Modelling Medium Speed Diesel Engine Combustion, Soot and NOx-emission Formations

Taskinen, Pertti

doi:10.4271/2000-01-1886

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Regarding the estimation of NOx emissions, domestic and foreign scholars have done a lot of research and put forward a variety of methods to build a prediction model, such as Taskinen [4] using combustion simulation software KIVA-2 to establish the NOx emission prediction model of medium-speed diesel engines. This method has a strong explanation for NOx generation, but the modeling method is complicated, the input parameters are more, the requirements for computing resources and data are higher, and it is not suitable for remote monitoring needs.…”

Section: Introductionmentioning

confidence: 99%

Remote NO_x emission prediction model based on LSTM neural network

Wang

Cheng

et al. 2021

E3S Web Conf.

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Test results from many researchers show that NOx emission from many on-broad heavy-duty diesel vehicles is higher than which been registered. Therefore, CN_VI emission regulations clearly proposes that the heavy-duty diesel vehicles should be supervised by a T-BOX which can transmit CAN message from vehicle OBD interface to the remote monitoring platform. Based on the formation mechanism of NOx emission and the variety of OBD data flow, the LSTM (Long Short-Term Memory) neural network model inputs such as engine speed, torque, atmospheric pressure, coolant temperature, fuel consumption rate and intake air mass flow are selected by using partial least square method (PLS). 19877 groups of data from engine test results were used for model training and verification, the root mean square error of training and test are RTR = 29.7 × 10-6 and RTE = 19.9 × 10-6,with a high prediction accuracy which can fully meet the requirements of the SCR system DeNOx performance diagnosis module in the OBD remote monitoring system.

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Section: Introductionmentioning

confidence: 99%

Remote NO_x emission prediction model based on LSTM neural network

Wang

Cheng

et al. 2021

E3S Web Conf.

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“…[9][10][11][12] Even though more sophisticated models have been proposed, [13][14][15][16][17][18][19][20][21][22] CFD modeling of IC engines still relies on extremely simplified submodels, both for combustion and for pollutants such as unburned components, SO x , and NO x . [23][24][25][26] This paper concerns the modeling of in-cylinder NO x (NO + NO 2 ) of low-and medium-speed CIE. In this engine, the fluid motion and its time-scale are such that the formed NO 2 efficiently reduces back to NO, which turns to be the only contributor to NO x .…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, to date, no experimental technique has been able to clarify all relevant chemical reactions, − and for a more detailed understanding of the chemical processes, investigators have turned to computer models. However, such models are often based on simplistic assumptions concerning both the fuel and the process itself. − Even though more sophisticated models have been proposed, − CFD modeling of IC engines still relies on extremely simplified submodels, both for combustion and for pollutants such as unburned components, SO x , and NO x . − …”

Section: Introductionmentioning

confidence: 99%

Improved NO_x Submodel for In-Cylinder CFD Simulation of Low- and Medium-Speed Compression Ignition Engines

Zabetta

Kilpinen

2001

Energy Fuels

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The NO x submodels currently used in the CFD-based simulation of engines have been developed for conditions where homogeneous and stoichiometric mixtures of fuel and air are burned. Such conditions apply well for spark ignition engines (SIE) but not for compression ignition engines (CIE), where combustion takes place in a highly heterogeneous environment. As a consequence, current NO x submodels do not satisfactorily describe the fate of nitrogen oxides in CIE. The aim of this work was to determine, by detailed chemical kinetic investigation, the mechanisms leading to in-cylinder NO x in low-and medium-speed CIE and to develop a more accurate submodel for the prediction of NO x in CIE by means of CFD. Calculations at constant pressure (1-150 bar) and constant temperature (1500-2200 °C) under ideal plug flow conditions show that, relative to the proportion of NO, the proportion of NO 2 in NO x is negligible and that the formation and destruction of NO occurs mainly via 10 reactions. These reactions can be organized into three NO mechanisms: thermal, N 2 O intermediate, and N 2 O extension, the latter being a set of five reactions that oxidize N 2 O to NO via NH and HNO intermediates. To our knowledge, the importance of the N 2 O-extension mechanism is reported here for the first time. An improved NO x submodel was developed taking into account all three mechanisms, after introducing approximations relevant to CIE. According to our kinetic investigations, partial equilibrium approximation can be applied to O, OH, and H and a quasi-steady-state approximation to N 2 O, N, NH, and HNO. The performance of the improved submodel is illustrated by comparing its NO x predictions with those of a detailed kinetic scheme and a NO submodel currently used in CFD. The comparison shows that the improved submodel always produces more accurate predictions of NO x than does the current submodel.

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Assessment of NOx Destruction in Diesel Engines by Injecting NO in the Intake Manifold

Vellaisamy¹,

Clark²,

Thompson³

et al. 2005

SAE Technical Paper Series

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Modelling Medium Speed Diesel Engine Combustion, Soot and NOx-emission Formations

Cited by 3 publications

References 24 publications

Remote NO_x emission prediction model based on LSTM neural network

Remote NO_x emission prediction model based on LSTM neural network

Improved NO_x Submodel for In-Cylinder CFD Simulation of Low- and Medium-Speed Compression Ignition Engines

Assessment of NOx Destruction in Diesel Engines by Injecting NO in the Intake Manifold

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Modelling Medium Speed Diesel Engine Combustion, Soot and NOx-emission Formations

Cited by 3 publications

References 24 publications

Remote NOx emission prediction model based on LSTM neural network

Remote NOx emission prediction model based on LSTM neural network

Improved NOx Submodel for In-Cylinder CFD Simulation of Low- and Medium-Speed Compression Ignition Engines

Assessment of NOx Destruction in Diesel Engines by Injecting NO in the Intake Manifold

Contact Info

Product

Resources

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Remote NO_x emission prediction model based on LSTM neural network

Remote NO_x emission prediction model based on LSTM neural network

Improved NO_x Submodel for In-Cylinder CFD Simulation of Low- and Medium-Speed Compression Ignition Engines