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Prediction of ECN Spray—A Characteristics Using Machine Learning
Technical Paper
2022-01-0494
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Flame lift-off length (FLOL), ignition delay time (IDT), liquid length (LL), and Soot are essential parameters defining spray combustion characteristics. They help understand the combustion dynamics and validate the spray and combustion models for numerical simulations. However, obtaining extensive data from experiments is costlier and time-consuming. Machine learning (ML) models have advanced to the point where they could create efficient models that could be used as surrogates for experiments. In this study, five different ML algorithms have been trained using the experimental dataset available through the engine combustion network (ECN) community. A novel genetic algorithm-based hyperparameter optimization code has been used to optimize the models to improve prediction accuracy. The model performances were compared, and the better model was chosen as an experimental surrogate to predict FLOL, IDT, LL, and Soot.
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Citation
Mohan, B. and Badra, J., "Prediction of ECN Spray—A Characteristics Using Machine Learning," SAE Technical Paper 2022-01-0494, 2022, https://doi.org/10.4271/2022-01-0494.Also In
References
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