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...
Egile Nagusiak: | Stangierska, M, Bajwa, A, Lewis, A, Akehurst, S, Turner, J, Leach, FCP |
---|---|
Formatua: | Conference item |
Hizkuntza: | English |
Argitaratua: |
SAE International
2024
|
Antzeko izenburuak
-
Sub-23 nm particulate emissions from a highly boosted GDI engine
nork: Leach, F, et al.
Argitaratua: (2019) -
Using neural network and random forest algorithmic approaches to predicting particulate emissions from a highly boosted GDI engine
nork: Papaioannou, N, et al.
Argitaratua: (2021) -
The effect of fuel composition on particulate emissions from a highly boosted GDI engine – An evaluation of three particulate indices
nork: Leach, F, et al.
Argitaratua: (2019) -
Particulate emissions from a highly boosted gasoline direct injection engine
nork: Leach, F, et al.
Argitaratua: (2017) -
Particulate emissions from gasoline direct injection engines
nork: Leach, FCP
Argitaratua: (2014)