SGD-based optimization in modeling combustion kinetics: Case studies in tuning mechanistic and hybrid kinetic models
Name
DP_Optimization.pdf
Description
Accepted version
Size
1.04 MB
Format
Adobe PDF
Checksum (MD5)
ea6dc570e0514f72bf1710d2ab45cb3e
Author(s)
Ji, Weiqi
Su, Xingyu
Pang, Bin
Li, Yujuan
Ren, Zhuyin
Deng, Sili
Date Issued
September 2022
Journal
Fuel
Publisher
Elsevier BV
Citation
Ji, Weiqi, Su, Xingyu, Pang, Bin, Li, Yujuan, Ren, Zhuyin et al. 2022. "SGD-based optimization in modeling combustion kinetics: Case studies in tuning mechanistic and hybrid kinetic models." Fuel, 324.
Version
Author's final manuscript
Abstract
Chemical kinetic modeling is an integral part of combustion simulation, and extensive efforts have been devoted to developing high-fidelity yet computationally affordable models. Despite these efforts, modeling combustion kinetics is still challenging due to the demand for expert knowledge and high dimensional optimization against experiments. Therefore, data-driven approaches that enable efficient discovery and calibration of kinetic models have received much attention in recent years, the core of which is the high-dimensional optimization based on big data. Evolutionary algorithms are usually adopted for optimizing chemical kinetic models, although they usually suffer from high computational costs and are limited to a small number of parameters. Meanwhile, gradient-based optimizations, especially the stochastic gradient descent (SGD) methods, have shown success in developing complex models by training large-scale deep learning models. Therefore, this work explores the applications of SGD-based optimizations in tuning mechanistic kinetic models and learning hybrid kinetic models. We first showed that SGD-based optimizations could substantially save computational cost compared to evolutionary algorithms when the number of kinetic parameters in mechanistic models reached about one hundred. We then demonstrated that the SGD-based optimization enabled us to use a neural network model to represent the pyrolysis of the Hybrid Chemistry and optimize the associated hundreds of weights in the neural network. These proof-of-concept studies showed that the SGD-based optimization is more efficient than evolutionary algorithms, is a promising approach for developing chemical kinetic models with high dimensional parameters, and is capable of developing hybrid mechanistic-machine learning kinetic models.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.fuel.2022.124560
