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Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements

Wan, K. and Hartl, S. and Vervisch, L. and Domingo, P. and Barlow, R. S. and Hasse, C. (2020):
Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements.
In: Combustion and Flame, 219, pp. 268 - 274. ISSN 0010-2180,
DOI: 10.1016/j.combustflame.2020.05.024,
[Article]

Abstract

A combustion regime identification based on convolutional neural networks (CNNs) is developed using the recently proposed gradient-free regime identification (GFRI) approach applied to two turbulent CH4/air jet flames featuring multi-regime characteristics. The training and the subsequent application of the CNN rely on the processing of one-dimensional Raman/Rayleigh line measurements of species mass fractions and temperature (CNN input). The combustion regime index is then readily predicted at every point along the measured line (CNN output). For training the neural network, the combustion regime index is first determined using the GFRI method (Hartl et al., 2018) based on the chemical explosive mode analysis (CEMA). Six classes of combustion regimes, including premixed (P), dominantly premixed (DP), multi-regime (MR), dominantly non-premixed (DNP), non-premixed (NP), and lean back-supported (LBS), are well detected by the trained CNN, with a pixel-wise accuracy of more than 85 for burner operating conditions unseen during training (different free-stream equivalence ratios). The quasi instantaneous neural network response provides a perspective towards real-time global combustion regime identification for pollutants emission control. From the results, it is also concluded that introducing physical insight, by combining advanced experimental (Raman/Rayleigh line measurements) and numerical analysis (GFRI), allows for reducing the amount of data needed to train neural networks.

Item Type: Article
Erschienen: 2020
Creators: Wan, K. and Hartl, S. and Vervisch, L. and Domingo, P. and Barlow, R. S. and Hasse, C.
Title: Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements
Language: English
Abstract:

A combustion regime identification based on convolutional neural networks (CNNs) is developed using the recently proposed gradient-free regime identification (GFRI) approach applied to two turbulent CH4/air jet flames featuring multi-regime characteristics. The training and the subsequent application of the CNN rely on the processing of one-dimensional Raman/Rayleigh line measurements of species mass fractions and temperature (CNN input). The combustion regime index is then readily predicted at every point along the measured line (CNN output). For training the neural network, the combustion regime index is first determined using the GFRI method (Hartl et al., 2018) based on the chemical explosive mode analysis (CEMA). Six classes of combustion regimes, including premixed (P), dominantly premixed (DP), multi-regime (MR), dominantly non-premixed (DNP), non-premixed (NP), and lean back-supported (LBS), are well detected by the trained CNN, with a pixel-wise accuracy of more than 85 for burner operating conditions unseen during training (different free-stream equivalence ratios). The quasi instantaneous neural network response provides a perspective towards real-time global combustion regime identification for pollutants emission control. From the results, it is also concluded that introducing physical insight, by combining advanced experimental (Raman/Rayleigh line measurements) and numerical analysis (GFRI), allows for reducing the amount of data needed to train neural networks.

Journal or Publication Title: Combustion and Flame
Journal volume: 219
Uncontrolled Keywords: Convolutional neural network, Combustion regime, Raman/Rayleigh, Flame regime marker, Gradient-free regime identification
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Simulation of reactive Thermo-Fluid Systems (STFS)
Profile Areas
Profile Areas > Thermo-Fluids & Interfaces
Date Deposited: 22 Jun 2020 05:43
DOI: 10.1016/j.combustflame.2020.05.024
Official URL: https://doi.org/10.1016/j.combustflame.2020.05.024
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