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Drawing of air taxi

If air taxis become a viable mode of transportation, Oak Ridge National Laboratory researchers have estimated they could reduce fuel consumption significantly while alleviating traffic congestion.

A Co-Optima research team led by Oak Ridge National Laboratory’s Jim Szybist in collaboration with Argonne, Sandia and the National Renewable Energy Laboratory, created a merit function tool that evaluates six fuel properties and their impact on engine performance, giving the scientific community a guide to quickly evaluate biofuels. Credit: ORNL/U.S. Dept. of Energy

As ORNL’s fuel properties technical lead for the U.S. Department of Energy’s Co-Optimization of Fuel and Engines, or Co-Optima, initiative, Jim Szybist has been on a quest for the past few years to identify the most significant indicators for predicting how a fuel will perform in engines designed for light-duty vehicles such as passenger cars and pickup trucks.

Suman Debnath is using simulation algorithms to accelerate understanding of the modern power grid and enhance its reliability and resilience. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.

Jianlin Li employs ORNL’s world-class battery research facility to validate the innovative safety technology. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Soteria Battery Innovation Group has exclusively licensed and optioned a technology developed by Oak Ridge National Laboratory designed to eliminate thermal runaway in lithium ion batteries due to mechanical damage.

ORNL scientists used new techniques to create long lengths of a composite copper-carbon nanotube material with improved properties for use in electric vehicle traction motors. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory used new techniques to create a composite that increases the electrical current capacity of copper wires, providing a new material that can be scaled for use in ultra-efficient, power-dense electric vehicle traction motors.

This photo shows the interior of the vessel of the General Atomics DIII-D National Fusion Facility in San Diego, where ORNL researchers are testing the suitability of tungsten to armor the inside of a fusion device. Credit: General Atomics

The inside of future nuclear fusion energy reactors will be among the harshest environments ever produced on Earth. What’s strong enough to protect the inside of a fusion reactor from plasma-produced heat fluxes akin to space shuttles reentering Earth’s atmosphere?

Cars and coronavirus

Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.

The hybrid inverter developed by ORNL is an intelligent power electronic inverter platform that can connect locally sited energy resources such as solar panels, energy storage and electric vehicles and interact efficiently with the utility power grid. Credit: Carlos Jones, ORNL/U.S. Dept of Energy.

ORNL researchers have developed an intelligent power electronic inverter platform that can connect locally sited energy resources such as solar panels, energy storage and electric vehicles and smoothly interact with the utility power grid.

ORNL’s Drew Elliott served as a major collaborator in upgrading the Princeton Plasma Physics Laboratory’s Lithium Tokamak Experiment-Beta. Credit: Robert Kaita, Princeton Plasma Physics Laboratory

Lithium, the silvery metal that powers smart phones and helps treat bipolar disorders, could also play a significant role in the worldwide effort to harvest on Earth the safe, clean and virtually limitless fusion energy that powers the sun and stars.

Map with focus on sub-saharan Africa

Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.