A Random Forest Algorithmic Approach to Predicting Particulate Emissions from a Highly Boosted GDI Engine
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- Format
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- Non-members (tax incl.)：￥6,600 Members (tax incl.)：￥5,280
- Paper/Info type
- SAE Paper
No.2021-24-0076
- Pages
- 1-9(Total 9 p)
- Date of publication
- Sep 2021
- Publisher
- SAE International
- Language
- English
- Event
- International Conference on Engines and Vehicles 2021
Detailed Information
| Author(E) | 1) Nick Papaioannou, 2) Xiaohang Fang, 3) Felix Leach, 4) Andrew Lewis, 5) Sam Akehurst, 6) James Turner |
|---|---|
| Affiliation(E) | 1) University of Oxford, 2) University of Oxford, 3) University of Oxford, 4) University of Bath, 5) University of Bath, 6) KAUST |
| Abstract(E) | Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out. The Random Forest algorithm is used as an estimator and the various engine parameters are ranked with a permutation feature importance technique using mean squared error as a performance metric. The results showed that from 82 model parameters only 17 are important for predicting the above PN emission parameters. Moreover, the permutation importance algorithm showed that when the parameters are reduced to 9 the model accuracy is improved due to a reduction in model variance. Overall, the model shows excellent predictive performance for all three parameters even when an independent dataset is used. |
