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Three ORNL scientists to receive DOE Early Career Research awards

ORNL’s Guannan Zhang, Elizabeth Herndon and Trey Gebhart have been selected to receive Department of Energy Early Career Research awards. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

The Department of Energy’s Office of Science has selected three Oak Ridge National Laboratory scientists for Early Career Research Program awards.

Now in its 13th year, the program aims to bolster national scientific discovery research by providing funding for researchers beginning careers in areas within DOE’s Office of Science's eight major program offices. Many scientists conduct their most formative work in these early years of their careers. A total of 84 scientists nationwide, including 27 employed by national laboratories, will receive awards through this year’s Early Career Research Program.

“Supporting America’s scientists and researchers early in their careers will ensure the U.S. remains at the forefront of scientific discovery and develops the solutions to our most pressing challenges,” said U.S. Secretary of Energy Jennifer M. Granholm. “The funding announced today will allow the recipients the freedom to find the answers to some of the most complex questions as they establish themselves as experts in their fields.”

“These ORNL researchers exemplify the groundbreaking work done by DOE to advance clean energy technologies and scientific discovery,” ORNL Director Thomas Zacharia said. “Supporting these scientists as they begin their careers will have a lasting impact on ORNL’s and the nation’s research efforts in critical fields.”

The ORNL researchers receiving awards are as following:

Trey Gebhart, a mechanical engineer in the Fusion Energy Division, was selected by the Fusion Energy Sciences Program for his proposal, “Solutions For a More Efficient and Economical Fusion Fuel Cycle.”

Nuclear fusion, the type of reaction that powers the Sun, has great potential as a clean and efficient energy source for the future. However, the success of fusion depends upon several factors, including power plant economics, energy gain factor and fuel cycle management. In terms of issues surrounding fusion efficiency in deuterium-tritium fueled power plants, research is needed to increase the efficiency of the infrastructure dedicated to capturing and reusing unburned tritium. Gebhart’s research aims to improve fusion’s efficiency by investigating the potential of direct internal recycling, in which a continuous plasma exhaust separation loop results in a significant amount of fuel being supplied directly back into the fuel systems, rather than passing through the tritium exhaust processing plant. This method is faster than other current tritium approaches and reduces the size of processing equipment and overall power plant tritium inventory.

Guannan Zhang, a computational mathematician in the Computer Science and Mathematics Division, was selected by the Advanced Scientific Computing Research Program for his proposal, “Advanced Uncertainty Quantification Methods for Scientific Inverse Problems.”

Scientific inverse sampling uses quantities measured in scientific experiments to make inferences about other parameters that are not observable in physical models. Inverse sampling can have an advantage over measurements taken in experiments subject to data noise and uncertainty, which can obscure important discoveries. However, the complex geometry of Bayesian posterior distributions, used for inverse sampling, also poses a problem of data uncertainty. Zhang’s research will address these challenges in inverse sampling by building deep neural networks, a type of artificial intelligence. The networks will learn Bayesian posterior distribution to conduct inverse sampling more efficiently. The generalized deep neural network model will be developed with continuum architecture, which addresses the problem of time-consuming tuning often required by deep neural networks. Ultimately, the networks will improve efficiency and accuracy while solving inverse sampling problems relevant to DOE research.

Elizabeth Herndon, a senior R&D staff member in the Environmental Sciences Division, was selected by the Biological and Environmental Research program for her proposal, “Biogeochemical Controls on Phosphorus Cycling in Urban-influenced Coastal Ecosystems.”

Coastal river deltas, home to more than 300 million people worldwide, are experiencing significant degradation and erosion as a result of human activities. This, in conjunction with chronic sea level rise, has made low-lying coastal communities more vulnerable to the effects of flooding and increased salinization. The Louisiana Gulf Coast, for example, has experienced significant land loss as a result of urban flood prevention measures. These flood prevention measures often include channelizing rivers to disconnect them from their floodplains and diverting freshwater away from urban areas and toward less developed coasts. However, these diversions also change sediment supply to deltas, altering their susceptibly to flooding, and change the amount of nutrients flowing to coastal ecosystems. Additional research is needed to determine how freshwater diversion affects nutrient availability to ecosystems in coastal river deltas. Herndon’s research will examine how flooding by freshwater and seawater affect interactions between the nutrient phosphorus and the elements iron and manganese in coastal river deltas in order to improve predictive modeling capabilities.

For five years, awardees will receive $500,000 annually to cover salary and research expenses. The final details for each project award are subject to final grant and contract negotiations between DOE and the awardees.

UT-Battelle manages ORNL for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit – Alexandra DeMarco