Prediction of ECN Spray—A Characteristics Using Machine Learning

2022-01-0494

03/29/2022

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-01-0494
Pages
8
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.
Additional Details
Publisher
Published
Mar 29, 2022
Product Code
2022-01-0494
Content Type
Technical Paper
Language
English