Integration of Diesel Engine, Exhaust System, Engine Emissions and Aftertreatment Device Models

Kapparos, David J.; Brahma, Indranil; Strzelec, Andrea; Rutland, Christopher J.; Foster, David E.

doi:10.4271/2005-01-0947

Cited by 12 publications

(2 citation statements)

References 13 publications

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“…The 0D DPF model used in this study is validated 1 by comparison with the integrated system-level model (ISM) which has been previously developed and continuously validated 18,[30][31][32][33][34] by several researchers. The ISM integrates the one-dimensional (1D) DOC model and 1D DPF model together with the engine model, emissions models, and advanced controller model.…”

Section: Model Validationmentioning

confidence: 99%

Modeling uncontrolled regeneration of diesel particulate filters, taking into account hydrocarbon slip

Lee

Rutland

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

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The effect of hydrocarbon slip from a diesel oxidation catalyst on active regeneration of a diesel particulate filter is investigated by developing a generalized zero-dimensional diesel particulate filter model to predict the wall temperature of the diesel particulate filter and the trapped mass of particulate matter. Exhaust fuel injection methodology is applied to provide a high temperature for regeneration by oxidation in the diesel oxidation catalyst. However, the diesel oxidation catalyst reactions may be incomplete and hydrocarbon slip can occur. It is confirmed that two exothermic reactions, namely soot oxidation and hydrocarbon oxidation, which proceed simultaneously may result in the synergistic effect of a rise in the temperature. This is primarily because the temperature is the common factor that affects both reaction rates, i.e. a rise in the temperature caused by one reaction can affect the increase in the reaction rate of another reaction through the exotherms created. Accurate prediction of the temperature rise during active regeneration of the diesel particulate filter is dependent on several crucial factors: the initial wall temperature of the diesel particulate filter, the inlet temperature of the diesel particulate filter, the initial loading level of particulate matter, the exhaust mass flow rate, the oxygen concentration, and hydrocarbon slip. Several simulations revealed that hydrocarbon slip can be one of the reasons for uncontrolled regeneration of the diesel particulate filter. It is concluded that, if there is a considerable amount of hydrocarbon slip, then uncontrolled regeneration of the diesel particulate filter easily occurs when the exhaust mass flow rate is rapidly reduced to the idle condition during regeneration. Flow transient simulations also confirmed that the temperature ramp-up rate and the point of time when the exhaust mass flow rate is reduced determine the overall trend of the temperature rise and the characteristics of the thermal behavior of the diesel particulate filter.

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Section: Model Validationmentioning

confidence: 99%

Modeling uncontrolled regeneration of diesel particulate filters, taking into account hydrocarbon slip

Lee

Rutland

2012

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Their model was capable of predicting particle distribution with the absolute square mean error of 3-7%. Ample evidence could be found in the literature in relation to application of neural network for predicting the behaviours of diesel particulate filter [37], NOx and soot emissions in diesel engine [38], prediction of emission levels using cylinder pressure [39][40][41] from diesel engines, cylinder pressure, NOx and CO 2 from gasoline engine [42] and neural network for CI and SI engines for predicting mainly emissions [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. This is not an exhaustive list but a few very studies relevant to current work.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

Reddy

Samuel

2017

Applied Thermal Engineering

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Predicting the amount of combustion generated nano-scale particulate matter (PM) emitted by gasoline direct injection (GDI) is a challenging task, but immensely useful for engine calibration engineers in order to meet the stringent emission legislation norms. The present work aimed to link the in-cylinder combustion with engine-out nano-scale PM for the size range of 23.7 to 1000 nm diameter. Neural network with a single hidden layer using first 8 principal components of cylinder pressure was employed for training and predicting the number of nano-scale PM number count. Using a systematic computational approach and comparing its results with experimental data this work demonstrates that machine-learning approach based on neural network is sufficient for predicting engine out nano-scale PM count as a function of engine load and speed.

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Optimization algorithm for diesel engine operating parameters based on a vehicle driving test cycle

et al. 2013

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Integration of Diesel Engine, Exhaust System, Engine Emissions and Aftertreatment Device Models

Cited by 12 publications

References 13 publications

Modeling uncontrolled regeneration of diesel particulate filters, taking into account hydrocarbon slip

Modeling uncontrolled regeneration of diesel particulate filters, taking into account hydrocarbon slip

Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

Optimization algorithm for diesel engine operating parameters based on a vehicle driving test cycle

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