Tejas Pant, Ph.D.
About Tejas Pant, Ph.D.
Tejas Pant Director of A.I. Research
Tejas Pant is the Director of A.I. Research, focusing on advanced computational methods and data-driven modeling. His work centers around developing high-fidelity physics-based and data-driven models to understand and study complex non-linear interactions. He brings extensive technical expertise in areas such as turbulence, chemical kinetics, acoustics, and molecular diffusion in various types of engines, including aircraft, rocket, and scramjets.
Educational Background of Tejas Pant
Tejas Pant has a strong academic background in engineering. He received his Ph.D. in Aeronautics and Astronautics Engineering from Purdue University. Prior to that, he obtained a Masters degree in Mechanical Engineering from Purdue University. His foundational education includes a Bachelor of Technology degree in Mechanical Engineering from Visvesvaraya National Institute of Technology in Nagpur, India. These rigorous programs have equipped him with a deep understanding of engineering principles and research methodologies.
Research Focus of Tejas Pant in Aeronautics and Astronautics
Tejas Pant's research primarily focuses on developing models to study the interaction between various physical phenomena in propulsion systems. He has developed high-fidelity physics-based and data-driven models to study the non-linear interaction between turbulence, chemical kinetics, acoustics, and molecular diffusion. His significant contributions include creating a generalized compressible turbulent combustion solver using a stochastic model known as the transported probability density function (PDF) method. This tool helps in studying turbulent reacting flows across a range of flow conditions from low-speed subsonic to high-speed supersonic flows.
Tejas Pant's Work on Data-Driven Modeling Approaches
Tejas Pant has worked extensively on data-driven modeling approaches, particularly focusing on studying thermo-acoustic instability. His efforts in this area are directed at enhancing the predictability and control of instabilities in various engine types. By leveraging data-driven techniques, he aims to develop more effective and reliable methods for analyzing and addressing these critical issues within propulsion systems.