Fellowship Experiences

Julia Ling

Julie Ling

2015 Truman Fellow

After graduating from Princeton University magna cum laude with a bachelor’s degree in physics, Julia continued her studies at Stanford University, where she earned a master’s degree and a Ph.D. in mechanical engineering and received six merit fellowships, including one from the National Science Foundation.

For her dissertation, Julia designed fundamental physical experiments to understand the effect of trailing-edge geometry on cooling jet behavior, the biggest source of inefficiency in gas turbine engines. Julia developed a new way to compare computer simulations to physical experiments and used insights from these comparisons to significantly improve the state of the art in the computational modeling of turbulence.

At a Truman Fellow, Julia is addressing the long-standing physics challenge of how to best model turbulent flows. Working closely with her Sandia mentors, Jeremy Templeton and Greg Wagner, Julia is applying optimization techniques and machine-learning algorithms to fluids datasets with the goal of improving uncertainty analysis for computational fluid dynamics simulations. She hopes to use these algorithms to predict when Reynolds-averaged Navier–Stokes (RANS) models will be inaccurate, enabling tighter uncertainty bounds and model improvements. If successful, Julia’s new insights will be invaluable to the design of gas turbine engines, internal combustion engines, and other high-Reynolds number flow situations.

“The Truman Fellowship offers an amazing opportunity to dive deep into a challenging research topic, spend time to learn about new fields, and set the research direction myself. The people here at Sandia and their expertise in uncertainty quantification, machine learning, and fluids simulations have been an invaluable help in my research so far. By tapping into their tremendous expertise, I hope to transform the way fluids simulations results are post-processed and understood.”