Lawrence Berkeley National Laboratory

How will the United States satisfy energy demand in a tightening global energy marketplace while, at the same time, reducing greenhouse gas emissions? Exascale computing -- expected to be available within the next eight to ten years ? may play a crucial role in answering that question by enabling a paradigm shift from test-based to science-based design and engineering. Computational modeling of complete power generation systems and engines, based on scientific first principles, will accelerate the improvement of existing energy technologies and the development of new transformational technologies by pre-selecting the designs most likely to be successful for experimental validation, rather than relying on trial and error. The predictive understanding of complex engineered systems made possible by computational modeling will also reduce the construction and operations costs, optimize performance, and improve safety. Exascale computing will make possible fundamentally new approaches to quantifying the uncertainty of safety and performance engineering. This report discusses potential contributions of exa-scale modeling in four areas of energy production and distribution: nuclear power, combustion, the electrical grid, and renewable sources of energy, which include hydrogen fuel, bioenergy conversion, photovoltaic solar energy, and wind turbines. Examples of current research are taken from projects funded by the U.S. Department of Energy (DOE) Office of Science at universities and national laboratories, with a special focus on research conducted at Lawrence Berkeley National Laboratory.