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ORNL engineer Canan Karakaya uses computational modeling to design and improve chemical reactors and how they are operated to convert methane, carbon dioxide, ammonia or ethanol into higher-value chemicals or energy-dense fuels. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Canan Karakaya, a R&D Staff member in the Chemical Process Scale-Up group at ORNL, was inspired to become a chemical engineer after she experienced a magical transformation that turned ammonia gas into ammonium nitrate, turning a liquid into white flakes gently floating through the air. 

Intern Noah Miller, left, and his mentor, Joe McVeigh, stand with their poster at the American Glovebox Society conference in 2023.

College intern Noah Miller is on his 3rd consecutive internship at ORNL, currently working on developing an automated pellet inspection system for Oak Ridge National Laboratory’s Plutonium-238 Supply Program. Along with his success at ORNL, Miller is also focusing on becoming a mentor for kids, giving back to the place where he discovered his passion and developed his skills. 

Instantaneous solution quantities shown for a static Mach 1.4 solution on a mesh consisting of 33 billion elements using 33,880 GPUs, or 90% of Frontier.  From left to right, contours show the mass fractions of the hydroxyl radical and H2O, the temperature in Kelvin, and the local Mach number. Credit: Gabriel Nastac/NASA

Since 2019, a team of NASA scientists and their partners have been using NASA’s FUN3D software on supercomputers located at the Department of Energy’s Oak Ridge Leadership Computing Facility to conduct computational fluid dynamics simulations of a human-scale Mars lander. The team’s ongoing research project is a first step in determining how to safely land a vehicle with humans onboard onto the surface of Mars.

New system combines human, artificial intelligence to improve experimentation

To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance. 

Prasad Kandula builds a medium-voltage solid state circuit breaker as part of ORNL’s project to develop medium-voltage power electronics in GRID-C. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.

ORNL’s Tomás Rush examines a culture as part of his research into the plant-fungus relationship that can help or hinder ecosystem health. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses. 
 

Prasanna Balaprakash, who leads ORNL’s AI Initiative, participated in events hosted by the White House Office of Science and Technology Policy and the Task Force on American Innovation to discuss the challenges and opportunities posed by AI. Credit: Brian Mosley/Computing Research Association

In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.

Naval Academy midshipmen look at tiny particle fuels while touring ORNL. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy

Nuclear engineering students from the United States Military Academy and United States Naval Academy are working with researchers at ORNL to complete design concepts for a nuclear propulsion rocket to go to space in 2027 as part of the Defense Advanced Research Projects Agency DRACO program.

Sangkeun “Matt” Lee received the Best Poster Award at the Institute of Electrical and Electronics Engineers 24th International Conference on Information Reuse and Integration.

Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii –  and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.

ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.

Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.