Beyond the Reynolds Analogy: Scalar Transport in Rough-wall Turbulent Boundary Layers

Turbulent air and water flows often carry not only momentum but also heat and other substances such as moisture, pollutants, or nutrients. For many decades, scientists believed that momentum and heat are transported within these flows in similar ways. This assumption works well for flow over smooth surfaces, but it breaks down when the surface is rough, such as forest canopies or textured surfaces. This project will investigate why and how this breakdown occurs, focusing on the fundamental differences in how heat and momentum are transferred to and from rough surfaces. The research team will study the detailed behavior of turbulent flow and heat transfer near complex roughness patterns using experiments in water and glycerin tunnels and high-fidelity computer simulations. Understanding this behavior can assist in improving predictions in weather modeling, pollutant dispersion, heat exchanger design, and even blood flow in roughened vessels. The project will also provide hands-on research experience to graduate students, support STEM education, and launch a public podcast series to communicate the science and its relevance to society. The technical objective of this project is to explain the physical differences between momentum and scalar (e.g., thermal) transport in rough-wall turbulent flows. The research will propose and test a new hypothesis: momentum exchange is governed by unsheltered windward surface areas, while scalar transfer depends on unsheltered planar surfaces. Experiments will be performed in two complementary tunnel facilities, one optimized for velocity measurements and the other for thermal fields, with matched flow and roughness conditions. Direct Numerical Simulations will provide fully resolved flow-field data to validate the experimental setup and guide measurement location. The combined data will be used to inform and train predictive “volumetric” models for heat and scalar transport in rough-walled environments referred to as Distributed Element Roughness Models. These models will combine physics-based reasoning with data-driven techniques to produce robust, generalizable predictions that can be adopted in science and engineering practice over an array of applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.