options toolkit
Computation/ Middleware
Ontology for
 Compute Toolkit
Engineering,
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Deliverables Summary
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Archive for Files
Archive for Metadata
Query & Retrieve
Acquisition
Ontology Services
Workflow Construction
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Advice Services
Ontology driven editing
Visualization
Traceability
OPTIMISATION
APPLICATION
SERVICE
PROVIDER
Intelligent
Application
Manager
Reliability
Security
QoS
Session
database
OPTIONS
System
• Knowledge
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Knowledge
repository
Globus & Condor
Using UK Level 2 Grid
SMS
Workflow in Matlab
Database
 XML Toolkit
 Database Toolkit
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GEODISE
PORTAL
Computation, &
Optimisation and
Design Search
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Engineer
CAD System
CADDS
IDEAS
ProE
CATIA, ICAD
Globus, Condor, OGSA
Optimisation
archive
COMPUTATION
Licenses
and code
Analysis
CFD
FEM
CEM
Parallel machines
Clusters
Internet Resource Providers
Pay-per-use
Design
archive
“Build
complex
things from
lots of simple
things”
Intelligent
Resource
Provider
The future of design optimisation
Design Optimisation needs integrated services
• Design improvements driven by CAD tools coupled to
advanced analysis codes (CFD, FEA, CEM etc.)
• On demand heterogeneous distributed computing and
data spread across companies and time zones.
• Optimization “for the masses” alongside manual search
as part of a problem solving environment.
• Knowledge based tools for advice and control of process
as well as product.
Geodise will provide grid-based seamless access to an
intelligent knowledge repository, a state-of-the-art
collection of optimisation and search tools, industrial
strength analysis codes, and distributed computing and
data resources