LES Multi-Cycle Analysis of the Combustion Process in a Small SI Engine

Koch, Jann; Schmitt, Martin; Wright, Yuri M.; Steurs, Karel; Boulouchos, Konstantinos

doi:10.4271/2014-01-1138

Cited by 40 publications

(39 citation statements)

References 21 publications

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“…up to 700 • CA. Following this calibration, using an unchanged ignition treatment for all cycles, the variation in the pressure evolutions can then be predicted, in good agreement with the experimental data; as was also found in [15] for a completely different engine.…”

Section: Combustion Modelsupporting

confidence: 51%

“…Similar approaches have been successfully adopted also in other multi-cycle LES studies (see e.g. [10,15,28]) and therefore used here as well. More sophisticated approaches have been proposed in the LES context in conjunction with Flame Surface Density combustion models [29,30] and for level sets in a RANS context in [31].…”

Section: Combustion Modelmentioning

confidence: 95%

“…In order to reduce the computational time, the meshes in the resolution study are employed as half cylinder meshes. According to Koch et al [15], the effect of the symmetry boundary condition is limited, since in this setup the tumble around the x-axis and the swirl are negligible small. In addition, the half cylinder meshes are only used for the LES and G-equation model analysis in Section 4.1.1.…”

Section: Mesh Modelsmentioning

confidence: 98%

“…The engine mesh consists of approximately 1.53 million cells at BDC, which results in an average cell size of 0.65 mm. Mesh resolutions from comparable studies, where multiple cycle [10,15] and spray behavior [33] in internal combustion engines are analyzed provided a mean grid size of approximately 0.8 and 1.2 mm, respectively. Further mesh details are shown in Table 2.…”

Section: Mesh Modelsmentioning

confidence: 99%

“…In the works [13,14] the Extended Coherent Flame Model (ECFM-LES) was used to study the effect of local cyclic flow variations around the spark plug on the global cycle-to-cycle variations of the pressure traces. Further studies in the fields of cycle-to-cycle variations (CCV) by using LES have been conducted by Koch et al [15]. In this work the subgrid turbulence is modeled by the k − l model and combustion is simulated using G-equation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multiple Cycle LES Simulations of a Direct Injection Natural Gas Engine

Schmitt¹,

Hu²,

Wright³

et al. 2015

Flow Turbulence Combust

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In this work the flow field evolution, mixture formation and combustion process in an engine with methane Direct Injection (DI) is investigated using Large Eddy Simulations. The supersonic methane injection is modeled according to Müller et al. (2013) and combustion by a level set approach. The flame propagation showed to be dependent on the grid resolution. Higher grid resolutions have two opposing effects: first the fraction of unresolved turbulence is reduced, which decrease the flame speed and second flame wrinkling is increased resulting in faster flame propagation. For the observed setup the wrinkling effect was stronger. The average in-cylinder pressure traces as well as the cyclic variability thereof were compared to experimental data and very good agreement was found. During the supersonic gaseous injection the turbulence level in the cylinder is significantly increased, which dissipates quickly and thus has only a minor effect on the flame propagation. The introduced momentum showed a larger impact, since it enhances the tumble motion resulting in increased turbulence levels as the tumble decays shortly before ignition. During DI the cyclic differences in the tumble motion are preserved, but the impact on the average tumble level results in changing relative differences of the cyclic turbulence levels at ignition timing. Thus an injection direction supporting the tumble flows is expected to reduce the Cycle-to-Cycle Variations (CCV), while a reduction of the tumble strength could increase the CCV level. Compared to the fluctuations in the turbulence levels, the cyclic variability of the equivalence ratio at the injection location with DI showed a minor effect on the simulated CCVs.

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Section: Combustion Modelsupporting

confidence: 51%

Section: Combustion Modelmentioning

confidence: 95%

Section: Mesh Modelsmentioning

confidence: 98%

Section: Mesh Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiple Cycle LES Simulations of a Direct Injection Natural Gas Engine

Schmitt¹,

Hu²,

Wright³

et al. 2015

Flow Turbulence Combust

Self Cite

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Quantitative metrics for comparison of in-cylinder velocity fields using particle image velocimetry

et al. 2020

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The in-cylinder flow field plays a key role in determining the combustion performance of internal combustion engines (ICEs) and it is critically important to validate numerical simulations of the flow field by comparison to experimental measurements using techniques such as particle image velocimetry (PIV). With the current trend for high-speed diagnostics, methods for quantitative comparison of vector fields are required which can provide robust spatially averaged results, without inspection of individual flow fields. The quality of match between vector fields, when quantified using current metrics such as the relevance index (RI), can be overly sensitive to the alignment of regions of low velocity such as the tumble vortex centre. This work presents complementary metrics, weighted using a function of the local velocity, for robust quantification of the alignment and magnitude differences between vector fields, the weighted relevance index (WRI) and the weighted magnitude index (WMI). These metrics are also normalized and combined in the combined magnitude and relevance index (CMRI). PIV measurements taken up to every 2 crank angle degrees within the tumble plane of a motored, optically accessible ICE are used to demonstrate the motivation for development and the application of the WRI, WMI, and CMRI metrics. The metrics are used to determine the number of cycles required to provide a representative mean flow field and to identify single cycles of interest. Variability of the flow field is quantified using the metrics and shows high variability in the region of the spark plug near typical ignition timings.

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