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...
Үндсэн зохиолчид: | Stangierska, M, Bajwa, A, Lewis, A, Akehurst, S, Turner, J, Leach, FCP |
---|---|
Формат: | Conference item |
Хэл сонгох: | English |
Хэвлэсэн: |
SAE International
2024
|
Ижил төстэй зүйлс
Ижил төстэй зүйлс
-
Sub-23 nm particulate emissions from a highly boosted GDI engine
-н: Leach, F, зэрэг
Хэвлэсэн: (2019) -
Using neural network and random forest algorithmic approaches to predicting particulate emissions from a highly boosted GDI engine
-н: Papaioannou, N, зэрэг
Хэвлэсэн: (2021) -
The effect of fuel composition on particulate emissions from a highly boosted GDI engine – An evaluation of three particulate indices
-н: Leach, F, зэрэг
Хэвлэсэн: (2019) -
Particulate emissions from a highly boosted gasoline direct injection engine
-н: Leach, F, зэрэг
Хэвлэсэн: (2017) -
Particulate emissions from gasoline direct injection engines
-н: Leach, FCP
Хэвлэсэн: (2014)