Large-scale features are particularly important in turbulence wall-bounded flows because they are responsible for nearly 60% of Reynolds shear stresses and 70% of turbulence kinetic energy of the flow. Modifying the large-scale features is important to control turbulence flows for enhanced heat transfer in applications like gas-turbine film cooling. This seminar presents how relatively small perturbations at the wall are used to modify the energetic and spatially large-scale features of the flow. It first discusses the implementation of low-order modeling techniques such as proper-orthogonal decomposition, and adaptive Gaussian filtering to study these large-scale features and their influence on the proliferation of coherent structures. Our previous work has shown that the frequencies of perturbations can be designed to match the wavenumbers of large-scale features to proliferate the coherent vortical structures responsible for heat transfer. The second part of the talk presents how the proper-orthogonal decomposition is used to study the dominant behavior of large-scale structures in the interaction of low-level jets (LLJ) with wind farms. This focuses on the mechanism of LLJ for increased capacity factor of wind farms and the role of large-scale features behind the wind turbines in CO2 sequestration.
About the Speaker
Venkatesh Pulletikurthi is a PhD Candidate in School of Mechanical Engineering at Purdue University. He is the first fellow of Purdue-UIUC Senior PhD Teaching Exchange Fellowship started in 2021 between UIUC and Purdue. As a part of this fellowship, he is the lead instructor of one of the sections of ME200 Thermodynamics at the University of Illinois at Urbana-Champaign, spring 2022. He started his direct PhD program at Purdue University in 2017. He earned his BTech (Hons.) in mechanical engineering at Indian Institute of Technology Madras, India in 2016. His research focuses on the numerical simulations of incompressible and transonic turbulence wall-bounded flows. He also conducted experimental studies in collaboration with Renewable Energy & Turbulent Environment Group at UIUC to study s low-level jets effects on wind farms and wind farms over complex topographies. During COVID-19 Pandemic, he expanded his turbulence research expertise onto experimental and numerical studies related to novel sustainable filter, Hy-Cu, designed and patented in Castillo’s Research lab. He is presently working on numerical studies to study the role of wind turbines in the CO2 mixing and facilitate efficient CO2 sequestration. He is also working in the implementation of low-ordered modeling techniques applied to MRI images for the early detection of brain concussion.
Host: Professor Leonardo Chamorro