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Media Contacts
![non-powered dam](/sites/default/files/styles/list_page_thumbnail/public/2022-07/hydropower-resized_0.jpg?h=56d0ca2e&itok=SQEPNh78)
Although more than 92,000 dams populate the country, the vast majority — about 89,000 — do not generate electricity through hydropower.
![An open-source code developed by an ORNL-led team could provide new insights into the everyday operation of the nation’s power grid. Credit: Pixabay](/sites/default/files/styles/list_page_thumbnail/public/2021-10/digitization-gef50ab16f_1920_0.jpg?h=e5aec6c8&itok=55oFYLLz)
Oak Ridge National Laboratory, University of Tennessee and University of Central Florida researchers released a new high-performance computing code designed to more efficiently examine power systems and identify electrical grid disruptions, such as
![The D2U model categorizes user data by capturing behavior in all open programs throughout a user’s day. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-09/User%20Emulation%20Graphic%20v1_0.jpg?h=8f74817f&itok=kZiQWuZI)
Oak Ridge National Laboratory researchers have created a technology that more realistically emulates user activities to improve cyber testbeds and ultimately prevent cyberattacks.
![ORNL’s particle entanglement machine is a precursor to the device that researchers at the University of Oklahoma are building, which will produce entangled quantum particles for quantum sensing to detect underground pipeline leaks. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-07/IMG_20170706_154618586AK_0.jpg?h=61873cd7&itok=0OWbsNbu)
To minimize potential damage from underground oil and gas leaks, Oak Ridge National Laboratory is co-developing a quantum sensing system to detect pipeline leaks more quickly.
![An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-07/2019-P07408_2.jpg?h=8f9cfe54&itok=jBvKdqIv)
Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.
![F1](/sites/default/files/styles/list_page_thumbnail/public/2020-10/F1_Edited.jpg?h=e91a75a9&itok=xdcO0UVz)
Oak Ridge National Laboratory scientists helped count the population of Nigeria – all without leaving the lab.
![GIS – LandScan goes public](/sites/default/files/styles/list_page_thumbnail/public/2020-05/sanfran%20%281%29_2.png?h=ea18fbc4&itok=c_VbFqAC)
Oak Ridge National Laboratory’s high-resolution population distribution database, LandScan USA, became permanently available to researchers in time to aid the response to the novel coronavirus pandemic.
![A new computational approach by ORNL can more quickly scan large-scale satellite images, such as these of Puerto Rico, for more accurate mapping of complex infrastructure like buildings. Credit: Maxar Technologies and Dalton Lunga/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2020-02/Puerto_Rico_Resflow9.png?h=a0a1befd&itok=5n2fss_e)
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
![Smart Neighborhood homes](/sites/default/files/styles/list_page_thumbnail/public/2020-01/04.09.TD-SMartHome_0.jpg?h=5b5a5437&itok=22S5Tle1)
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
![Project bridges compute staff, resources at ORNL and VA health data to speed suicide risk screening for US veterans. Image Credit: Carlos Jones, ORNL](/sites/default/files/styles/list_page_thumbnail/public/2019-08/VA_REACHVET1%5B6%5D_0.jpg?h=173ee000&itok=-eA5t15j)
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.