Cooperation agreements, Contests / awards

Dr. Sridhar Chellappa is a postdoctoral researcher in the research group Computational Methods in Systems and Control Theory (Head: Prof. Dr. rer. nat. Peter Benner) at the Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg. He successfully completed his Ph. D. thesis on A Posteriori Error Estimation and Adaptivity for Model Order Reduction of Large-Scale Systems, supervised by Dr. Lihong Feng (Team Leader Model Order Reduction) and Prof. Dr. rer. nat. Peter Benner, in September 2022.

Computer simulations are essential for modeling and understanding physical phenomena in many areas of science and technology - be it on a small scale to understand electromagnetic radiation, e.g. in the development of new smartphones, to simulate the service lifetime of new batteries for electric cars, or be it on a large scale to test the functioning and aerodynamics of aircrafts or of vehicles or to simulate large-scale chemical processes.

Such extensive simulations are very time-consuming. With the help of model order reduction (MOR) of dynamical systems, computer simulations can be accelerated by taking advantage of some mathematical properties. The results of model order reduction, known as reduced-order models (ROMs), can be simulated very quickly while still being accurate and providing significant results.

In his research work, Sridhar Chellappa has proposed new adaptive methods that substantially reduce the computing time of MOR. His dissertation focused on efficient error estimation, adaptivity of model updates and parameter sampling. The driving force behind the adaptive methods is the concept of a posteriori error estimation, which states how close the ROM is to the ground truth exact solution. This knowledge can be used in a clever way to obtain ROMs iteratively and quickly.

Consideration of Chemical Processes such as Batch Chromatography and Fluidized Bed Crystallization

Sridhar Chellappa investigated process engineering systems such as batch chromatography, which is used to separate chemical compounds. Such processes are being researched at the Max Planck Institute in Magdeburg and used in many branches of industry, for example in pharmaceutical and food production.

He also applied MOR techniques to fluidized bed crystallization, a process to obtain pure crystals of a particular chemical compound.

The application of adaptive model order reduction techniques led to a reduction in computation time of at least 30 percent. In addition, the adaptive sampling techniques he used yielded reduced-order models that generalized well and captured the overall physical behavior of the system very well.

This makes model order reduction more accessible than ever to current engineering problems in order to create compact simulation models that can be evaluated quickly.

About Dr. rer. nat. Sridhar Chellappa

Sridhar Chellappa was born in Chennai, India, in 1992. He studied Technology, Electrical and Electronics Engineering (B. Tech.) at SASTRA University, India, from 2009 to 2013. He obtained his Master's degree in 2016 after studying Electrical Engineering at the University of Oviedo, Spain. He gained practical, applied experience in mathematical modeling while writing his Bachelor's thesis at Alstom Grid in Chennai, India, and during his Master's thesis at Robert Bosch GmbH in Renningen, Germany. He has been a researcher at the Max Planck Institute Magdeburg since 2017 and was a Ph.D. student at the International Max Planck Research School for Advanced Methods in Process and Systems Engineering Magdeburg until 2022.

Sridhar Chellappa is currently working at the Max Planck Institute Magdeburg on hybrid model reduction approaches combining machine learning techniques and model order reduction applied to cardiac electromechanics.

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Weitere Informationen:
https://www.mpi-magdeburg.mpg.de/4457740/2023-11-22-pm-mpi-magdeburg-chellappa-ovgu-promotionspreis