Presenter - Prof Simon J. Cox from the Computational Engineering Design Research Group (CED) with describe The use of Super Computers for Design optimisation. The CED is a centre of excellence for multi-disciplinary engineering simulation and design which combines together a range of analytical, computational, and experimental techniques.
2024.03.23 What do successful readers do - Sandy Millin for PARK.pptx
Ignite your...supercomputing 24 jul12_v2
1. Supercomputing & Design
Professor Simon Cox
Associate Dean, Enterprise
Faculty of Engineering and the Environment
24th July 2012
sjc@soton.ac.uk
2. Faculty of Engineering
Aeronautics, Astronautics
and the Environment
and Computational
Engineering
Prof Keane - Head of AACE Unit
Prof Simon Cox
Chair, Computational
Engineering and Design Group
Associate Dean, Enterprise
Faculty of Engineering and the Environment
3. Faculty of Engineering
and the Environment
AACE Overview
• Three research groups (aeronautics & flight mechanics, astronautics
and computational engineering & design). Head of Unit: Prof Keane
• 30 members of academic staff holding £13m of research grants.
• Three major research centres (Airbus Noise Technology Centre, R-R
UTC for Computational Engineering, Microsoft Institute for HPC).
• Nearly 80 PhD and EngD students under supervision (major partner in
DTC for Complexity).
• Linked to highly successful undergraduate and MSc teaching
programmes (100% student satisfaction in 2008 National Student
Survey for Aeronautics-Astronautics course; Royal Academy of
Engineering/Exxonmobil Excellence in Engineering Teaching Award).
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4. Faculty of Engineering
and the Environment
Design Exemplars
Computational Engineering
and Design Research Group
5. Faculty of Engineering
and the Environment
Rolls-Royce UTC in Computational
Engineering
PIs: Keane & Scanlan
5 Academic staff, 9 Research fellows, 11 EngD/PhDs,
£1.2m per annum external funding (EU/UK Govt/EPSRC)
Close integration with Rolls-Royce R&T objectives
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6. Typical Design Faculty of Engineering
Improvement Flowchart and the Environment
Initial
Design Background
Geometry Effort
Rule
Parametric Bases
Base
Geometry
Final CAD Uncertainty
Optimiser Models
Design
Meshing
Boundary
Search Conditions
Results
Extraction CFD CSM
Post
Costs 6
PI: Keane Processing
7. Faculty of Engineering
Multi-fidelity Optimization and the Environment
• 12 geometry variables
• 10 full car RANS
simulations 15h each
• 120 rear wing only RANS
simulations 1.5h each
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PI: Keane
8. Faculty of Engineering
and the Environment
Multi-scale modelling for nanotechnology
• Hans Fangohr
• Nanotechnology length
scales are close to the
atomic lattice spacings.
• Need to consider atomistic
nature of matter to be
accurate
• Need to combine atomistic
simulations with
continuum approaches to
make simulations feasible
9. Faculty of Engineering
Multi-physics modelling and the Environment
for nanotechnology
• Relevant properties
• magnetic
• thermodynamic
• conducting
• mechanic
• Optical
• Crucial for Nanodevices to
consider these together
PI: Fangohr
10. Faculty of Engineering
and the Environment
Design Optimisation of Nanodevices
• Magnetic storage, sensors,
spintronics
• Prototype fabrication
expensive vs “cheap”
Simulation
• Requires
– multi-scale simulations
– multi-physics simulations
• Computational Design
Optimisation highly
beneficial PI: Fangohr
11. Multi-objective, multi-disciplinary
design (coronary stents) Faculty of Engineering
and the Environment
Balloon expansion: structural integrity Haemodynamics: re-endothelialisation
Optimal design: patient specific
Flexibility: conformability
Systematic design search and optimisation:
Recover and maintain healthy blood flow
Drug elution:
control of
restenosis
Stress: minimise
artery wall damage
PI: Bressloff
12. Future cath-lab Faculty of Engineering
and the Environment
(1) Cath Lab
(2) Stent Design Tool
(a virtual cath. lab)
(4) Manufacture
(3) The Cloud
(1) http://www.everydaychampions.org/hospital/cardiac_cath_lab.php
(2) Stent design tool (N.W.Bressloff)
PI: Bressloff
(3) http://rohitsaxenawrites.wordpress.com/author/rohitklar/
(4) http://www.raydiance.com/our-technology
13. Airbus Noise Technology Centre Faculty of Engineering
(AFM Group) and the Environment
PI: Zhang
• First Airbus-University
Technology Centre in the World
• Opened November 2008
• Focussing on future aircraft
technologies for noise reduction
• Fifteen academic staff and
research students
• Computation and experiment
14. Sample ANTC projects Faculty of Engineering
(AFM Group) and the Environment
LES of noise generated by interaction of aircraft engine with
wing (Zhang/Hu)
Aircraft high-lift device noise generation and control
(Zhang/Hu)
High-speed train aeroacoustics (Hu & Thompson (ISVR) )
PI: Zhang
15. Faculty of Engineering
and the Environment
Under the Hood
… tools, technologies and
platforms
16. Microsoft Institute for HPC Faculty of Engineering
and the Environment
• PI: Cox
• Workflow + HPC + Data
• Centre for Fluid Mechanics Simulation (CFMS)
• Flight simulation HPC
• μVis – X-Ray CT Centre workflow and data
management
• Cloud & Mobile computing – space situational
awareness
• Institutional and Large Scale Data Management
– Materials
– Chemistry
– Medicine
– Archaeology
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– Engineering HPC Data
17. Faculty of Engineering
and the Environment
Engineering Design - from the Desktop to the Enterprise
• Engineering design demands integration
of complex systems to set-up, execute,
monitor, post-process, review, and store
computational processes, data and
knowledge
• Research to tackle new challenges
• Applied and customised commodity off-
the-shelf Microsoft tools, technologies
and platforms Microsoft Institute for HPC @
• Collaboration with Rolls-Royce, Airbus, Southampton
BAE Systems, MBDA, Microsoft, others: “We demonstrate why, where, and how current and
20 Million Euro UK TSB (DTI) future Microsoft tools and technologies can make the
programme engineering design process faster, cheaper and better.”
Launched by Bill Gates, Nov 2005
Initial Research 1998
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18. TAGtivity Wiki BAE Systems Rolls-Royce Airbus
Software + Services Robust, reliable and
Organizing thoughts Semantic structure, Orchestration of gas
for connecting scalable data
and reference search and turbine design
engineers and experts intensive
material... experience reasoning calculations
to users and data collaboration
Concept Computation Data
Individual review MBDA
Data Driven Product Small group review
Lifecycle
Management
Corporate review
Data sources
Wiki database Task database
Workflow templates
Tagtivity database Workflow database Conversation database Sharepoint database
Workflow tracking
Filesystem Knowledge database Workflow tracking Active Directory
Filesystem
Corporate database Simulation database
Technology
Windows Server 2008 Windows Workflow Sharepoint Server
Microsoft Office 2007 MediaWiki Hyper-V RC0 Foundation Active Directory
Windows HPC Server 2008 HP-UX (Interop)
SQL Server 2005/ 2008 SQL Server 2008 Windows CCS 2003
Beta2 Windows
Windows Presentation D2R Server SQL Server 2008 CTP6
Linux (Interop)
Foundation; Matlab ARQ/SPARQL Visual Studio 2005 Communication
Office Communication
SQL Server 2005 Foundation
Server
19. Exploiting cloud computing
for algorithm development
N. O’Brien, E. Hart, S. Johnston, K. Djidjeli, S. J. Cox
sjc@soton.ac .uk and Neil.OBrien@soton.ac.uk
20. Case study: photonic crystals
• Nanostructures that affect propagation of light
Morpho butterfly Wing cross-section
Man-made example
Opal Peacock’s magnificent Man-made example 20
tail
21. Strengths of cloud computing
• Algorithm development
– Time-, data-, compute-intensive
• Data dissemination
– Sharing large input or output data sets
• Burst capability
– Testing/parameter sweeping new revisions
• Super-scalability
– Apply algorithm to big problems
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22. Case study: photonic crystal modelling
• Photonic crystals are periodic dielectric structures, we
assume periodicity in 𝑥 and 𝑦 directions and infinite extent
in 𝑧
22
23. Maxwell’s Equations
• Maxwell’s equations give the allowed modes of propagation
for TM, TE modes, after applying standard simplifications
1
− ∆𝜓 = 𝜆𝜓
𝜀
1
−𝛻 ⋅ 𝛻𝜓 = 𝜆𝜓
𝜀
• When the dielectric 𝜀 varies, no analytical solution exists
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24. Algorithms for Photonic Crystal Modelling
• Plane Wave Methods Gibbs Phenomenon
– Scale poorly + Gibbs Phenomenon
• Finite Difference Method
– Simple to code
FD Stencil
– Requires fine mesh for accuracy
» Large and Sparse Matrices
• Finite Element Method
– Large amounts of code
FE Mesh
– Requires fine (complicated) mesh for accuracy
» Large and Sparse Matrices
• Meshless Method
– Simple to code & data parallel Radial Basis
Function
– Improved geometry handling
» Small and Dense Matrices
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25. Meshless Method Formulation
• Maxwell’s equations for 2D problem
TE . i k . i k u u
1 1
TM
1
1
i k i k u u
.
• Simplifying, the 2D elliptic Helmholtz equation takes the form:
• Introducing the following notation:
• Then use the Standard RBF approximation of a function to write the Helmholtz
equation as a generalized eigenvalue problem:
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26. Cloud application
• Verification by checking a high-resolution run against
existing software
– Developer workstation free to run other tasks
– Institutional cluster queue time avoided
• Parameter sweeps through high dimensional spaces
– Wall-time bounded below by (provisioning time +
longest simulation time)
• Application of the new algorithm to novel shapes, bandgap
engineering and optimisation
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28. Concluding remarks
• Cloud paradigm demonstrated
– Accelerated development cycle
– Parts of a traditionally-serial workflow ran on Windows
Azure in parallel
– Enhanced collaboration opportunities
– Modularity of design
• New meshless algorithm demonstrated
– Solves Maxwell equations successfully
– Applicable for bio-inspired photonic materials
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29. Azure+Windows Phone 7
Atmospheric Science
Professor Simon Cox and Dr Steven Johnston with:
ASTRA: Dr András Sóbester, Prof Jim Scanlan, and Neil O’Brien
Microsoft Institute for High Performance Computing @ Southampton
sjc@soton.ac.uk & sjj698@zepler.org
30. Atmospheric Science
Through Robotic Aircraft
• Monitoring the atmosphere
– Weather, pollution
– Volcanic ash…
• Low cost metrological balloons
– High altitude
• Instrument retrieval using WP7 +
GPS
• Predicting the landing location
with Azure
31. ASTRA architecture
Meteorological Windows Logging and
weather balloon
Azure data storage
Blob
storage
Windows Device
Phone 7 Windows registration and
Phone 7 GPS logging
GSM notifications notifications
Internet
Flight University of
Followers prediction Southampton
algorithms
Tracker using
and trajectory
Bing maps
updates
on WP7
32. ASTRA flights using
WP7 and Windows Azure
• ASTRA 7
– 18,237 meters (59,832 feet)
– Top speed 145 km/h
(90mph)
– 1hr 16’
• ASTRA 8
– 21,600 meters (71,000 feet)
– Top speed 82 km/h (51mph)
– 2hr 30’