Filter News
Area of Research
- Advanced Manufacturing (22)
- Biology and Environment (13)
- Building Technologies (1)
- Computer Science (2)
- Electricity and Smart Grid (3)
- Energy Science (114)
- Functional Materials for Energy (1)
- Fusion and Fission (5)
- Fusion Energy (1)
- Materials (28)
- Materials for Computing (4)
- National Security (8)
- Neutron Science (6)
- Nuclear Science and Technology (4)
- Quantum information Science (1)
- Sensors and Controls (1)
- Supercomputing (10)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (143)
- (-) Grid (74)
- Advanced Reactors (40)
- Artificial Intelligence (124)
- Big Data (77)
- Bioenergy (110)
- Biology (126)
- Biomedical (73)
- Biotechnology (37)
- Buildings (73)
- Chemical Sciences (84)
- Clean Water (32)
- Composites (33)
- Computer Science (223)
- Coronavirus (48)
- Critical Materials (29)
- Cybersecurity (35)
- Education (5)
- Element Discovery (1)
- Emergency (4)
- Energy Storage (114)
- Environment (217)
- Exascale Computing (64)
- Fossil Energy (8)
- Frontier (62)
- Fusion (65)
- High-Performance Computing (128)
- Hydropower (12)
- Irradiation (3)
- Isotopes (62)
- ITER (9)
- Machine Learning (67)
- Materials (156)
- Materials Science (156)
- Mathematics (12)
- Mercury (12)
- Microelectronics (4)
- Microscopy (56)
- Molten Salt (10)
- Nanotechnology (62)
- National Security (86)
- Neutron Science (169)
- Nuclear Energy (121)
- Partnerships (66)
- Physics (68)
- Polymers (34)
- Quantum Computing (52)
- Quantum Science (88)
- Security (30)
- Simulation (64)
- Software (1)
- Space Exploration (26)
- Statistics (4)
- Summit (70)
- Transportation (102)
ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.
21 - 30 of 211 Results

Researchers at ORNL are using advanced manufacturing techniques to revitalize the domestic production of very large metal parts that weigh at least 10,000 pounds each and are necessary for a variety of industries, including energy.

Justin West, an advanced machining and machine tool researcher at ORNL, has been selected as a recipient of the 2024 30 Under 30 award by the Society of Manufacturing Engineers.

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.

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.

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.

ORNL is working with industry partners to develop a technique that combines 3D printing and conventional machining to produce large metal parts for 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.
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.

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.

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.

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.