Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model

Mauß

Seidel³

et al. 2020

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This work presents the assessment of direct water injection in spark-ignition engines using single cylinder experiments and tabulated chemistry-based simulations. In addition, direct water injection is compared with cooled low-pressure exhaust gas recirculation at full load operation. The analysis of the two knock suppressing and exhaust gas cooling methods is performed using the quasi-dimensional stochastic reactor model with a novel dual fuel tabulated chemistry model. To evaluate the characteristics of the autoignition in the end gas, the detonation diagram developed by Bradley and co-workers is applied. The single cylinder experiments with direct water injection outline the decreasing carbon monoxide emissions with increasing water content, while the nitrogen oxide emissions indicate only a minor decrease. The simulation results show that the engine can be operated at λ = 1 at full load using water–fuel ratios of up to 60% or cooled low-pressure exhaust gas recirculation rates of up to 30%. Both technologies enable the reduction of the knock probability and the decrease in the catalyst inlet temperature to protect the aftertreatment system components. The strongest exhaust temperature reduction is found with cooled low-pressure exhaust gas recirculation. With stoichiometric air–fuel ratio and water injection, the indicated efficiency is improved to 40% and the carbon monoxide emissions are reduced. The nitrogen oxide concentrations are increased compared to the fuel-rich base operating conditions and the nitrogen oxide emissions decrease with higher water content. With stoichiometric air–fuel ratio and exhaust gas recirculation, the indicated efficiency is improved to 43% and the carbon monoxide emissions are decreased. Increasing the exhaust gas recirculation rate to 30% drops the nitrogen oxide emissions below the concentrations of the fuel-rich base operating conditions.

Section: Tabulated Chemistrymentioning

confidence: 99%

“…Furthermore, a progress variable approach is used for the chemistry table lookup. 24–27 The progress variable C is defined as…”

Section: Tabulated Chemistrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gasoline engine performance simulation of water injection and low-pressure exhaust gas recirculation using tabulated chemistry

Mauß

Seidel³

et al. 2020

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“…The QD SRM was already applied to investigate the effect of different octane number fuels on auto-ignition in the unburned gas as shown by Netzer et al 17 The detailed chemistry for multi-component fuels used in that work as well as in the presented work is based on the methodology of reaction mechanism development and reduction introduced by Seidel and colleagues. 18,19 To reduce the computational cost of the QD SRM simulations, Matrisciano et al 20,21 proposed a reaction-progress-variable-based tabulation strategy. Thereby, the detailed chemistry is pre-compiled in a look-up table based on thermodynamic conditions and reaction progress variable.…”

Section: Introductionmentioning

confidence: 99%

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

Netzer

Mauß

et al. 2019

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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.

“…It is tested extensively for compression-ignited engine applications. 14–26 A modified version of the Euclidean Minimum Spanning Tree (EMST) mixing model 27 is incorporated, which comprises the total enthalpy ( H ) as the only mixing limiting scalar. Hence, it is possible to account for effects of thermal stratification on the localness of the mixing process.…”

Section: Introductionmentioning

confidence: 99%

Prediction of thermal stratification in an engine-like geometry using a zero-dimensional stochastic reactor model

Klauer²,

Kienberg³

et al. 2019

Self Cite

The prediction of local heat transfer and thermal stratification in the zero-dimensional stochastic reactor model is compared to direct numerical simulation published by Schmitt et al. in 2015. Direct numerical simulation solves the Navier–Stokes equations without incorporating model assumptions for turbulence and wall heat transfer. Therefore, it can be considered as numerical experiment and is suitable to validate approximations in low-dimensional models. The stochastic reactor model incorporates a modified version of the Euclidean Minimum Spanning Tree mixing model proposed by Subramaniam et al. in 1998. To capture the thermal stratification of the direct numerical simulation, the total enthalpy ( H) is used as the only mixing limiting scalar within the newly proposed H-Euclidean-Minimum-Spanning-Tree. Furthermore, a stochastic heat transfer model is incorporated to mimic turbulence effects on local heat transfer distribution to the walls. By adjusting the Cϕ mixing time and Ch stochastic heat transfer parameter, the stochastic reactor model predicts accurately the thermal stratification of the direct numerical simulation. Comparing the Woschni, Hohenberg and Heinle heat transfer model shows that the modified Heinle model matches accurately the direct numerical simulation results. Thereby, the Heinle model accounts for the influence of turbulent kinetic energy on the characteristic velocity in the heat transfer coefficient calculation. This highlights the importance of incorporating turbulence effects in low-dimensional heat transfer models. Overall, the zero-dimensional stochastic reactor model with the H-Euclidean-Minimum-Spanning-Tree mixing model, the stochastic heat transfer model and the modified Heinle correlation have proven successfully the prediction of mean quantities like temperature and heat transfer and thermal stratification of the direct numerical simulation.