Acceleration of Chemical Kinetics Computation with the Learned Intelligent Tabulation (LIT) Method

Majid Haghshenas, Peetak Mitra, Niccolò Dal Santo and David P. Schmidt
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Majid Haghshenas: Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA
Peetak Mitra: Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA
Niccolò Dal Santo: MathWorks Inc., Cambridge CB4 0HH, UK
David P. Schmidt: Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA

Energies, 2021, vol. 14, issue 23, 1-15

Abstract: In this work, a data-driven methodology for modeling combustion kinetics, Learned Intelligent Tabulation (LIT), is presented. LIT aims to accelerate the tabulation of combustion mechanisms via machine learning algorithms such as Deep Neural Networks (DNNs). The high-dimensional composition space is sampled from high-fidelity simulations covering a wide range of initial conditions to train these DNNs. The input data are clustered into subspaces, while each subspace is trained with a DNN regression model targeted to a particular part of the high-dimensional composition space. This localized approach has proven to be more tractable than having a global ANN regression model, which fails to generalize across various composition spaces. The clustering is performed using an unsupervised method, Self-Organizing Map (SOM), which automatically subdivides the space. A dense network comprised of fully connected layers is considered for the regression model, while the network hyper parameters are optimized using Bayesian optimization. A nonlinear transformation of the parameters is used to improve sensitivity to minor species and enhance the prediction of ignition delay. The LIT method is employed to model the chemistry kinetics of zero-dimensional H 2 – O 2 and CH 4 -air combustion. The data-driven method achieves good agreement with the benchmark method while being cheaper in terms of computational cost. LIT is naturally extensible to different combustion models such as flamelet and PDF transport models.

Keywords: combustion; kinetics; machine learning; neural network (NN); CFD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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