4 PM 1050 Ford Robotics Bldg with Reception to follow in lobby

Karen A. Flack is a Professor in the Mechanical Engineering Department at the United States Naval Academy in Annapolis, Maryland. She received a bachelor’s degree from Rice University, a master’s degree from the University of California, Berkeley and a Ph.D. from Stanford University, all in Mechanical Engineering. Professor Flack teaches courses in thermodynamics, fluid mechanics, heat transfer, and design. Her research focuses on turbulent boundary layer physics with a concentration on rough wall boundary layers and frictional drag prediction. Recent work also includes performance characteristics of tidal turbines in unsteady flow conditions. She is on the editorial boards of the International Journal of Heat and Fluid Flow, Experiments in Fluids and Flow Turbulence and Combustion. She is a Fellow of the American Physical Society and has received the following: an ASME award for best paper in the Journal of Fluids Engineering, a Pi Tau Sigma teaching award, the Naval Academy Research award and United States government meritorious service medals.

Significant progress has been made towards the understanding of rough-wall boundary layers and the subsequent drag penalty. Continued progress is promising since a larger range of parameter space can now be investigated experimentally and numerically. Recent advances in rapid prototyping techniques enables the generation of systematic variations of roughness scales and computationally efficient simulations with creative surface mapping techniques allows for experiments and computations to investigate similar complex roughness. While a universal drag prediction correlation is still elusive and may not be possible, predictive correlations for classes of surface roughness pertinent to engineering applications seem achievable. Three surface parameters based solely on surface statistics are showing promise in predictive correlations for a range of studies. These include a measure of surface elevation a slope parameter and the skewness of the surface elevation probability density function. Other candidate parameters that may be useful in a predictive correlation or a surface filter are the streamwise and spanwise correlation lengths. The challenges to represent this wide range of surface conditions and potential scales to characterize engineering roughness including biofouling in predictive correlations will be discussed.