Ensemble machine learning techniques for particulate emissions estimation from a highly boosted GDI engine fuelled by different gasoline blends
Light-duty vehicle emissions regulations worldwide impose stringent limits on particulate matter (PM) emissions, necessitating accurate modelling and prediction of particulate emissions across a range of sizes (as low as 10 nm). It has been shown that the decision tree-based ensemble machine learnin...
Váldodahkkit: | Stangierska, M, Bajwa, A, Lewis, A, Akehurst, S, Turner, J, Leach, FCP |
---|---|
Materiálatiipa: | Conference item |
Giella: | English |
Almmustuhtton: |
SAE International
2024
|
Geahča maid
-
Sub-23 nm particulate emissions from a highly boosted GDI engine
Dahkki: Leach, F, et al.
Almmustuhtton: (2019) -
Using neural network and random forest algorithmic approaches to predicting particulate emissions from a highly boosted GDI engine
Dahkki: Papaioannou, N, et al.
Almmustuhtton: (2021) -
The effect of fuel composition on particulate emissions from a highly boosted GDI engine – An evaluation of three particulate indices
Dahkki: Leach, F, et al.
Almmustuhtton: (2019) -
Particulate emissions from a highly boosted gasoline direct injection engine
Dahkki: Leach, F, et al.
Almmustuhtton: (2017) -
Particulate emissions from gasoline direct injection engines
Dahkki: Leach, FCP
Almmustuhtton: (2014)