Dr. Janardhan Kodavasal is a Mechanical Engineer at Argonne’s Center for Transportation Research. His research focuses on applying Machine Learning and High-Performance Computing to accelerate product design. Janardhan led the development of a machine learning genetic algorithm (ML-GA) software pipeline that reduces design time from months to days. As part of Argonne’s 2018 Launchpad cohort, he is developing a program focused on “New Areas in Applying Machine Learning and Big Data to Simulation-Driven Design for Propulsion Applications.” He helped develop a method to run thousands of high-fidelity engine simulations on Argonne’s supercomputer, aimed at significantly reducing engine design time.
As a member of the Virtual Engine Research Institute and Fuels Initiative (VERIFI) team at Argonne, Janardhan leads various projects related to data science, advanced engine concepts, cyclic variability in spark-ignited engines, and engine design through computer simulation. He collaborates with multiple groups within the lab, such as the Argonne Leadership Computing Facility, the Mathematics & Computer Science division, and the Chemical Sciences & Engineering division. Additionally, he has helped develop collaborative projects with several industry partners as well.
Memberships, Service and Awards
- Society of Automotive Engineers (SAE)
- Session Chair for Homogeneous Charge Compression Ignition and Spark Assisted Compression Ignition sessions at multiple SAE conferences
- Research Poster Award, ISC 2015, Frankfurt, Germany
- Ph.D., Mechanical Engineering, University of Michigan, Ann Arbor, MI
- M.S.E., Aerospace Engineering, University of Michigan, Ann Arbor, MI
- M.S.E., Mechanical Engineering, University of Michigan, Ann Arbor, MI
- B.Tech, Mechanical Engineering, National Institute of Technology Karnataka, India
- Awardees announced for Cohort 2 of Launchpad Program
- HPC revs up engine designs
- Engine design takes a major leap at Argonne
- VERIFI code optimization yields three-fold increase in engine simulation speed
Machine Learning, Data Science, Energy, Energy Efficiency, Vehicles, Supercomputing, Internal Combustion Engines, High Performance Computing, Advanced Engine Concepts