Information-Driven and Thermodynamics-Based Approaches to High-Efficiency, Low End-Use Energy Systems by Dr Sankaran Ramakrishnan

Location and Date: 
Wednesday, 22 Jan 2020, 11:30 AM to 12:30 pm, Seminar Hall, Second Floor, DESE-CESE Building
Efficient energy conversion reduces life-cycle emissions and the costs of energy technologies. Reducing end-use energy demand also reduces emissions, costs, and energy storage needs. Lower cost of energy storage is particularly important to increase the adoption of renewable electricity generation and electric transportation.  Traditionally, efforts towards higher efficiency and lower energy-use have been driven by improved device design. Recent advances in our ability to collect and analyze real-world data, allows us to consider opportunities and challenges from patterns in energy use behavior. In this talk, I will present my work on thermodynamic and data-driven approaches to seek higher efficiencies and lower energy use in electricity generation and transportation.
Thermodynamics defines the efficiency limits for energy systems and has guided energy system research for well over a century. However, have we identified the best thermodynamic designs for most systems of interest? How do we best integrate multiple energy systems to meet the simultaneous needs of the power, heating, cooling, water, and transportation? Towards answering such questions, I will first present a systematic thermodynamic optimization approach. Applied to gas turbine engines, I’ll show how efficiency gains can be achieved over currently known gas turbine architectures. Second, I will present data-driven models to analyze transportation energy use. These models have been used to develop accurate energy-use predictions and design optimal charging infrastructure for electric vehicles. Finally, I will motivate directions for future research that will combine data-driven models of energy use and concepts of efficient thermodynamic design to develop optimal ways to meet urban energy and water needs. I will also highlight how such an approach allows us to develop country-, city-, or individual-specific recommendations.

Sankaran Ramakrishnan is a postdoctoral associate at the Institute of Data, Systems, and Society at MIT. He completed his PhD in mechanical engineering from Stanford University. Sankaran’s research interests are in identifying pathways to high efficiency in energy conversion, storage, and use, through optimal design and integration of energy technologies. He develops models and employs methods from thermodynamics, optimization, and data science in his research.