Beyond Traditional Modeling: Deep Learning Solutions for Turbulent Combustion Simulations by Dr Shubhangi Bansude | Department of Energy Science and Engineering

Location and Date:

25 Sep 2024 (Wednesday), 4 PM, DESE seminar Room, 2nd Floor

Abstract - The large eddy simulation (LES) of turbulent combustion is pertinent to numerous applications of practical importance such as internal combustion engines, gas turbines, industrial furnaces, and propulsion systems. This talk will briefly explore the role of LES in advancing future combustion technologies aimed at decarbonization, such as hydrogen gas turbines and rotating detonation engines. These simulations are inherently complex owing to the intricacies of turbulent flow, chemical kinetics, and the interaction between them. In LES, only the large scales of the flow field are resolved, while motions at the small scales, generally referred to as subgrid scales (SGS), are modeled. SGS models based on transported Filtered Density Function (FDF) approaches are attractive because they provide closed-form representations of the highly non-linear chemical source terms in transport equations. However, applying these models to realistic flame calculations is challenging due to the high costs associated with numerically integrating the stiff finite-rate chemical kinetics ordinary differential equations (ODEs). The first part of this talk focuses on a data-driven framework that employs neural ODEs (NODE) to speed up the integration of stiff chemistry. The performance of this approach is demonstrated for hydrogen-air combustion in a pairwise mixing stirred reactor (PMSR) with varying mixing timescales.

An alternative to the high-fidelity transported FDF approach is the low-fidelity moments method, which reduces the chemistry description to one or two characteristic variables and incorporates SGS statistics through presumed FDF. One example of this is non-premixed flames with infinitely fast chemistry, where the thermochemical state depends solely on mixture fraction. The second part of the talk focuses on deep neural network (DNN) models for predicting the FDF of mixture fraction in variable density 3-D mixing layers. The performance of the DNN-FDF model is seen to be more accurate than the conventional beta FDF model based on the appraisal compared to high-fidelity simulation predictions from direct numerical simulation (DNS) and transported FDF.

About the Speaker: Dr. Shubhangi Bansude is a Postdoctoral Appointee at Argonne National Laboratory, USA. She earned her Ph.D. in Mechanical Engineering from the School of Aerospace, Mechanical, and Manufacturing at the University of Connecticut (UConn) in 2023. She received her B.Tech from the Indian Institute of Technology Gandhinagar in 2014. Prior to her Ph.D., she worked as an Application Engineer at Siemens PLM Software, where she contributed to a range of computational fluid dynamics (CFD) projects in the various automotive and OEM industries. Shubhangi has also served as a Graduate Teaching Fellow for the academic year 2020-21 in the Department of Mechanical Engineering at UConn. She was a recipient of the General Electric (GE) Graduate Fellowship for Innovation in 2020. Shubhangi’s research interests lie in the areas of CFD, physics-informed deep learning, hydrogen combustion, and turbulent combustion simulations with a special focus on Large Eddy Simulations (LES).