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Berkeley Lab Receives $4 Million in Recovery Act Funding to Develop Computational Methods for Energy-Related Research

June 18, 2010

Contact: John Hules, JAHules@lbl.gov , 510-486-6008

A sampling of pore structures in zeolites, a type of microporous mineral. One of

A sampling of pore structures in zeolites, a type of microporous mineral. One of the Berkeley Lab SciDAC-e projects will automate the computational screening of 2.5 million theoretically possible zeolite structures to find materials that can economically capture CO2 at its sources.

Researchers in the Computational Research Division (CRD) of the U.S. Department of Energy's (DOE's) Lawrence Berkeley National Laboratory (Berkeley Lab) have received more than $4 million in funding for six projects that will help develop computational methods to answer some of the nation's most pressing questions regarding energy efficiency, climate stabilization, and next-generation, carbon-neutral energy sources.

The awards were made by the Office of Advanced Scientific Computing Research (ASCR) in the DOE Office of Science under the SciDAC-e program (Scientific Discovery through Advanced Computing–Energy). Using funds from the American Recovery and Reinvestment Act, SciDAC-e specifically targets projects that can accelerate discoveries in energy-related science by improving and developing new scientific algorithms and software.

All of the projects involve collaborations with one or more of DOE's Energy Frontier Research Centers (EFRCs). Three of the six projects address research in carbon capture and sequestration, three address solar energy, and one also addresses combustion efficiency. Carbon capture and sequestration—injecting carbon dioxide (CO2) into the Earth's subsurface instead of releasing it into the atmosphere—is seen as an important strategy to help stop global warming.

However, the mathematical methods and computational tools developed here will also have applications in many other scientific domains, such as the search for improved catalysts for hydrogen fuel cells and storage. CRD researchers are Lead Principal Investigators in five of the six projects.

"These projects are an important addition to Berkeley Lab's Carbon Cycle 2.0 portfolio," said Horst Simon, Associate Laboratory Director for Computing Sciences and CRD Division Director. "We are doing significant work in computing for energy."

Carbon Cycle 2.0 is a Berkeley Lab initiative to provide the science needed to restore Earth's carbon balance by integrating the Lab's diverse research activities and delivering creative solutions toward a carbon-neutral energy future.

Here are brief descriptions of Berkeley Lab's six SciDAC-e projects:

Advanced Simulation of Subsurface Flow and Transport at the Pore Scale

Energy Application: Carbon capture and sequestration
Principal Investigator: Phillip Colella, Berkeley Lab
Co-Investigators: David Trebotich, Gregory Miller, Brian Van Straalen, Hans Johansen, Robert Crockett, Sergi Molins Rafa, and Carl Steefel, Berkeley Lab
SciDAC-e Award: $1,224,986

This project, a collaboration between the SciDAC Applied Partial Differential Equations Center for Enabling Technology (APDEC) and the Energy Frontier Research Center for Nanoscale Control of Geologic CO2 (NCGC), will extend and further develop the algorithmic and software infrastructure tools, including CHOMBO, to enable NCGC's goal of modeling molecular-to pore-scale processes in geologic systems. Specifically, the team will develop algorithms and software to model multiphase, reacting flow of CO2 and water in a complex heterogeneous medium, with modification of microscale pore structures by mineral dissolution and precipitation.

Visualization and Analysis for Nanoscale Control of Geologic CO2

Energy Application: Carbon capture and sequestration
Principal Investigator: E. Wes Bethel, Berkeley Lab
Co-Investigators: Gunther Weber, Daniela Ushizima, and Janet Jacobsen, Berkeley Lab
SciDAC-e Award: $427,000

This collaboration between the SciDAC Visualization and Analytics Center for Enabling Technology (VACET) and the Energy Frontier Research Center for Nanoscale Control of Geologic CO2 (NCGC) will develop visualization tools to help NCGC researchers understand fluid-fluid and fluid-rock interactions that occur when CO2 is injected into geologic formations for carbon sequestration. The tools developed in this project will accelerate data processing and provide new capabilities for analysis of simulations, thus allowing NCGC researchers to run more experiments and effectively target new experiments.

Accelerating Discovery of New Materials for Energy-Related Gas Separations through PDE-Based Mathematical and Geometrical Algorithms and Advanced Visualization Tools

Energy Application: Carbon capture and sequestration
Principal Investigator: E. Wes Bethel, Berkeley Lab
Co-Investigators: Maciej Haranczyk, Prabhat, and James Sethian, Berkeley Lab
SciDAC-e Award: $383,000

The SciDAC VACET center will collaborate with the EFRC for Gas Separations Relevant to Clean Energy Technologies to develop state-of-the-art algorithms for analyzing and screening chemical systems, using a combination of advanced partial differential equation (PDE) algorithms to detect and probe geometric structures, and visualization techniques to track the motion of chemical probes through complex structures. Moving these algorithms to high performance computing platforms and deploying advanced data analysis tools will allow the researchers to automatically screen millions of pore structures without human intervention. The most promising structures can then be synthesized and tested.

Autotuning Large Computational Chemistry Codes

Energy Applications: Combustion efficiency and solar energy
Principal Investigators: David H. Bailey, Berkeley Lab; Jack Dongarra and Shirley Moore, University of Tennessee, Knoxville
Co-Investigators: Samuel Williams, Berkeley Lab; Mark Gordon and Theresa Windus, Ames Laboratory; Curtis Janssen and Joseph Kenny, Sandia National Laboratory; Allen D. Malony and Sameer Shende, University of Oregon
SciDAC-e Award: $1,100,000

Computational chemistry codes such as GAMESS, NWChem, and MPQC are among the most widely used in the DOE research community and beyond, with applications in solar energy cell design, combustion efficiency, materials science, nanoscience, nanoelectronics and related fields. Tuning these codes by hand to make them run efficiently on a particular computer usually requires a high level of expertise and lots of time.

One promising way to speed up the process is autotuning—the development of tools and techniques that can automatically generate and test variations of a scientific code, resulting in a tuned code that achieves very high performance on a given system. This project will implement state-of-the-art performance analysis and autotuning techniques to accelerate some key computational chemistry applications, notably a linear scaling multi-reference configuration interaction (MRCI) module in the GAMESS code. One particular application targeted will be a set of large-scale simulations of the large hydrocarbons and sulfur-containing hydrocarbons that are components of diesel fuel. Collaborators will include two EFRCs: the Combustion Energy Frontier Research Center (CEFRC) and the Argonne-Northwestern Solar Energy Research Center (ANSER).

Large-Scale Eigenvalue Calculations in the Study of Electron Excitation for Photovoltaic Materials

Energy Application: Solar energy
Principal Investigator: Esmond Ng, Berkeley Lab
Co-Investigators: Andrew Canning, Lin-Wang Wang, and Chao Yang, Berkeley Lab
SciDAC-e Award: $702,000

This project is a collaboration between the SciDAC TOPS (Towards Optimal Petascale Simulations) center and two EFRCs: the Center for Inverse Design at the National Renewable Energy Laboratory (NREL), and the Molecularly Assembled Material Architectures for Solar Energy Production, Storage, and Carbon Capture EFRC at the University of California, Los Angeles (UCLA).

This project will focus on developing and deploying state-of-the-art solvers for tackling specific large-scale eigenvalue problems that arise in the study of electron excitation for photovoltaic materials. The project aims to provide algorithmic and performance improvements to codes used by the EFRCs to allow more efficient use of modern multicore supercomputers.

Enhancing Productivity of Materials Discovery Computations for Solar Fuels and Next Generation Photovoltaics

Energy Application: Solar energy
Principal Investigators: Robert J. Fowler, Renaissance Computing Institute, University of North Carolina (UNC); David Bailey, Berkeley Lab; John Mellor-Crummey, Rice University; Jeffrey Vetter, Oak Ridge National Laboratory (ORNL)
Co-Investigators: Jeff Tilson, UNC; Juan Meza, Berkeley Lab; Gabriel Marin, ORNL
SciDAC-e Award (Berkeley Lab portion): $200,000

Production of solar energy can take many forms, from the direct production of fuels from sunlight through artificial photosynthesis, to the production of electricity through photovoltaics. Using computation to identify promising classes of chemicals that can catalyze such reactions involves multiscale QM/MM (quantum mechanics/molecular mechanics) simulations coupled with higher-level models, all under the coordination of an optimization framework to search the space of candidate molecules.

This project brings together a group of computer scientists and applied mathematicians from the SciDAC Performance Engineering Research Institute (PERI) to work on improving the productivity of computational activities as well as the overall research program at the Solar Fuels and Next Generation Photovoltaics EFRC at the University of North Carolina. The three major thrusts of this effort will be: (1) the integration of performance engineering tools and methods with the ongoing development of the computational chemistry methods and codes of the UNC EFRC; (2) the development of advanced simulation-driven optimization methods to apply to this problem; and (3) the structuring of the computations under a workflow and data management framework to ensure performance portability across a range of systems while improving the productivity of the computational chemists at the EFRC.

For more information on the Berkeley Lab's Carbon Cycle 2.0 Initiative, please visit: http://carboncycle2.lbl.gov/

About Computing Sciences at Berkeley Lab

The Lawrence Berkeley National Laboratory (Berkeley Lab) Computing Sciences Area provides the computing and networking resources and expertise critical to advancing Department of Energy Office of Science (DOE-SC) research missions: developing new energy sources, improving energy efficiency, developing new materials, and increasing our understanding of ourselves, our world and our universe.

ESnet, the Energy Sciences Network, provides the high-bandwidth, reliable connections that link scientists at 40 DOE research sites to each other and to experimental facilities and supercomputing centers around the country. The National Energy Research Scientific Computing Center (NERSC) powers the discoveries of 7,000-plus scientists at national laboratories and universities, including those at Berkeley Lab's Computational Research Division (CRD). NERSC and ESnet are both Department of Energy Office of Science National User Facilities. The Computational Research Division (CRD) conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture and high-performance software implementation. NERSC and ESnet are Department of Energy Office of Science User Facilities.

Berkeley Lab addresses the world's most urgent scientific challenges by advancing sustainable energy, protecting human health, creating new materials, and revealing the origin and fate of the universe. Founded in 1931, Berkeley Lab's scientific expertise has been recognized with 13 Nobel prizes. The University of California manages Berkeley Lab for the DOE’s Office of Science.

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