Quantum precision reached in modeling molten salt behavior
Additive manufacturing can make the design and production of specialized tools for geothermal energy cheaper and more efficient, according to a study by Oak Ridge National Laboratory.
As a computer engineer at Oak Ridge National Laboratory, Gina Accawi has long been the quiet and steady force behind some of the Department of Energy’s most widely used online tools and applications.
Oak Ridge National Laboratory researchers, in collaboration with Cincinnati Inc., demonstrated the potential for using multimaterials and recycled composites in large-scale applications by 3D printing a mold that replicated a single facet of a
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
In his career focused on energy storage science, Jianlin Li has learned that discovering new ways to process and assemble batteries is just as important as the development of new materials.
Oak Ridge National Laboratory scientists proved molybdenum titanium carbide, a refractory metal alloy that can withstand extreme temperature environments, can also be crack free and dense when produced with electron beam powder bed fusion.
Oak Ridge National Laboratory researchers combined additive manufacturing with conventional compression molding to produce high-performance thermoplastic composites reinforced with short carbon fibers.
Xin Sun has been selected as the associate laboratory director for the Energy Science and Technology Directorate, or ESTD, at the Department of Energy’s Oak Ridge National Laboratory.
A team of Oak Ridge National Laboratory researchers demonstrated that an additively manufactured hot stamping die – a tool used to create car body components – cooled faster than those produced by conventional manufacturing methods.
A method developed at Oak Ridge National Laboratory to print high-fidelity, passive sensors for energy applications can reduce the cost of monitoring critical power grid assets.