Combustion kinetic model development using surrogate model similarity method

Abstract

An ideal combustion kinetic model needs to be validated by different experimental targets over a wide range of temperatures and pressures that represent operating conditions in real engines. However, conditions of laboratory experiments for model validation are often limited by the constraint of experimental techniques. In order to improve model predictions under certain conditions (for example, at a relatively higher pressure), it is often needed to use the experimental data obtained under other conditions. In this work, the surrogate model similarity (SMS) method is proposed to find the experimental conditions or targets for model optimisation under certain conditions where the experiments are hard to be conducted. The similarity coefficient is calculated by the cosine similarity between the characteristic coefficients (vectors) of the High Dimensional Model Representation (HDMR) models for different model predictions. A larger similarity coefficient represents a closer relationship between two model predictions. The experimental data with larger similarity coefficients could be more effective to model uncertainty reduction under the concerned conditions. To demonstrate this method, simulations were conducted for two selected combustion systems with hydrogen or methanol as the fuel. In addition to its strength in available experimental data selection for model optimization, this method can be used to screen out experimental targets with strong constraint effect beforehand, thus providing an effective way to maximise utilisation of experimental resources.

Keywords:

• combustion kinetic model
• model optimisation
• surrogate model similarity
• HDMR
• experimental design

Acknowledgements

This study is supported by the National Natural Science Foundation of China [91741109 and 91541113].

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed at https://doi.org/10.1080/13647830.2018.145460.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 91741109 and 91541113].