Experimental and simulative investigation of flame–wall interactions and quenching in spark-ignition engines

Suckart, Dominik; Linse, Dirk; Schutting, Eberhard; Eichlseder, Helmut

doi:10.1007/s41104-016-0015-z

Cited by 28 publications

(20 citation statements)

References 32 publications

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“…6 This could be even high as 7.0 in engine conditions. 25 Peclet number is the ratio between flame power and wall heat flux, which simplifies to;…”

Section: Modelling Near-wall Quenching Ratementioning

confidence: 99%

“…Once a suitable relation for P e Q and P e max is given, equations (4)–(7) can effectively be used in modelling studies to account for the reduction in burning rate near solid walls. Following, Suckart et al 25 assuming equal diffusivities of all chemical species, the characteristic flame thickness δ L was taken to be the diffusive flame thickness given by equation (8), where ν is the kinematic viscosity of unburned gas.…”

Section: Modelling Near-wall Quenching Ratementioning

confidence: 99%

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Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

Ranasinghe

Malalasekera

2020

International Journal of Engine Research

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A flame front is quenched when approaching a cold wall due to excessive heat loss. Accurate computation of combustion rate in such situations requires accounting for near wall flame quenching. Combustion models, developed without considering wall effects, cannot be used for wall bounded combustion modelling, as it leads to wall flame acceleration problem. In this work, a new model was developed to estimate the near wall combustion rate, accommodating quenching effects. The developed correlation was then applied to predict the combustion in two spark ignition engines in combination with the famous Bray–Moss–Libby (BML) combustion model. BML model normally fails when applied to wall bounded combustion due to flame wall acceleration. Results show that the proposed quenching correlation has significantly improved the performance of BML model in wall bounded combustion. As a second step, in order to further enhance the performance, the BML model was modified with the use of Kolmogorov–Petrovski–Piskunov analysis and fractal theory. In which, a new dynamic formulation is proposed to evaluate the mean flame wrinkling scale, there by accounting for spatial inhomogeneity of turbulence. Results indicate that the combination of the quenching correlation and the modified BML model has been successful in eliminating wall flame acceleration problem, while accurately predicting in-cylinder pressure rise, mass burn rates and heat release rates.

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“…6 This could be even high as 7.0 in engine conditions. 25 Peclet number is the ratio between flame power and wall heat flux, which simplifies to;…”

Section: Modelling Near-wall Quenching Ratementioning

confidence: 99%

Section: Modelling Near-wall Quenching Ratementioning

confidence: 99%

Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

Ranasinghe

Malalasekera

2020

International Journal of Engine Research

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“…However, these fluctuating effects occur as subsequent causes of the phenomena described above. Fluctuations in the flame–wall quenching 28 entail variations in total hydrocarbon (THC) emissions. 29,30 High cyclic flame–wall distance fluctuations may significantly increase THC emissions and thus lead to an incomplete combustion, again increasing CCV.…”

Section: Physical Background Of CCVmentioning

confidence: 99%

“…This reference point is defined for the Miller engine, which acts as the design engine (see Table 3). From the literature, 7,28,33 a dimension for the level of fluctuations of the five implemented physical causes is extracted and imposed on each cause. Table 4 shows these fixed values.…”

Section: Model Design Calibration and Validationmentioning

confidence: 99%

A physical-based approach for modeling cycle-to-cycle variations within a zero-dimensional/one-dimensional simulation environment

Krost

Hübner

Hasse

2017

International Journal of Engine Research

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In the spark-ignition engine development and optimization process, the cyclic variability of combustion is an essential and current issue. Cycle-to-cycle variations are extensively investigated by means of quasi-dimensional (zero-dimensional/ one-dimensional) simulation modeling. To date, these model approaches have been limited either because they neglected particularly significant physical causes and factors influencing cyclic combustion variations or because of their solely empirical combustion modeling basis. However, in order to ensure the high validity of simulation results, quasidimensional model approaches have to accurately describe the physical background of engine combustion. Therefore, a new cyclic combustion variation model is introduced in this study. This cycle-to-cycle variation model is based on previously developed, highly sophisticated physical turbulence, ignition and combustion models, thus for the first time enabling the physical description of cycle-to-cycle variation. The model integrates the most significant physical causes of combustion variations and the factors which influence them, obtained from a literature study. Hence, the derived cycle-to-cycle variation model can physically react to changes in engine parameters such as the engine speed, load, spark timing, valve lift and timing as well as the air-fuel equivalence ratio l. For validation, the new cycle-to-cycle variation model is compared to a state-of-the-art cycle-to-cycle variation model and analyzed by means of engines with different combustion processes. This new cycle-to-cycle variation model uniquely features the physical background of the underlying combustion model and the integration of more influencing factors than in previous approaches. Another unique feature is its basis on extensive experimental data, gained by changing various engine parameters for homogeneous charge sparkignition engines with different combustion/engine concepts. These include engines with high turbulence generation or a long expansion stroke via crank and valve train.

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“…The authors determined quenching distances under various flame and wall conditions. Since then, extensive experimental (Lu et al 1991;Ezekoye et al 1992;Bellenoue et al 2003;Sotton et al 2005;Kim et al 2006;Boust et al 2007;Labuda et al 2011;Mann et al 2014;Dreizler and Böhm 2015;Suckart et al 2016;Rißmann et al 2017;Jainski et al 2017aJainski et al , b, 2018Kosaka et al 2018;Häber and Suntz 2018;Kosaka et al 2019) and numerical (Poinsot et al 1993;Bruneaux et al 1996;Popp and Baum 1997;Hasse et al 2000;Andrae et al 2002;Singh 2004;Chauvy et al 2010;Proch and Kempf 2015;Ganter et al 2017;Heinrich et al 2018a, b;Strassacker et al , 2019) studies on flame-wall interactions (FWI) have been carried out in recent years. Even though almost seven decades have been spent on the investigation of flame quenching near (cold) walls, which has improved the understanding of the flame-wall interaction substantially, one is far from having a quantitative understanding of the phenomena.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study of Quenching Distances for Side-Wall Quenching Using Detailed Diffusion and Chemistry

Zirwes

Häber

Zhang

et al. 2020

Flow Turbulence Combust

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The numerical investigation of quenching distances in laminar flows is mainly concerned with two setups: head-on quenching (HOQ) and side-wall quenching (SWQ). While most of the numerical work has been conducted for HOQ with good agreement between simulation and experiment, far less analysis has been done for SWQ. Most of the SWQ simulations used simplified diffusion models or reduced chemistry and achieved reasonable agreement with experiments. However, it has been found that quenching distances for the SWQ setup differ from experimental results if detailed diffusion models and chemical reaction mechanisms are employed. Side-wall quenching is investigated numerically in this work with steady-state 2D and 3D simulations of an experimental flame setup. The simulations fully resolve the flame and employ detailed reaction mechanisms as well as molecular diffusion models. The goal is to provide data for the sensitivity of numerical quenching distances to different parameters. Quenching distances are determined based on different markers: chemiluminescent species, temperature and OH iso-surface. The quenching distances and heat fluxes at the cold wall from simulations and measurements agree well qualitatively. However, quenching distances from the simulations are lower than those from the experiments by a constant factor, which is the same for both methane and propane flames and also for a wide range of equivalence ratios and different markers. A systematic study of different influencing factors is performed: Changing the reaction mechanism in the simulation has little impact on the quenching distance, which has been tested with over 20 different reaction mechanisms. Detailed diffusion models like the mixture-averaged diffusion model and multi-component diffusion model with and without Soret effect yield the same quenching distances. By assuming a unity Lewis number, however, quenching distances increase significantly and have better agreement with measurements. This was validated by two different numerical codes (OpenFOAM and FASTEST) and also by 1D head-on quenching simulations (HOQ). Superimposing a fluctuation on the inlet velocity in the simulation also increases the quenching distance on average compared to the reference steady-state case. The inlet velocity profile, temperature boundary condition of the rod and radiation have a negligible effect. Finally, three dimensional simulations are necessary in order to obtain the correct velocity field in the SWQ computations. This however has only a negligible effect on quenching distances.

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Experimental and simulative investigation of flame–wall interactions and quenching in spark-ignition engines

Cited by 28 publications

References 32 publications

Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

A physical-based approach for modeling cycle-to-cycle variations within a zero-dimensional/one-dimensional simulation environment

Numerical Study of Quenching Distances for Side-Wall Quenching Using Detailed Diffusion and Chemistry

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