Transcript Computational Science for Energy

Computational Science for Energy
Wanda Andreoni
Centre de Calcul Atomique et Moleculaire (CECAM)
Ecole Polytechnique Federale – Lausanne
www.cecam.org
Trieste, May 31 2010
Computational Science:
main domains of application
New powerful algorithms, better software (& hardware)
are needed…
 to help advancement of knowledge (basic science)
 to design new solutions: from materials &
processes to device architectures
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to establish an intelligent management of power
generation and distribution systems
to monitor/control/forecast “green” operations.
Outline
Materials science and chemistry
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Solar Energy
Hydrogen and Water
Novel batteries
Nuclear
CO2 capture and sequestration
Modeling and simulations : other applications
The Sun
as Source of Energy
MODELLING & SIMULATIONS needed
(i) to improve on and design new materials;
(ii) to monitor, improve on and guide materials processing;
(iii) to optimize system-device integration & architecture;
(iv) to optimize performance of PV power supply systems
(e.g. sizing).
The Science of Photovoltaics
Class
Issues
(examples)
Status
of modeling
Needs
for simulations
a-Si
defects
degradation
rich literature
large-size long-time
CIGS/CdTe
defects, doping..
structure growth
Ab initio
improved algorithms
HPC
Organic
Hybrid
fundamental mechs
model calculations
exciton & carriers
and theories
generation & migration
degradation (polymer)
QD
Multi Exciton Generation
physical insights
debate on mechanism
new algorithms
?
Combination with experiment: crucial for model development.
a-Si based solar cells
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Advantages
mature technology
material is abundant
low cost
Disadvantages
- less efficient than Xtal
- degrades easily under
illumination (StaeblerWronski effect)
a-Si:H
Why are experiments not sufficient?
Hydrogen often eludes experiments; fast dynamics
What can simulations provide?
Proofs of models; new possible scenarios; dynamics
What type of simulations?
Classical molecular dynamics, electronic structure calculations,
and synergy (Car-Parrinello method and alike)
Potentials? Sampling?
CdTe- and CIGS-based PV
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Advantages
relatively cheap
thin-film technology
Disadvantages
complex structure
scarcity of core elements
Issues
Defects & impurities: generation,
diffusion
Nature of interfaces (dependence on
deposition method)
Carrier transport also through interfaces
Role of Grain Boundaries
What can atomistic simulations do more?
More accurate prediction of energy gaps, defect levels
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Study of interfaces is lacking
structure and composition
inter-diffusion
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Study of grain boundaries
formation and role
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Study of the effect of temperature & stress conditions
Models must be of relatively large sizes
(at least 1000 atoms)
Methods:
Combination of classical MD and ab initio simulations
Difficulty to obtain reliable interatomic potentials
Efficient intelligent sampling of atomic configurations (REMD;
MetaDynamics etc)
Accurate and efficient algorithms for high-performance computing
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PV Materials Processing
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Modeling is complicated ; it
may require multiscale (from
atomistic to continuum) but
also sophisticated optimization
procedures.
Need for robust algorithms
development (simulations and
analysis)
System:
Integration & Design
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New design problems for PV require the
combination of tools and methodologies from
electronic and photonic technologies.
Maxwell equations & models of the electronic behavior
(carrier generation, collection and transport) –
Technology-Computer-Aided-Design
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Algorithm development required
for integration of different methodologies for
hierarchical optimization (multi-parameter)
Photocatalysis I
for hydrogen production via water splitting
(also for air and water purification; surface
self-cleaning and self-sterilizing…)
Typical catalyst: TiO2
Challenges & need for simulations
• Catalysts in the visible
• Avoid modification of the “catalyst”
• Avoid use of sacrificial reductants or oxidants
(see Kohl et al. Science 324, 74 (09))
Photocatalysis II
Do we really understand what happens at the
water/TiO2 interface?
“wet electrons”
K. Onda et al., Science 308, 1154 (05)
Innovative Batteries
Li-air aprotic batteries
Oxygen through an air cathode:
an “unlimited” cathode reactant !
Non-aqueous electrolyte avoids corrosion
• Light, small, cheap
• No self-discharge
• Long-time storage
Li/Air: Research Questions & Topics
Computational Models and Tools
Battery research combines the three most challenging aspects of computational
physics: ** non-equilibrium, multiphase and multiscale (in space and in time) **
=> A complete model may require 100’s of Petaflops (Exascale) computing.
Nuclear power: safety issues
Examples
Reactors : materials under extreme conditions; aging
Fuel cycle : recycling of minor actinides
Nuclear waste : safe storage
Structural materials : Understanding interaction of dislocations
with irradiation defects (e.g. the microstructure) is
necessary to predict steel hardening under irradiation.
Fuels : Understanding the chemistry of actinides is vital to
optimize actinide extraction and complexation
Reactor materials aging : Corrosion, fatigue, fracture…
Hierarchical multi-scale simulation of nuclear fuel
MD simulation of
radiation damage
Materials science engineering scale
linkage
Atomistically-informed
phase-field approach
for void nucleation and
growth & fission-gas
behavior
Atomic/electronic level
(Newton’s laws)
Radiation damage,
micro-structural mechanisms
and materials parameters
Continuum level
Continuum mechanics,
PDEs, constitutive laws
‘Mesoscale’
(viscous force laws)
Effect of microstructural processes (fission gas, voids,
cracks, diffusion, …) on thermo-mechanical properties
CO2 : capture & sequestration (CCS)
Challenges (examples)
I. Find new solvents and additives for wet CO2 capture by scrubbing.
Amine absorption not amenable to large scale deployment
in power plants e.g. high rate of degradation due to oxidation
and salt formation; high energy penalty for amine regeneration.
II. Accelerate mineral carbonation for permanent CO2 fixation as carbonate.
Increase the reaction rate is crucial to obtain an industrial viable process.
E.g. aqueous mineral carbonation: accelerate the rate of CO2 hydration and
of silicate dissolution
CCS: a multi-scale multi-physics problem
Basic and general needs for C.S.
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Higher accuracy
Electron excitation spectrum
Defect energies
Rates of chemical reactions
Rates for diffusion in complex systems…
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More realistic models of complex systems
Multi-scale methodologies
High Performance Computing often crucial !
Close collaboration with experimental research
Advanced modeling & simulations for …
future technologies of power generation & distribution (e.g. smart GRID)
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Powerful and novel algorithms
to optimize planning, to characterize behaviour & forecast response
(short- and long-term) under various scenarios (multiple temporal and
spatial scales).
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Better software and visualization capabilities
to transform grid management to real-time automated state.
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New demands to technology will require the aid of computer-aided design.
Examples: for large-scale energy storage and low-loss transmission.
Designing and simulating a network so that it works in real time
represents a grand computational challenge on an
unprecedented scale.
PV-based Power Supply Systems
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PV Stand-alone, Grid-connected or Hybrid
Note: HPV includes other RE sources (typically wind, hydrogen, diesel)
Need: Optimize system engineering
Modeling of single components
Control and coordination
System sizing
Prediction of maximum-power point
Methods:
Conventional approaches: empiric, analytic, numeric, statistical
Innovative approaches: Artificial Intelligence methods (ANN, GA, FL…)
ANN=artificial neural network
GA=genetic algorithm
FL=fuzzy logic
CECAM and C.S. for Energy
Our activities
 First workshop on “Critical materials
issues in inorganic photovoltaics”
W.A., Claudia Felser,Tanja Shilling, June 2008
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Brainstorm meeting on “Computational
Science for Energy ”
W.A. and Claude Guet, Divonne, May 09
CECAM Workshops
on ENERGY & ENVIRONMENT (2010)
2010
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Materials modelling in nuclear energy environments: state of the art
and beyond
M. Samaras, R. Stoller, R. Schaeublin, M. Bertolus, April 26-29 (Zurich)
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Gas separation & gas storage using porous materials
L. Valenzano, C.O. Arean, C.M. Zicovich-Wilson, May 17-19 (Lausanne)
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Electronic-structure challenges in materials modeling for energy
applications
N. Marzari and A. Rubio, June 1-4 (Lausanne)
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Ab initio electrochemistry
M. Sprik and M. Koper , July 12-14 (Lausanne)
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Actinides: Correlated electrons and nuclear materials
L. Petit, B. Amadon, S. Miller, June 14-16 (Manchester)
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Computational carbon capture
B. Smit, S. Calero, T.J.H. Vlugt, July 26-28 (Lausanne)
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Simulations and Experiments on Materials for Hydrogen Storage
S. Meloni, S. Bonella, G. Schenter, October 11-14 (Dublin)
THANK YOU
FOR
YOUR ATTENTION
Knowledge advancement and design of new solutions
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There is a strong need for advanced materials, novel
processing routes and innovative devices in the generation and
exploitation of alternative energies. Control and design imply
substantial progress in understanding. Simulations
(computational materials science and chemistry) using accurate
methodologies and HPC are often invoked as critical auxiliary
tools to experiment.
Examples: increase lifetime of nuclear reactors; tailor materials
properties for better performance, guide materials processing
to lower cost & help system-level integration.
New methods for carbon sequestration rely on understanding that only HPC
simulations can provide
 Use of bio-fuels relies on the understanding of bio-energy conversion
mechanisms (plant and microbial processes) for which HPC simulations
are mandatory
 Coupling climate and environmental modeling is a must to make a step
forward.
Free Energy Diagram of Metal-Oxide
catalyzed Recharge
Goal: Oxygen Gas + Li Metal
O + 2(Li+ + e)
2
O2Li
I
+ (Li+ + e)
M-O-M-O- 2 e U0
G
- e U0
Start
Li2O2 (s)
 = U – U0
Li+ + LiO2
Time
Computational Example 1 –
Redox Reaction on Cathode
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Realistic, ab-initio
modeling of Oxygen
Redox Reaction in
aprotic environments
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Ab-inito calculation of possible reaction pathway for the
oxygen reduction reaction on a catalytic surface. By
Manos Mavrikakis, U.Wisconsin et.al., performed at
NCSA and SDSC TeraGrid systems. (Fuel Cell)
the challenge is the
reverse (recharge)
reaction
Realistic, ab-initio
modeling of Oxygen
Redox Reaction in
aqueous environments
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similar to fuel-cells, but
Computational Example 2 –
 Realistic
Interfaces and Transport
modeling of
electrolyte/electr
ode interfaces
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Purpose
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Combined Quantum-mechanical and molecular
Mechanical Model of electrolyte/electrode interface
Model by T.Jacob, Univ. Ulm
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Model the
solvent and ion
transport
mechanisms
which is a very
Experiments
MetaDynamics: A. Laio and F.L. Gervasio, Rep. Prog. Phys. 71 (2008)
Materials Science