Efficient modeling of the filtered density function in turbulent sprays using ensemble learning

An efficient ensemble learning approach is used for modeling the filtered density function (FDF) of mixture fraction in turbulent evaporating sprays. This is achieved by implementing the state-of-the-art eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms. T...

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Bibliographic Details
Published in:Combustion and flame 2022-03, Vol.237, p.111722, Article 111722
Main Authors: Yao, S., Kronenburg, A., Stein, O.T.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Density
Ensemble learning
Machine learning
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Summary:An efficient ensemble learning approach is used for modeling the filtered density function (FDF) of mixture fraction in turbulent evaporating sprays. This is achieved by implementing the state-of-the-art eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms. The results show that ensemble learning models achieve a very high accuracy that is comparable to a deep neural network. Computational requirements are, however, much reduced and of the order of those needed for the computation of a conventional β-FDF. Ensemble learning thus provides a suitable alternative to model FDF statistics and corresponding software for training and a C++ model library are provided.
ISSN:0010-2180
1556-2921
DOI:10.1016/j.combustflame.2021.111722