Exploring fossil fuel efficiency through mathematics
From age 10, Ashley Weber knew she wanted to pursue mathematics. Initially, she was inspired by her mother who studied mathematics in college. As she continued through school and learned more, the excitement for mathematics never ceased. Weber entered college as a mathematics major and continued to graduate school at Brown University, where she is currently pursuing a doctoral degree.
“This experience has increased my knowledge and shown me the practical application of data analytics. I was able to broaden my network and experience research at a national laboratory. My overall impression of the experience is great. I would recommend it to anyone.”
Seeking real-world experience to supplement her education, Weber found the perfect opportunity in the Mickey Leland Energy Fellowship (MLEF) Program.
The MLEF Program provides students with fellowship opportunities to gain hands-on research experience with the U.S. Department of Energy (DOE) Office of Fossil Energy. The program’s mission is to strengthen and increase the pipeline of diverse future science, technology, engineering and math professionals.
For her fellowship, Weber was stationed at Pacific Northwest National Laboratory in Richland, Washington. Weber researched with her mentor Ram Devanathan, Ph.D., on the Fossil Energy eXtremeMAT project.
The Fossil Energy eXtremeMAT project is part of a long-term effort to make fossil energy more efficient. If fossil fuel technologies could operate at higher temperatures (800⁰C), they would increase in efficiency and have a lower environmental impact. To operate at higher temperatures, however, the materials used in energy technologies must be resistant to degradation. Typically, those materials are expensive. The goal of the DOE Fossil Energy eXtremeMAT project is to find a new iron-based alloy that will allow fossil fuel technologies to operate at higher temperatures at a modest cost.
Discovering a new alloy isn’t easy. Traditionally, research like this can take up to 20 years or more. The eXtremeMAT group is eager to cut the timeline to less than seven years. Researchers seek to speed up the process by using validated simulations, data analytics and machine learning.
Weber’s mission was to begin the data analytic process. She compiled large amounts of data on iron alloys. With no widely used database to collect from, Weber had to do a significant amount of searching with a variety of sources. Once found, Weber had to extract the data in a format that would be useful and readable for the project’s purposes, including addressing when and where there were gaps in the data. Additionally, Weber prepared the data to be analyzed, which included determining a data format that could be used in machine learning. Finally, Weber sought to test and determine a machine learning algorithm that could analyze the data.
As a result of her efforts, Weber was able to preliminarily investigate data analytics on iron alloy data, including successful attempts at applying multiple machine learning algorithms. Weber contributions have established building blocks for the eXtremeMAT project.
For Weber, the experience was valuable academically as well as professionally. Weber gained insight into conducting research at a national laboratory and networking with professionals in her field.
“This experience has increased my knowledge and shown me the practical application of data analytics. I was able to broaden my network and experience research at a national laboratory. My overall impression of the experience is great. I would recommend it to anyone,” Weber said.
After completing her fellowship, Weber returned to Brown University to finish her doctoral degree in mathematics, which is expected in 2019. After graduation, Weber would like to seek employment in data science.
The MLEF Program is administered by the Oak Ridge Institute for Science and Education (ORISE) for the U.S. Department of Energy. ORISE is managed for DOE by ORAU.