A Computational Study on the Impact of Cycle-to-Cycle Combustion Fluctuations on Fuel Consumption and Knock in Steady-State and Drivecycle Operation

Poetsch, Christoph; Schuemie, Henrik; Ofner, Herwig; Tatschl, Reinhard; Vítek, Oldřich

doi:10.4271/2013-24-0030

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Final comment concerns the influence of CCV on global/integral performance parameters of SI ICEs. Based on the data shown in this paper and supported by results from [9,10], the following can be stated. The high CCV cases are typically related to high load at low engine speed or low load at low engine speed.…”

Section: Resultsmentioning

confidence: 79%

“…Hence, there is a need to develop methodology which enables knowledge transfer from reference data (typically 3-D CFD data) to 0-D/1-D SW tools while preserving important statistics of CCV. The methodology is based on the scheme of Figure 4 and papers [9,10], moreover it is briefly described in the following subsections.…”

Section: Methodology Of CCV Modelling In 0-d/1-d Cfd Sw Toolmentioning

confidence: 99%

“…Hence, the simulation results (from 0-D/1-D CFD tool) are presented including their statistical analysis to allow for comparison with statistics of reference data. Many important results from 0-D/1-D system simulation tools when applying the above-mentioned methodology are presented in [9,10]. However, the focus of these papers was slightly different.…”

Section: Computed Casesmentioning

confidence: 97%

“…Figure 4). More detailed description of individual cycle matching procedure can be found in [9,10]. The matching itself was achieved by application of optimization (genetic algorithm) -an automatic procedure was necessary due to high amount of data which had to be processed.…”

Section: Individual Cycle Calibrationmentioning

confidence: 99%

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Knowledge Transfer from Detailed 3-D CFD Codes to System Simulation Tools – CCV Modeling in SI Engine

Vítek¹,

Máček²,

Poetsch³

et al. 2016

Journal of Middle European Construction and Design of Cars

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The paper deals with CCV knowledge transfer from reference data (either experiments or 3-D CFD data) into system simulation SW tools (based on 0-D/1-D CFD). It was verified that CCV phenomenon can be modeled by means of combustion model perturbations. The proposed methodology consists of two major steps. First, individual cycle data have to be matched with the 0-D/1-D model, i.e., combustion model parameters are varied to achieve the best possible match of in-cylinder pressure traces. Second, the combustion model parameters (obtained in previous step) are statistically evaluated to obtain PDFs and crosscorrelations. Then such information is imposed to the 0-D/1-D tool to mimic pressure traces CCV. Good correspondence with the reference data is achieved only if both PDFs and cross-correlations are imposed simultaneously. Different engine operating points were evaluated to draw some general conclusions in terms of CCV. It was confirmed that turbulence properties and initial flame kernel development are the dominant factors. However, these factors are neither independent nor random -they seem to be correlated. Operating points with high CCV are more organized in terms of the statistics -they exhibit strong cross-correlations of combustion model parameters. SHRNUTÍČlánek se zabývá přenosem znalostí ohledně mezicyklové variability z referenčních dat (buď experimenty nebo 3-D CFD data) do SW nástrojů pro systémovou simulaci (založeno na 0-D/1-D CFD). Bylo ověřeno, že mezicyklovou variabilitu lze modelovat pomocí fluktuací parametrů modelu hoření. Navržená metoda se skládá ze dvou kroků. Zaprvé, data z jednotlivých cyklů jsou kalibrována s 0-D/1-D modelem, tj. parametry modelu hoření jsou měněny tak, aby bylo dosaženo nejlepší možné shody průběhu tlaku ve válci. Zadruhé, parametry modelu hoření (získané v předchozím kroku) jsou statisticky zpracovány pro získání hustot pravděpodobnosti a vzájemných korelací. Pak je tato informace vnucena do 0-D/1-D nástroje pro napodobení mezicyklové variability tlaku ve válci. Dobrá shoda s referenčními daty je dosažena pouze pokud je jak hustota pravděpodobnosti, tak vzájemná korelace použita současně. Různé pracovní body motoru byly vyhodnoceny pro formulaci obecných závěrů z hlediska mezicyklové variace. Bylo ověřeno, že vlastnosti turbulence a počáteční vývoj jádra plamene jsou dominantní parametry. Avšak tyto parametry nejsou ani nezávislé ani náhodné -zdá se, že jsou vzájemně provázané. Pracovní body s velkou mezicyklovou variabilitou jsou ze statistického pohledu více organizované -vykazují silnou vzájemnou korelaci parametrů modelu hoření.

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Section: Resultsmentioning

confidence: 79%

Section: Methodology Of CCV Modelling In 0-d/1-d Cfd Sw Toolmentioning

confidence: 99%

Section: Computed Casesmentioning

confidence: 97%

Section: Individual Cycle Calibrationmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge Transfer from Detailed 3-D CFD Codes to System Simulation Tools – CCV Modeling in SI Engine

Vítek¹,

Máček²,

Poetsch³

et al. 2016

Journal of Middle European Construction and Design of Cars

Self Cite

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show abstract

“…As a result, this randomness leads to the cycle-to-cycle variation of engine output such as indicated mean efficient pressure (IMEP), thermal efficiency and emissions. In the last three decades, much literature on cycle-to-cycle combustion has been published [4][5][6][7][8][9][10]. For example, a statistical analysis of the cyclic combustion event is explained from view of physics [11], and experimental analyses on cyclic variation have also been provided [12].…”

Section: Introductionmentioning

confidence: 99%

Simulation of knock probability in an internal combustion engine

Shen

2018

Phys. Rev. E

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In spark-ignition internal combustion engines, fluctuations of the in-cylinder pressure trace and the tendency of combustion knock are usually different from one cycle to another. These cycle-to-cycle variations are affected by the initial state at ignition time and the subsequent burning. The occurrence of the phenomena is unpredictable, and their stochastic nature offers challenges in the optimization of engine control strategies. In this paper, a simulator providing a series of cycle-to-cycle varied in-cylinder pressures is introduced. The Wiebe function and Livengood-Wu integration are used to describe the determinacy of combustion. Various means, including the Markov chain, are introduced to express the stochastic quantities during combustion. In addition, the combustion of a given knock probability is simulated.

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System Simulation in Automotive Industry

Knaus¹,

Wurzenberger²

2020

Systems Engineering for Automotive Powertrain Development

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System simulation is a part of the overall field of systems engineering and provides quantitative results to verify and validate the architecture and parameters of a system under development. In the meantime, system simulation is a wellestablished activity in the automotive development area and covers a broad range of applications over the whole development process from system architecture definition, component dimensioning up to virtual testing and calibration. In all of these steps, simulation helps the systems engineer find the best choice of system architectures, evaluate the component target characteristics, define the control logic and parameters, and support the individual tests. Thanks to system simulation, proper system function can be confirmed in early phases of development, which minimizes the number of cost-intensive development loops. In addition, verification and validation and calibration tasks can be frontloaded to reduce overall development time. This chapter focuses on the setup of different physical domain models and how they are linked together. The modeling approaches of the individual domains and simulation management are discussed.

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A Computational Study on the Impact of Cycle-to-Cycle Combustion Fluctuations on Fuel Consumption and Knock in Steady-State and Drivecycle Operation

Cited by 8 publications

References 8 publications

Knowledge Transfer from Detailed 3-D CFD Codes to System Simulation Tools – CCV Modeling in SI Engine

Knowledge Transfer from Detailed 3-D CFD Codes to System Simulation Tools – CCV Modeling in SI Engine

Simulation of knock probability in an internal combustion engine

System Simulation in Automotive Industry

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