Application of the SRM Engine Suite over the Entire Load-Speed Operation of a U.S. EPA Tier 4 Capable IC Engine

Bernard, Guillaume; Scaife, Mark; Bhave, Amit; Ooi, David; Dizy, Julian

doi:10.4271/2016-01-0571

Cited by 20 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the field of Compression Ignition engines, the SRM approach was shown to be able to predict the in-cylinder pressure trace and heat release rate fairly well [21], [22]. Similar good performance has been reported for predicting NOx emissions [1], [18]. However, predicting HC, CO and Pn is a still challenge [23], [24].…”

Section: Use Of Probability Density Function In Thermodynamic Modelssupporting

confidence: 58%

“…The work in this paper is concerned with the evaluation of the capability of CMCL SRM environment for modelling of Diesel engine combustion, with a particular focus on NOx emissions prediction. The review of the literature has identified SRM as capable of modelling in-cylinder conditions (pressure trace and heat release) [21] & [22], and predict fairly well NOx emissions [1] & [18]. This work is based on a Diesel engine case study, with the following research aims defined in relation to the SRM engine simulation:…”

Section: Methodsmentioning

confidence: 99%

“…PDF approaches have attracted attention from both the academic research and the industrial application domains, as they offer satisfactory prediction level of the in-cylinder mixture composition and temperature, requiring relatively low computational and time expense. Significant work has been reported on the use of the PDF-SRM approach for Compression Ignition (diesel) engines in [1], [18], [21] - [25], gasoline direct injection engine in [25] - [27] and the Homogeneous Charge Compression Ignition engines in [4], [5], [28]- [32]. Within the field of Compression Ignition engines, the SRM approach was shown to be able to predict the in-cylinder pressure trace and heat release rate fairly well [21], [22].…”

Section: Use Of Probability Density Function In Thermodynamic Modelsmentioning

confidence: 99%

“…The PDF approaches have attracted attention from both the academic research and the industrial application domains, as they offer satisfactory prediction level of the in-cylinder mixture composition and temperature, requiring relatively low computational and time expense. Significant work has been reported on the use of the PDF-SRM approach for compression ignition (Diesel) engines in Bernard et al, 1,18 Mauviot et al, 21 Bordet et al, 22 Pasternak et al, 23 Franken and Mauss 24 and Smallbone et al 25 ; gasoline direct injection engine in Smallbone et al, 25 Pasternak et al 26 and Themi 27 and the HCCI engines in Mosbach et al, 4,5 Su et al, 28 Bhave et al, 29 Aldawood et al 30 and Mosbach et al 31,32 Within the field of compression ignition engines, the SRM approach was shown to be able to predict the in-cylinder pressure trace and heat release rate fairly well. 21,22 Similar good performance has been reported for predicting NO x emissions.…”

Section: Review Of Related Literature On Thermodynamic Engine Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application

Korsunovs

Campean

Pant

et al. 2019

International Journal of Engine Research

View full text Add to dashboard Cite

Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NO x emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of both in-cylinder conditions and NO x output. The approach proposed in this article introduces an automatic stochastic reactor engine model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal stochastic reactor engine model parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NO x emission predictions error. The results from the case study provide a valuable insight into the effectiveness of the stochastic reactor engine model, showing good capability for NO x emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.

show abstract

Section: Use Of Probability Density Function In Thermodynamic Modelssupporting

confidence: 58%

Section: Methodsmentioning

confidence: 99%

Section: Use Of Probability Density Function In Thermodynamic Modelsmentioning

confidence: 99%

Section: Review Of Related Literature On Thermodynamic Engine Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application

Korsunovs

Campean

Pant

et al. 2019

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…6,7 More recently the SRM was applied for the simulation of a diesel engine. 8 In our previous works, a variant of the SRM for the simulation of direct injection (DI) engines (direct-injection stochastic reactor model (DI-SRM)) was applied for the simulation of diesel engine performance parameters, 9,10 fuel effects and exhaust emissions. [11][12][13] The time dependence of the mean mixing time within the engine, along with the method of its determination via an extended heat release analysis, was addressed in Pasternak and colleagues.…”

Section: Introductionmentioning

confidence: 99%

Diesel engine performance mapping using a parametrized mixing time model

Pasternak

Mauß

Klauer³

et al. 2017

International Journal of Engine Research

View full text Add to dashboard Cite

A numerical platform is presented for diesel engine performance mapping. The platform employs a zero-dimensional stochastic reactor model for the simulation of engine in-cylinder processes. n-Heptane is used as diesel surrogate for the modeling of fuel oxidation and emission formation. The overall simulation process is carried out in an automated manner using a genetic algorithm. The probability density function formulation of the stochastic reactor model enables an insight into the locality of turbulence-chemistry interactions that characterize the combustion process in diesel engines. The interactions are accounted for by the modeling of representative mixing time. The mixing time is parametrized with known engine operating parameters such as load, speed and fuel injection strategy. The detailed chemistry consideration and mixing time parametrization enable the extrapolation of engine performance parameters beyond the operating points used for model training. The results show that the model responds correctly to the changes of engine control parameters such as fuel injection timing and exhaust gas recirculation rate. It is demonstrated that the method developed can be applied to the prediction of engine load-speed maps for exhaust NO x , indicated mean effective pressure and fuel consumption. The maps can be derived from the limited experimental data available for model calibration. Significant speedup of the simulations process can be achieved using tabulated chemistry. Overall, the method presented can be considered as a bridge between the experimental works and the development of mean value engine models for engine control applications.

show abstract

Investigation of An Ammonia Diesel Dual-Fuel Combustion Process on a Heavy-Duty Single Cylinder Research Engine for the Development of Suitable Simulation Tools for Maritime Applications

Mante,

Prehn,

Theile

et al. 2023

Proceedings

View full text Add to dashboard Cite

Application of the SRM Engine Suite over the Entire Load-Speed Operation of a U.S. EPA Tier 4 Capable IC Engine

Cited by 20 publications

References 24 publications

Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application

Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application

Diesel engine performance mapping using a parametrized mixing time model

Investigation of An Ammonia Diesel Dual-Fuel Combustion Process on a Heavy-Duty Single Cylinder Research Engine for the Development of Suitable Simulation Tools for Maritime Applications

Contact Info

Product

Resources

About