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Through a new technical collaboration program, companies will be able to propose research projects that utilize the labs and expertise in ORNL’s Grid Research Integration and Deployment Center. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A new technical collaboration program at the Department of Energy’s Oak Ridge National Laboratory will help businesses develop and launch electric grid innovations. Sponsored by the Transformer Resilience and Advanced Components program in DOE’s Office of Electricity, the initiative will provide companies with access to national laboratory resources, enabling them to capture market opportunities. 

ORNL’s convergent manufacturing platform, on display at IMTS 2024 in Chicago, Illinois, integrates multiple systems into one.

A new convergent manufacturing platform, developed in only five months at the Department of Energy’s Oak Ridge National Laboratory, is debuting at the International Manufacturing Technology Show, or IMTS, in Chicago, Sept. 9–12, 2024.

ORNL scientists used molecular dynamics simulations, exascale computing, lab testing and analysis to accelerate the development of an energy-saving method to produce nanocellulosic fibers.

A team led by scientists at ORNL identified and demonstrated a method to process a plant-based material called nanocellulose that reduced energy needs by a whopping 21%, using simulations on the lab’s supercomputers and follow-on analysis.

Jay Tiley inspects a hydroelectric runner from TVA’s Cherokee Dam

ORNL is working with industry partners to develop a technique that combines 3D printing and conventional machining to produce large metal parts for clean energy applications. The project, known as Rapid Research on Universal Near Net Shape Fabrication Strategies for Expedited Runner Systems, or Rapid RUNNERS, recently received $15 million in funding from DOE. 

Illustration of oscillating UCI3 bonds

Researchers for the first time documented the specific chemistry dynamics and structure of high-temperature liquid uranium trichloride salt, a potential nuclear fuel source for next-generation reactors. 

ORNL researchers demonstrated the use of drones equipped with cameras and other sensors to check power lines at an EPB of Chattanooga training center for electrical line workers.

Researchers at ORNL recently demonstrated an automated drone-inspection technology at EPB of Chattanooga that will allow utilities to more quickly and easily check remote power lines for malfunctions, catching problems before outages occur.

Debjani Singh

Debjani Singh, a senior scientist at ORNL, leads the HydroSource project, which enhances hydropower research by making water data more accessible and useful. With a background in water resources, data science, and earth science, Singh applies innovative tools like AI to advance research. Her career, shaped by her early exposure to science in India, focuses on bridging research with practical applications.

Green and blue background of a graphic image that says Honors and Awards

Two additive manufacturing researchers from ORNL received prestigious awards from national organizations. Amy Elliott and Nadim Hmeidat, who both work in the Manufacturing Science Division, were recognized recently for their early career accomplishments.

This photo is of a male scientist sitting at a desk working with materials, wearing protective glasses.

Researchers at the Department of Energy’s Oak Ridge National Laboratory and partner institutions have launched a project to develop an innovative suite of tools that will employ machine learning algorithms for more effective cybersecurity analysis of the U.S. power grid. 

ORNL researchers Phani Marthi and Suman Debnath work on developing and scaling up new EMT simulation software to analyze how power electronics in the electric grid will respond to brief interruptions in power flow. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Power companies and electric grid developers turn to simulation tools as they attempt to understand how modern equipment will be affected by rapidly unfolding events in a complex grid.