News & Media
Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions
Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.
Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:
- Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
- Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
- Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
- Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
- Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC).
The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.
Top Ten Blendstocks for Turbocharged Gasoline Engines
Bio-blendstocks with the potential to deliver the highest engine efficiency
More efficient engines enabled by better fuels could increase the fuel economy of light duty (LD) vehicles by 10 percent beyond current technology and planned developments. This report identifies top blendstocks that can be derived from biomass and are suitable for further development and commercialization. These blendstocks are best-suited for LD gasoline, boosted spark ignition (BSI) engines. The blendstocks were identified using a fuel property basis using the BSI merit function. The merit function determines potential improvements in engine efficiency, was used to evaluate the performance of candidate bioblendstocks in blends up to 30%. Those that exceeded the efficiency of an E10 premium were included in this list. This report is aimed at biofuel researchers looking to better understand the efficiency implications of biofuels under development, as well as engine researchers who are interested in future biofuels with properties that enable more efficient engine design and operation.
The Co-Optimization of Fuels & Engines (Co-Optima) team includes experts from nine national laboratories: Argonne, Idaho, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge, Pacific Northwest, and Sandia National Laboratories and the National Renewable Energy Laboratory. The team’s expertise includes biofuel development, fuel property testing and characterization, combustion fundamentals, modeling and simulation from atomic scale to engine scale, and analysis.
Gaspar, Daniel J., West, Brian H., Ruddy, Danial, Wilke, Trenton J., Polikarpov, Evgueni, Alleman, Teresa L., George, Anthe, Monroe, Eric, Davis, Ryan W., Vardon, Derek, Sutton, Andrew D., Moore, Cameron M., Benavides, Pahola T., Dunn, Jennifer, Biddy, Mary J., Jones, Susanne B., Kass, Michael D., Pihl, Josh A., Pihl, Josh A., Debusk, Melanie M., Sjoberg, Magnus, Szybist, Jim, Sluder, C S., Fioroni, Gina, and Pitz, William J. Top Ten Blendstocks Derived From Biomass For Turbocharged Spark Ignition Engines: Bio-blendstocks With Potential for Highest Engine Efficiency. United States: N. p., 2019. Web. doi:10.2172/1567705.