Events at ORNL

Nanostructured Materials for Tribology

The traditional approach in designing a moving mechanical assembly with contacting surfaces has been to identify a lubrication strategy after major decisions on materials selection and fabrication routes have been made. However, an engineering material for a tribocomponent, whether for the Wright Brothers’ Engine that powered the first historic flight at Kitty Hawk or for the modern satellite, must address the challenge of achieving the desired balances between physical, mechanical and tribological properties.


  • Somuri V. Prasad, Sandia National Laboratories, Albuquerque, New Mexico

History and Status of Ceramic Development for Gas Turbines at GE

General Electric has been working on development of Ceramic Composites for about 30 years, focusing since early nineties on SiC fiber reinforced SiC-Si matrix composites made by silicon melt infiltration (MI CMCs). Early work for industrial gas turbines was partly funded by DOE under the CFCC (Continuous Fiber Reinforced Ceramic Composites) program while the activities for aircraft engines were partly funded by NASA under the High Speed Civil Transport (HSCT).


  • Krishan L. Luthra, GE Global Research, Schenectady, New York

Developing Leadership Potential

For over 25 years, I’ve led large research organizations, was a science advisor in the U.S. Congress, oversaw all civil research for three presidents, and walked across Spain. I’ll talk about the leaders I met along this journey and how they formed my own views on leadership.


  • Jack D. Fellows, Climate Change Science Institute

Synthesis of Near-Infrared Dyes

Several synthetic routes utilizing different heterocyclic scaffolds were develop to produce near-infrared (NIR) dyes. A number of products were synthesized and characterized. The product heptamethine and nonamethine-based fluorescent dyes were tested to determine the relationship between photophysical properties and structures with an emphasis on increasing the rigidity of the structure.


  • Michael Quinn, Division Staff

HPC: Powering Deep Learning

During the past few years, deep learning has made incredible progress towards solving many previously difficult Artificial Intelligence (AI) tasks.  Although the techniques behind deep learning have been studied for decades, they rely on large datasets and large computational resources, and so have only recently become practical for many problems.  Training deep neural networks is very computationally intensive: training one model takes tens of exaflops of work, and so HPC techniques are key to creating these models.  As in other fields, progress


  • Bryan Catanzaro, Baidu Research Silicon Valley Artificial Intelligence Laboratory

Modeling for Building Energy Savings

Buildings use 41% of the energy used in the United States, and consumption is projected to grow by 25% in the next two decades. Use of energy simulation tools in the design of energy-related building systems is essential for identifying methods that improve building efficiency. A recent study showed that buildings designed with simulation consume 44% less energy.


  • Mahabir Bhandari, ORNL Building Technologies Research and Integration Center, Oak Ridge, TN

Parallelization and Adaptive Mesh Refinement

Parallelization and adaptive mesh refinement (AMR) are two techniques that can be exploited to speedup computation and to solve problems that would otherwise be inaccessible due to large memory requirements. In the case of parallelization, the speedup is obtained by partitioning the work between more processors while larger problems can be solved by having access to more memory.


  • Bruno Turcksin, Departments of Mathematics, Texas A&M University, College Station, TX