Saxton-Fox receives Young Investigator Program and NSF award
From more than 260 applicants, AE Professor Theresa Saxton-Fox is one of just 38 recipients of an Office of Naval Research Young Investigator Program award. They will share $20 million in funding to conduct innovative scientific research that will benefit the U.S. Navy and Marine Corps.
Her research is titled, "Measurements of Turbulent Boundary Layers with Adverse Pressure Gradients and Curvature," with program officer Peter Chang in hydrodynamics related to ships/submarines.
The Office of Naval Research Young Investigator Program award is a highly competitive and popular early-career award program which requires prior academic achievement and demonstrated potential for significant scientific breakthroughs as part of the evaluation criteria. Recipients must be college or university faculty who obtained a Ph.D. on or after Jan. 1, 2013.
According to the ONR YIP press release about the award, it was established in 1985 and is one of the nation’s oldest and most selective basic-research, early-career awards in science and technology. Its purpose is to fund tenure-track academic researchers, or equivalent, whose scientific pursuits show outstanding promise for supporting the Department of Defense, while also promoting their professional development.
Saxton-Fox also received an award from the National Science Foundation for her work entitled “Uncovering the self-sustaining cycle in the outer region of turbulent boundary layers.”
About that work, she said, “Turbulent boundary layers dominate drag on ships and planes, limit the efficiency of engines, and prevent the prediction of forest fire spread. Certain turbulent motions are commonly observed instantaneously and in the statistics of turbulent boundary layers and contribute significantly to drag and heat transfer on surfaces. If we identify the mechanisms that promote and sustain these common turbulent motions, we can disrupt those mechanisms to decrease drag and heat transfer and model those mechanisms to generate dynamically representative, low-cost turbulence models.”