Kiran Bhaganagar
University of Texas, San Antonio, Université Grenoble Alpes, Cornell University
0000-0001-5205-4690
About
Prof. Kiran Bhaganagar is an expert in the area of computational turbulence. Bhaganagar received her Ph.D. from the Sibley School of Mechanical and Aerospace Engineering at Cornell University under the guidance of Prof. John Lumley a renowned expert on Turbulence. In her Ph.D. thesis, Bhaganagar developed one of the first Direct numerical simulation (DNS) solvers to simulate spatial transition to turbulence. Bhaganagar has made important and fundamental contributions to the areas of the rough-wall turbulent boundary layer, Buoyancy-driven flows, Stratified turbulence and its effects on wind turbine wake, plume transport, density currents, mixing, and entrainment. Bhaganagar has developed state-of-art DNS solvers for various physical problems, including, DNS of Oscillatory, pulsatile and unsteady flows over roughness, WRF-LES for buoyant transport, DNS of lock-exchange density currents. Bhaganagar is currently extending the turbulent framework to study turbulence mixing in the Rotation Detonation Engine and Plume-surface Interactions during Rocket landing. Bhaganagar has versatile contributions to the various areas of turbulence. Currently, Bhaganagar is developing new generation AI solvers for Navier Stokes Equations. Bhaganagar is an Associate Fellow of the American Institute of Aeronautics and Astronautics. Bhaganagar is currently the Professor of Mechanical Engineering at the University of Texas San Antonio. Bhaganagar served as a Visiting Professor at LEGI, Grenoble, France, Andy Thomas Space Institute, University of Adelaide, Australia during her Sabbaticals. Email her at [email protected]
Employment
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University of Texas, San Antonio Professor2021 - Present
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Université Grenoble Alpes Visiting Professor2017 - 2017
Education
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Cornell University Ph.D.
Projects & Funding
Projects & funding information is unavailable.
Publications (54)
- UrbanDRL-CFD: A Prospective Framework for Integrating Computational Fluid Dynamics and Deep Reinforcement Learning in Urban Heat Island Mitigation Save
- Deep Learning Approach Integrating Physical and Data-Driven Features for Predicting Leeway Drift, Downwind and Crosswind Components Save
- Accelerated Elliptical PDE Solver for Computational Fluid Dynamics Based on Configurable U-Net Architecture: Analogy to V-Cycle Multigrid Save
- PyPlume: An Automated Python-Based Library for Analyzing Turbulent Plumes from bplume-WRF-LES Model Save
- Energetics of buoyancy-generated turbulent flows with active scalar: pure buoyant plume Save
- A Novel Machine-Learning Framework With a Moving Platform for Maritime Drift Calculations Save
- Configurable simulation strategies for testing pollutant plume source localization algorithms using autonomous multisensor mobile robots Save
- New findings in vorticity dynamics of turbulent buoyant plumes Save
- Computational Fluid Dynamic Analysis of the Flow Around a Propeller Blade of Multirotor Unmanned Aerodynamic Vehicle Save
- The Four Stage Development of Starting Turbulent Buoyant Plumes Save