A Monte Carlo Based Turbulent Flame Propagation Model for Predictive SI In-Cylinder Engine Simulations Employing Detailed Chemistry for Accurate Knock Prediction

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Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.

Section: Srmmentioning

confidence: 99%

Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry

Franken

Netzer

Mauß

et al. 2019

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“…There is no interaction between unburnt and burnt zone, except for mass transfer from the unburnt to burnt zone due to flame propagation. The turbulent flame propagation model from Kolla et al, 52 introduced in Bjerkborn et al 53 for the SI-SRM, governs the mass transfer…”

Section: Reaction Scheme and Combustion Modelsmentioning

confidence: 99%

Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines

Netzer

Pasternak²,

Seidel³

et al. 2019

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Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels’ tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier–Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier–Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier–Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier–Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.

“…The quasi-3D turbulent flame propagation 12,13 assumes spherical flame growth that governs the mass transfer from the unburned zone to the burned zone that is limited by the cylinder walls.…”

Section: Modellingmentioning

confidence: 99%

“…The 0D SI-SRM is a main numerical model of the presented simulation process and it has been presented in detail elsewhere. 12,[14][15][16] Here, we report the most important features, only.…”

Section: Modellingmentioning

confidence: 99%

“…A version of the SRM for the simulation of SI engines (denoted as SI-SRM) has been introduced in Bjerkborn and colleagues. 12,13 It contains a sub-model for turbulent flame propagation and quasi-3D treatment of the combustion geometry with limitation to spherical flame growth. In Pasternak and colleagues, 14,15 the SI-SRM has been applied to evaluate the impact of multiple spark plug technology on the combustion processes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gasoline engine simulations using zero-dimensional spark ignition stochastic reactor model and three-dimensional computational fluid dynamics engine model

Pasternak¹,

Mauß

Sens

et al. 2015

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A simulation process for spark ignition gasoline engines is proposed. The process is based on a zero-dimensional spark ignition stochastic reactor model and three-dimensional computational fluid dynamics of the cold in-cylinder flow. The cold flow simulations are carried out to analyse changes in the turbulent kinetic energy and its dissipation. From this analysis, the volume-averaged turbulent mixing time can be estimated that is a main input parameter for the spark ignition stochastic reactor model. The spark ignition stochastic reactor model is used to simulate combustion progress and to analyse auto-ignition tendency in the end-gas zone based on the detailed reaction kinetics. The presented engineering process bridges the gap between three-dimensional and zero-dimensional models and is applicable to various engine concepts, such as, port-injected and direct injection engines, with single and multiple spark plug technology. The modelling enables predicting combustion effects and estimating the risk of knock occurrence at different operating points or new engine concepts for which limited experimental data are available.