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The REVISE-II modeling tool developed at ORNL supports decision-making for electric vehicle charging infrastructure development along interstate highways in support of intercity travel. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory have developed a nationwide modeling tool to help infrastructure planners decide where and when to locate electric vehicle charging stations along interstate highways. The goal is to encourage the adoption of EVs for cross-country travel.

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

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.

Scientists genetically engineered bacteria for itaconic acid production, creating dynamic controls that separate microbial growth and production phases for increased efficiency and acid yield. Credit: NREL

A research team led by Oak Ridge National Laboratory bioengineered a microbe to efficiently turn waste into itaconic acid, an industrial chemical used in plastics and paints.

Urban climate modeling

Researchers at Oak Ridge National Laboratory have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks. These impervious surfaces impede the flow of water into the ground, affecting the water cycle and, by extension, the climate.

An X-ray CT image of a 3D-printed metal turbine blade was reconstructed using ORNL’s neural network and advanced algorithms. Credit: Amir Ziabari/ORNL, U.S. Dept. of Energy

Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.

Heat impact map

A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns 

Low-cost, compact, printed sensor that can collect and transmit data on electrical appliances for better load monitoring

Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.

 

Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.

Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.

Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w

A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.

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Oak Ridge National Laboratory scientists studying fuel cells as a potential alternative to internal combustion engines used sophisticated electron microscopy to investigate the benefits of replacing high-cost platinum with a lower cost, carbon-nitrogen-manganese-based catalyst.