High Performance Computing in the Broader Energy Context

High Performance Computing in the Broader Energy Context Steven E. Koonin Under Secretary for Science U. S. Department of Energy May 2011

U. S. Energy Challenges Energy Security Daily Spot Price OK WTI Share of Reserves Held by NOC/IOC Competitiveness Environment Global Lithium-ion Battery Manufacturing (2009) Federal Deficit 2

Administration Goals Transport q q Reduce oil imports by 1/3 by 2025 (~3. 7 M bbl/day) Put 1 million electric vehicles on the road by 2015 Stationary q q By 2035, generate 80% of electricity from a diverse set of clean energy sources Make non-residential buildings 20% more energy efficient by 2020 Environmental q Cut greenhouse gas emissions in the range of 17% below 2005 levels by 2020, and 83% by 2050 3

Estimated U. S. Energy Use in 2009: ~94. 6 Quads https: //flowcharts. llnl. gov/ 4

Energy Essentials Supply As a whole, energy is • A big and expensive system • In private hands • Governed by economics, modulated by government policies • Fewer, long-lived centralized facilities with distribution networks • Change has required decades • Power and fuels are commodities with thin margins • Markets with government regulation and distortion • Technology alone does not a transformation make • Transport and Stationary are disjoint Demand • Many distributed players, shorter-lived assets • User benefit (economics, convenience, personal preference) • Transport is powered by oil • Power • Requires boiling large amounts of water • Sized for extremes (storage is difficult) • Numerous sources with differing… • Determined by price, standards, behavior • Cap. Ex and Op. Ex • Little attention to system optimization for stationary use • Base/Peak/Intermittency • Emissions 5

Six Strategies Supply Stationary Transport Demand Deploy Clean Electricity Modernize the Grid Increase Building and Industrial Efficiency Deploy Alternative Fuels Progressively Electrify the Fleet Increase Vehicle Efficiency 6

DOE-QTR Scope The DOE-QTR will provide a context and robust framework for the Department’s energy programs, as well as principles by which to establish multiyear programs plans and budgets. It will also offer high-level views of the technical status and potential of various energy technologies. The primary focus of the DOE-QTR process and document will be on the following: q Framing the energy challenges q A discussion of the roles of government, industry, national laboratories, and universities in energy system transformation q Summary roadmaps for advancing key energy technologies, systems, and sectors q Principles by which the Department can judge the priority of various technology efforts q A discussion of support for demonstration projects q The connections of energy technology innovation to energy policy http: //www. energy. gov/QTR 7

DOE-QTR Process Nov 2010 3/14 – 4/15 4/20 PCAST made recommendations for DOE to do QER Public comment period for DOE-QTR Framing Document First batch of public comments released on project website Through mid-June End July/Aug Before Dec 2011 Hold workshops and discussions of each of the Six Strategies Submit DOE-QTR to White House for approval Release DOE-QTR http: //www. energy. gov/QTR

DOE-QTR Logic Flow Energy context § Supply/demand § Energy essentials Energy challenges § Oil security § US Competitiveness § Environmental Impact Players and Roles § Private/Gov’t § Within gov’t § Econ/Policy/Tech § Acad/Lab/Private DOE portfolio principles Six strategies Technology Assessments § History § Status § Potential Technology Roadmaps § Milestones § Cost § Schedule § Performers DOE priorities and portfolio Balanced within and across strategies Program plans and budgets http: //www. energy. gov/QTR 9

Energy supply has changed on decadal scales US energy supply since 1850 Source: EIA 10

Technical conservatism is the norm Laboratory Pilot Facilities $ millions 0. 01 bbl/day $ 10’s millions 0. 5 bbl/day Full-scale Demonstration Production At Scale and Infrastructure $ 100’s millions 500 bbl/day 10+ Years $ billions 50, 000 bbl/day

Applications of High Performance Computing Nuclear Energy Applications to Science, National Security, Energy, Product Development High-fidelity predictive simulation tools for the design of next-generation nuclear reactors to safely increase operating margins. Turbulence Understanding the statistical geometry of turbulent dispersion of pollutants in the environment. Energy Storage Understanding the storage and flow of energy in next-generation nanostructured carbon tube supercapacitors Biofuels A comprehensive simulation model of lignocellulosic biomass to understand the bottleneck to sustainable and economical ethanol production. Fusion Energy Substantial progress in the understanding of anomalous electron energy loss in the National Spherical Torus Experiment (NSTX). Nano Science Understanding the atomic and electronic properties of nanostructures in nextgeneration photovoltaic solar cell materials.

Simulations: Early Impacts on Energy Boeing Cummins Goodyear Innovation Predictive optimization of airfoil design New engine brought to market solely with modeling and analysis tools Predictive modeling for new tire design Ford Virtual aluminum casting BMI Corp. Complex fluid dynamics analysis GE/Pratt Accelerated insertion & Whitney of materials in components Impact 7 -fold decrease in testing Reduced development time and cost; improved engine performance 3 -fold reduction in product development time Estimated 7: 1 return on investment; $100 M in savings Reduced concept to production design time by 50%; predicted 12% drag reduction; yielded EPA-certified 6. 9% increase in fuel efficiency Reduced development time by 50%; increased capability with reduced testing Simulations have demonstrated significant improvements in product development cycles across several industry sectors

Boeing: Innovation for flight “High Performance Computing has Fundamentally Changed the Way that Boeing Designs Flight Vehicles. ” -Director, Boeing Commercial Airplanes

Science-Based Engine Design Basic Science BES Sustained support in 2 areas Development of predictive chemistry in model flames Computational kinetics and experiments Advance laser diagnostics applied to model flames Laser-based chemical imaging Applied R&D BES EERE Applications of chemistry and diagnostics to engines Predictive chemical models under realistic conditions Laser diagnostics of diesel fuel sprays in engine cylinders Manufacturing/ Commercialization Cummins and Dodge Cummins used simulation tools and improved understanding of diesel fuel sprays to design a new diesel engine with reduced development time and cost and improved fuel efficiency. ISB 6. 7 liter Cummins diesel engine first marketed in the 2007 Dodge Ram pickup truck; more than 100, 000 sold/year

Research Tools Bridge Fundamentals to Application and Support Model Development q Close coupled modeling and experiments Ø Ø Ø Advanced diagnostics including optical, laser, x-ray, and neutron based techniques Combustion simulators Multi-dimensional computational models Fuel kinetics Multi- and single-cylinder engines HCCI & Leanburn Gasoline Nozzle Sac X-Ray Image Optical Engines Engine Simulation LTC Simulator 3 -Million Cell LES Grid q Close collaboration between industry, national labs and universities q Cross-cuts light- and heavy-duty R&D Leading to engine CFD modeling tools widely used in industry

Smart. Truck/DOE Partnership in HPC: Aerodynamic forces account for ~53% of long haul truck fuel use. § Class 8 semi trucks (300, 000 sold annually) have average fuel efficiency of 6. 7 MPG § Used ORNL’s Jaguar Cray XT-5 2. 3 petaflop computer for complex fluid dynamics analysis – cutting in half the time needed to go from concept to production design § Outcome: Smart. Truck Under. Tray add -on accessories predict reduction of drag of 12% and yield EPA-certified 6. 9% increase in fuel efficiency. § If the 1. 3 million Class 8 trucks in the U. S. had these components, we would save 1. 5 billion gallons of diesel fuel annually (~$4. 4 B in costs and 16. 4 M tons of CO 2) § Awarded as one of the “Top 20 products of 2010” from Heavy Duty Trucking magazine Con-way Freight Inc. is the first corporation to install the Smart. Truck Under. Tray system. 17

CASL: Consortium for Advanced Simulation of Light Water Reactors Leverage Develop • Current state-of-the-art neutronics, thermal-fluid, structural, and fuel performance applications Deliver • New requirements-driven physical models • An unprecedented predictive simulation tool for simulation • Efficient, tightly-coupled multi of physical reactors -scale/multi-physics • Architected for platform • Existing systems and safety algorithms and software with portability ranging from analysis simulation tools quantifiable accuracy desktops to DOE’s leadership • Improved systems and safety class and advanced architecture systems analysis tools (large user base) • UQ framework • Validation basis against 60% of existing U. S. reactor fleet (PWRs), using data from TVA reactors • Base M&S LWR capability

Nuclear Energy models spanning multiple time and length scales 19

Grid Application Opportunities Accomplishments Statistical parameterization of physical reality Uncertainty Quantification Operational Optimization Potential Applications Statistical geometry of turbulent dispersion Topology estimation Stockpile stewardship Look-ahead contingency analysis Nuclear reactors Dynamic and voltage stability analyses 20

Top Performing Supercomputers in the World From the Top 100 rankings

Exascale Program Elements Platform R&D • Power • Integration • Risk Mitigation Critical Technologies (everyone benefits) • Memory • Nonvolatile storage • Optics Software and Environments • Operating environment • Systems Software • System reliability • Programming models Exascale Elements Today’s capability platform becomes tomorrow’s desktop Co-design Platforms • Performance models • Simulators • Applications integration with vendors • Mathematics • Early prototypes to ensure component integration and usefulness • Risk mitigation for vendors – Non recoverable engineering cost

Exascale Competitiveness China is pursuing HPC vigorously q China investing in both hardware and software development q US investment critical if we want to compete q China & US 10 Peta flops 1 0. 1 US China 0. 01 0. 001 Nov, Nov, June, Nov, 2005 2006 2007 2008 2009 2010 Machine Location Speed (max) On list since (rank) Tianhe-1 Tianjin, China 2. 57 PF 2010 (1) Nebulae Shenzhen, China 1. 27 PF 2010 (3)

Budget: Simulations and Exascale Computing President Obama’s FY 12 Budget Proposal q$126 M to DOE for next-gen supercomputing ($91 million in SC and $36 million in NNSA) q. First time federal budget explicitly mentioned “exascale” q. Development of exascale system estimated in 2018 -2020 time frame, contingent on development of software systems that can utilize ~100 million cores FY 12 DOE Exascale Activities Will q Design cost effective, useable, and energy efficient exascale capability by end of decade q Support research efforts in applied mathematics and computer science to develop libraries, tools, and software for these new technologies q Create close partnerships with computational and computer scientists, applied mathematicians, and vendors to develop exascale platforms and codes cooperatively 24

QUESTIONS? /COMMENTS? http: //science. energy. gov/s-4 http: //www. energy. gov/QTR 25