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Nano5. N ew a rchitectural design for no vel experimental domains. Maria Grazia Pia INFN Genova INFN Commissione Nazionale V 17 settembre 2008. R&D on simulation methods, technology and architectural design for new experimental domains. Courtesy CMS Collaboration. - PowerPoint PPT Presentation
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Maria Grazia Pia, INFN Genova
NNewew architectural rchitectural design fordesign for novel vel
experimental domainsexperimental domains
R&D on simulation methods, technology and architectural design
for new experimental domains
Maria Grazia Pia INFN Genova
INFN Commissione Nazionale V17 settembre 2008
Nano5Nano5
Maria Grazia Pia, INFN GenovaCourtesy Borexino
Courtesy H. Araujo and A. Howard, IC London
ZEPLIN III
Courtesy CMS Collaboration
Courtesy ATLAS Collaboration
Courtesy GATE Collaboration
Courtesy R. Nartallo et al.,ESA
Widely used also in Space science and astrophysics Medical physics, nuclear medicine Radiation protection Accelerator physics Humanitarian projects, security etc.Technology transfer to industry, hospitals…
Born from the requirements of large scale HEP experiments
Most cited Most cited “Nuclear Science “Nuclear Science and Technology” and Technology”
publication!publication!(>132000 papers)
3rd most cited INFN paper
“Modern classic”
S. Agostinelli et al.GEANT4 - a simulation toolkit
NIM A 506 (2003) 250-303
Maria Grazia Pia, INFN Genova
BackgroundBackgroundGeant4 R&D phase: RD44 1994-1998 (Geant4 0: 15 December 1998) Designed and built Geant4 New software technology GEANT 3 experience + some new ideas
Foundation of the current Geant4: dates back to the mid ’90s Requirements for core capabilities Software technology
Evolution: 1998-2008 Consolidation, validation Support to the experimental community Refinement of existing capabilities Extension of physics models, geometry tools etc. Same core capabilities and technology as in the mid ’90s
1994mid of LEP era
GEANT 3 successfully used in
many experiments
Collected from the experimental community
Object Oriented methods introduced in HEP
Maria Grazia Pia, INFN Genova
The world changes…The world changes…
New experimental domainsNew requirementsNew technology
Start SPS 1976
W and Z observed 1983
Start LEP 1989
LHCSuperLHC?
astrophysicsnuclear power
medical physicsradiobiology
nanotechnologydetectors…
hardware, software, OShardware, software, OS
WWWGrid1998
Tevatron
new R&D
Maria Grazia Pia, INFN Genova
R&DR&DMotivated by scientific interests within INFN scope
Response to current limitations of Geant4 of all major Monte Carlo systems, not only Geant4
Address concrete experimental use cases by going to the very core of Monte Carlo methodscore of Monte Carlo methods
Exploit new software technologyin response to experimental issues
Build on existing experienceDomain knowledge: simulation in multi-disciplinary research
Software technology expertise
R&Dlaunched LowE Electromagnetic Physics in 1998new simulation capabilities and application domains for Geant4
Maria Grazia Pia, INFN Genova
Topics of researchTopics of researchR&D on
complementary, co-working transport methods
Condensed-random-walkCondensed-random-walk schemeDiscreteDiscrete scheme
Monte CarloMonte Carlo methodDeterministicDeterministic methods
Nanotechnology detectorsRadiation effects on components
RadiobiologyPlasma physics
Material analysisetc.
Nuclear power plantsRadiotherapy
Homeland securityetc.
Side topics (instrumental to the main objectives)
Physics configurability
Concerns(scattered and tangled)
Built-in physics V&V-ability
Maria Grazia Pia, INFN Genova
Condensed-random-Condensed-random-walkwalk
DiscreteDiscreteCondensed-random-walk approximationCondensed-random-walk approximation all general-purpose Monte Carlo codes (EGS, FLUKA, GEANT 3, Geant4, MCNP)
charged particle tracks divided into many steps, several interactions occur in a step
one energy loss and one deflection are calculated for each step further simplification of Continuous Slowing Down Approximation: energy loss rate
determined by stopping power
collisions are treated as binary processes target electrons free and at rest (or binding accounted only in an approximated way)
adequate as long as the discrete energy loss events are » electronic binding energies
Discrete simulationDiscrete simulation all collisions are explicitly simulated as single-scattering interactions prohibitively time-consuming on large scale for charged particles (infrared divergence)
many “track structure” codes documented in literature single-purpose, not public, maintenance not ensured, lack general functionality
SimulatioSimulationn
Maria Grazia Pia, INFN Genova
Two worlds…Two worlds…Condensed-random-walk OROR “discrete” régime Characterizing choice in a Monte Carlo system Limited exception: Penelope (switch to elastic scattering near boundaries)
Subtle consequences e.g. X-ray fluorescence emission (PIXE) by impact ionisation has a dependence on
secondary production cut introduced to handle infrared divergence! can affect macroscopic applications: material analysis, precise dosimetry etc.
ATLAS
How do you estimate radiation effects on componentseffects on components exposed to LHC + detector environmentLHC + detector environment?
How do you link dosimetrydosimetry to radiation biologyradiation biology?
What does it mean in practice?
And what about the plasmaplasma facing materials in a fusion reactorfusion reactor?And nanotechnology-based detectors for HEP?
And tracking in a gaseous detector?
RADMON
Maria Grazia Pia, INFN Genova
R&D on co-working CRW-R&D on co-working CRW-discretediscrete
Scientific motivation Address large-scale and nano-scale simulation in the same environment
Realistic model of the whole system
Accurate evaluation of radiation effects in small scale structures
Objective
Seamless transition of simulation régime
Capability of simulating complex multi-scale systems
Conceptual and software design challenges
Physics process adaptation to environment
Embedding “mutability” in Monte Carlo physics entitiesDifficult …otherwise it would have already been done
Maria Grazia Pia, INFN Genova
Re-think the design of Geant4 physics domain Kernel: how processes interact with tracking Processes: mutability “in the guts” Particles: they also become mutable entities
e.g. ions (beyond effective charge scaling)
Multiple scattering, its relation to energy loss
New domain design exploiting new technology In response to physics requirementsphysics requirements Configurability + performance Side-by-side with conventional OO methods
Detangle the current spaghetti first Problem domain analysis Rigorous domain decomposition
How?How?
Lot of w
ork
Unavoidable
Maria Grazia Pia, INFN Genova
Generic programmingGeneric programmingRelatively new technology Aka “programming with templates”
Aka “modern design”: post-Alexandrescu’s book era
C++ is capable of a Turing machine at two levels Exploit both
Mix and match
Further step: generative programming
Extreme configurability
Bind configurability at compile time Performance gain relevant to nano-scale simulation
Memory consumption
“the hardest of hardcore template programming”
Maria Grazia Pia, INFN Genova
Aspect Oriented ProgrammingAspect Oriented Programming
Scattered concerns (+ tangled concerns)
e.g. atomic relaxation occurring in photoelectric effect (discrete), ionisation (continuous-discrete), Compton scattering, radioactive decay/photo-evaporation (Geant4 hadronic package)
R&D on Aspect Oriented Programming Secondary priority: use only in support to prime objectives
Not so well supported in C++ as, for instance, in Java
Same design concept also suitable to native physics “testability”
V&V is today’s greatest concern of Geant4! Geant4 does not have a test framework, nor a design supporting test processes
V&V left to individual efforts
Maria Grazia Pia, INFN Genova
Software Software ProcessProcess
Risk Risk mitigation strategymitigation strategyNo perturbation to a system currently in production in LHC experiments and many other projects
Develop in parallel to Geant4 kernelIterative-incremental process: to mitigate “waterfall” riskFrequent integration-releases for testing and application feedbackTransition to new kernel for production use when mature
Freedom to explore different solutions Difficult problem Iterations and intermediate benchmarks to identify optimal design Sound confirmation from fully functional prototypes
UP-basedTailored to the project(s)
Mapped to ISO 15504 level 3 at least
R&D at the very heart of Monte Carlo concepts and Geant4 architecture Not to be taken easily!
Maria Grazia Pia, INFN Genova
PrototypesPrototypes For risk mitigation risk mitigation
PTB Monte Carlo models and data >30 years’ experience!
Experimental set-up: nanodosimeter Experimental validation
Collaboration: PTB+Hamburg, LLU
“Conventional” PIXE Elemental analysis
High-energy PIXE Next generation X-ray astrophysics
Relevant to precision dosimetry too
Collaboration with MPI
Would the proposed technology be a suitable solution?Can the software address a realistic experimental use case?
Does it work at a realistically large scale?
Can it handle systems at macroscopic scalemacroscopic scale?
Fully functional nano-prototype
Fully functional PIXE-prototype
New design affecting Monte Carlo (Geant4) core
Figure: G. Weidenspointner et al., Nature
Figure courtesy of LLU
Maria Grazia Pia, INFN Genova
Deterministic and Monte Carlo Deterministic and Monte Carlo simulationsimulation
Deterministic methods are widely used in Reactor physics calculations
Based on the concept of “neutron flux”
Medical physics Treatment planning
Reactors: series of codes specialized in specific functions Cumbersome…
Monte Carlo intrinsically more accurate Model geometry and physics accurately
New trends Monte Carlo group constant generation for deterministic codes
Conventional deterministic codes not well-suited to complex assembly designs, next generation reactors, advanced MOX technology etc.
Monte Carlo calculations
Figure credit: A. Leppanen
Maria Grazia Pia, INFN Genova
Simulation for nuclear power Simulation for nuclear power studiesstudies
New generation reactors
ITER? of interest to INFN
INFN expertise in simulation methods and tools is useful to approach this new research domain …but direct expertise in nuclear power plant simulation still to be built at INFN
Geant4 not widely used in nuclear power studies yet MCNP is the “standard” Monte Carlo code… for standard problems
Deterministic codes play a major role in reactor calculations Monte Carlo methods are prohibitively time consuming for some problems
MCNP is developed and maintained at LANL INFN priorities are not necessarily LANL priorities…
R&D for nuclear power simulation with Geant4
Maria Grazia Pia, INFN Genova
Co-working Monte Carlo - deterministic Co-working Monte Carlo - deterministic methodsmethods
One calculation environment
Use either transport method where it is best suited Profit of set-up modelling facilities developed for general-purpose
Monte Carlo simulation
Complex design problem in a new application domain R&D needed Plan to strengthen collaboration with ANS
Design solutions to be explored in Geant4 Parallel worlds
Multiple geometries in the same simulation environment
Concept of “mutability” of transport
Maria Grazia Pia, INFN Genova
Staged approachStaged approachDue to the complexity of the problemAnd need to build new expertise not currently present at INFN No “tradition” in deterministic transport methods nor in reactor simulation methods
1st phase Deterministic methods to calculate ingredients for biasing technique Produce concrete deliverable Build up expertise
Project: use discrete ordinates adjoint function for automated variance reduction of Monte Carlo calculations
Concrete deliverable Similar problem addressed with MCNP
Evaluation benchmarks of Geant4 for nuclear power studies
2nd phaseCo-operation of the two approaches in the same environment
Maria Grazia Pia, INFN Genova
Main deliverablesMain deliverables
CRW-discrete simulation Work PackageNano-prototype Requirements (or use case model)
Design model
Implementation (PTB-like models)
Performance and physics benchmarks
PIXE-prototype Requirements (or use case model)
Design model
Implementation
Validation
Deterministic-Monte Carlo methods Work Package Package for variance reduction calculation through deterministic methods
Benchmarks of Geant4 applicability to nuclear power simulation
Include new Monte Carlo
kernel
Geant4 Nano5 Geant5…
Maria Grazia Pia, INFN Genova
MilestonesMilestones
CRW-discrete Problem domain analysis, design model, “detangled” prototype: July 2009
PIXE prototype: December 2009
PTB Monte Carlo reengineered: July 2010
Nanodosimeter prototype functional: end 2010
Nanodosimeter prototype validation: mid 2011
Transition phase: end 2011
Deterministic-Monte Carlo methods Use case model & analysis: end 2009
Discrete ordinates adjoint function calculation: end 2010
Variance reduction application: 2011
Geant4 evaluation for nuclear power studies: end 2009
Maria Grazia Pia, INFN Genova
Book on Book on
Simulation Techniques in Simulation Techniques in PhysicsPhysics
Invito da primaria casa editrice
a pubblicare un libro su
tecniche di simulazione in fisicatecniche di simulazione in fisica
NANO5 scaturisce da una lunga esperienza di simulazione…
Maria Grazia Pia, INFN Genova
AcknowledgmentAcknowledgmentThanks to:
T. Evans (ORNL)
E. Gargioni (PTB)
S. Giani (CERN), RD44 Spokesman and Project Leader
B. Grosswendt (PTB)
L. Moneta (CERN)
A. Pfeiffer (CERN)
R. Schulte (LLU)
E. Smith (PNL)
G. Weidenspointner (MPI)
A. Wroe (LLU)
A. Zoglauer (LBL)