Anjini Chandra

TomKat Graduate Fellow for Translational Research

Research Lab: Sanjiva Lele

Year Awarded: 2025

Anjini Chandra is a Ph.D. candidate in the Department of Mechanical Engineering and is advised by Dr. Sanjiva Lele. She is a member of the Flow Physics and Aeroacoustics Laboratory (FPAL) and is studying the physics of compressible, turbulent carbon dioxide flows under transcritical and supercritical conditions. The goal of her work is to inform the design of more efficient and compact supercritical carbon dioxide (sCO₂) power cycles to provide cleaner energy with a smaller carbon footprint. Anjini earned her master’s degree in Mechanical Engineering from Stanford in 2024. She completed her bachelor’s degree in Mechanical Engineering with a minor in Information and Data Sciences (IDS) at Caltech in 2022. Outside of research, Anjini very much enjoys teaching and educational outreach and leads the seeME (“see Mechanical Engineering”) program at Stanford. She is also an outreach lead on the leadership team for the Stanford Mechanical Engineering Women and Gender Minorities Group (MEGM). In her spare time, Anjini greatly enjoys playing the piano and violin and folding origami models. 

Computational analysis and prediction of turbulent Supercritical carbon dioxide (sCO₂) compressor flows

Supercritical carbon dioxide (sCO₂) has the potential to greatly improve the efficiency and sustainability of modern power cycles. In contrast to more traditional working fluids, like steam, sCO₂ undergoes large variations in density for correspondingly small changes in temperature thus increasing thermodynamic cycle efficiency and using less fuel. It is also less toxic and flammable than other working fluids such as those used in organic Rankine cycles. The favorable thermodynamic and chemical properties of sCO₂ make it an effective working fluid in a variety of applications including waste heat recovery, concentrated solar power, geothermal energy, nuclear power, and fossil-fuel-powered cycles.

Although closed-loop sCO₂ power cycles show promise in efficiently providing cleaner electricity, they have not yet been widely adopted. This is because of their high cost of implementation and the fact that many cycle components have yet to be optimized. The optimization of sCO₂ cycle components remains a challenge both experimentally and computationally given the extreme thermodynamic operating conditions of the cycle. The goal of this project is to enable better computational prediction of the flow conditions and behavior in utility-scale sCO₂ turbomachinery so that it can be optimized for efficient and long-term performance at a large scale.