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.
Scientists at the Department of Energy’s Oak Ridge National Laboratory have a powerful new tool in the quest to produce better plants for biofuels, bioproducts and agriculture.
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.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.
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.
Ada Sedova’s journey to Oak Ridge National Laboratory has taken her on the path from pre-med studies in college to an accelerated graduate career in mathematics and biophysics and now to the intersection of computational science and biology
Research by an international team led by Duke University and the Department of Energy’s Oak Ridge National Laboratory scientists could speed the way to safer rechargeable batteries for consumer electronics such as laptops and cellphones.
Scientists at the Department of Energy’s Oak Ridge National Laboratory have developed a new method to peer deep into the nanostructure of biomaterials without damaging the sample. This novel technique can confirm structural features in starch, a carbohydrate important in biofuel production.
Each year, approximately 6 billion gallons of fuel are wasted as vehicles wait at stop lights or sit in dense traffic with engines idling, according to US Department of Energy estimates.
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.