TY - JOUR
T1 - Application and comparison of multiple machine learning techniques for the calculation of laminar burning velocity for hydrogen-methane mixtures
AU - Eckart, Sven
AU - Prieler, René Josef
AU - Hochenauer, Christoph
AU - Krause, Hartmut
PY - 2022/7/1
Y1 - 2022/7/1
N2 - In the present discussion of transition the energy supply and sector coupling processes, hydrogen and hydrogen/natural gas mixtures will play an important role in future gas usage as gaseous energy carrier mainly natural gas is widely used in industrial combustion systems, combustion engines as well as domestic heating systems. Combustion properties of hydrogen differ completely from natural gas. Therefore, numerical modelling of combustion phenomena is an important task due to development and optimization of innovative combustion systems or for safety issues. In this area laminar burning velocity (LBV) is one of the most important physical properties of a flammable mixture. LBV is one of the parameters used for assessment and development of detailed chemical kinetic mechanisms and burners as well. The goal of this work is to develop models by using machine-learning algorithms for predicting laminar burning velocities of methane/hydrogen/air mixtures at different states. Development of the models is based on a large experimental data set with over 1400 data points collected from the literature after 2005. The models are developed in Python taking into account (i) generalized linear regression model (GLM), (ii) support vector machine (SVM), (iii) Random Forest (RF) and (iv) artificial neural network (ANN). The influence of the number of hidden layers and neurons per layer were investigated to find the best possible solution for an ANN. The performance of the developed models was evaluated with one widely used detailed chemical reaction mechanisms. Therefore the GRI 3.0 DRM was used for this purpose in the numerical simulations. The main advantage of developed models is the much shorter computational time compared to the solving procedures for detailed chemical reaction mechanism.
AB - In the present discussion of transition the energy supply and sector coupling processes, hydrogen and hydrogen/natural gas mixtures will play an important role in future gas usage as gaseous energy carrier mainly natural gas is widely used in industrial combustion systems, combustion engines as well as domestic heating systems. Combustion properties of hydrogen differ completely from natural gas. Therefore, numerical modelling of combustion phenomena is an important task due to development and optimization of innovative combustion systems or for safety issues. In this area laminar burning velocity (LBV) is one of the most important physical properties of a flammable mixture. LBV is one of the parameters used for assessment and development of detailed chemical kinetic mechanisms and burners as well. The goal of this work is to develop models by using machine-learning algorithms for predicting laminar burning velocities of methane/hydrogen/air mixtures at different states. Development of the models is based on a large experimental data set with over 1400 data points collected from the literature after 2005. The models are developed in Python taking into account (i) generalized linear regression model (GLM), (ii) support vector machine (SVM), (iii) Random Forest (RF) and (iv) artificial neural network (ANN). The influence of the number of hidden layers and neurons per layer were investigated to find the best possible solution for an ANN. The performance of the developed models was evaluated with one widely used detailed chemical reaction mechanisms. Therefore the GRI 3.0 DRM was used for this purpose in the numerical simulations. The main advantage of developed models is the much shorter computational time compared to the solving procedures for detailed chemical reaction mechanism.
KW - artificial neural network (ANN)
KW - laminar burning velocity (LBV)
KW - Machine learning, hydrogen-methane mixture
UR - http://www.scopus.com/inward/record.url?scp=85129493527&partnerID=8YFLogxK
U2 - 10.1016/j.tsep.2022.101306
DO - 10.1016/j.tsep.2022.101306
M3 - Article
SN - 2451-9049
VL - 32
JO - Thermal Science and Engineering Progress
JF - Thermal Science and Engineering Progress
M1 - 101306
ER -