TY - GEN A1 - Netzer, Corinna A1 - Seidel, Lars A1 - Ravet, Frédéric A1 - Mauß, Fabian T1 - Impact of the surrogate formulation on 3D CFD engine knock prediction using detailed chemistry T2 - Fuel N2 - For engine knock prediction, surrogate fuels are often composed of iso-octane and n-heptane since they are the components of the Primary Reference Fuel (PRF). By definition, a PRF has no octane sensitivity (S = RON-MON). However, for a commercial gasoline fuel holds RON > MON and therefor S > 0. More complex surrogates are Toluene Reference Fuels (TRF) and Ethanol containing Toluene Reference Fuels (ETRF). In this work, the impact of the surrogate formulation on the prediction of flame propagation and auto-ignition in the unburnt gases are investigated. The surrogates are composed such that the Research Octane Number is the same. The auto-ignition events ahead of the flame front are predicted using 3D CFD and a combustion model based on the ETRF mechanism by Seidel (2017). The strength of the auto-ignition is determined using the detonation diagram by Bradley and co-workers (2002, 2003). Applying the different surrogates, ignition kernels of different size and reactivity are predicted. The results indicate a dependency on the local temperature history and the low temperature chemistry of the fuel species. The comparison of homogenous constant volume reactor and transient simulations show that the analysis of ignition delay time and octane rating solely from homogenous simulations is not sufficient if the knock tendency of a surrogate in engine simulations needs to be characterized. Y1 - 2019 U6 - https://doi.org/10.1016/j.fuel.2019.115678 SN - 1873-7153 VL - Volume 254 ER - TY - GEN A1 - Vacca, Antonino A1 - Bargende, Michael A1 - Chiodi, Marco A1 - Netzer, Corinna A1 - Gern, Maike Sophie A1 - Kauf, Georg Malte A1 - Kulzer, André Casal A1 - Franken, Tim T1 - Analysis of Water Injection Strategies to Exploit the Thermodynamic Effects of Water in Gasoline Engines by Means of a 3D-CFD Virtual Test Bench N2 - CO2 emission constraints taking effect from 2020 lead to further investigations of technologies to lower knock sensitivity of gasoline engines, main limiting factor to increase engine efficiency and thus reduce fuel consumption. Moreover the RDE cycle demands for higher power operation, where fuel enrichment is needed for component protection. To achieve high efficiency, the engine should be run at stoichiometric conditions in order to have better emission control and reduce fuel consumption. Among others, water injection is a promising technology to improve engine combustion efficiency, by mainly reducing knock sensitivity and to keep high conversion rates of the TWC over the whole engine map. The comprehension of multiple thermodynamic effects of water injection through 3D-CFD simulations and their exploitation to enhance the engine combustion efficiency is the main purpose of the analysis. As basis for the research a single cylinder engine derived from a 1l turbocharged 3-cylinders engine is used to evaluate indirect and direct water injection. The entire engine flow field is reproduced and analyzed with 3D-CFD simulations and numerical models are employed to separate the influence of chemical and thermodynamic properties. Measurements are performed with different injectors for indirect/direct water injection in the single-cylinder engine in order to assess water break-up, wall wetting, spray interaction and penetration. Several injection strategies, such as varying start of injection, injection pressure, and water to fuel ratio, are tested at the single-cylinder engine test bench. Detailed gas phase chemistry is employed to link flame front speed with water concentration and knocking occurrence. These results are correlated with the 3D-CFD simulation of mixture formation, in-cylinder flow and water distribution for two different operating points (part load and maximum power) in order to study water behavior, with focus on the evaporation process, in-cylinder pressure and temperature profile, as well as the combustion development, during multiple engine cycles. KW - Water Injection KW - Computational Fluid Dynamics KW - Simulation KW - Virtual Test Bench KW - Thermodynamics Y1 - 2019 UR - https://saemobilus.sae.org/content/2019-24-0102 U6 - https://doi.org/10.4271/2019-24-0102 PB - SAE International CY - Neapel ER - TY - GEN A1 - Franken, Tim A1 - Netzer, Corinna A1 - Mauß, Fabian A1 - Pasternak, Michal A1 - Seidel, Lars A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Matrisciano, Andrea A1 - Kulzer, André Casal T1 - Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry T2 - International Journal of Engine Research N2 - 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. KW - Water Injection KW - Genetic Optimization KW - Spark Ignition Engine KW - Stochastic Reactor Model KW - Detailed Chemistry Y1 - 2019 UR - https://journals.sagepub.com/doi/full/10.1177/1468087419857602 U6 - https://doi.org/10.1177/1468087419857602 SN - 2041-3149 VL - 20 IS - 10 SP - 1089 EP - 1100 ER - TY - GEN A1 - Matrisciano, Andrea A1 - Netzer, Corinna A1 - Werner, Adina A1 - Borg, Anders A1 - Seidel, Lars A1 - Mauß, Fabian T1 - A Computationally Efficient Progress Variable Approach for In-Cylinder Combustion and Emissions Simulations T2 - SAE Technical Paper N2 - The use of complex reaction schemes is accompanied by high computational cost in 3D CFD simulations but is particularly important to predict pollutant emissions in internal combustion engine simulations. One solution to tackle this problem is to solve the chemistry prior the CFD run and store the chemistry information in look-up tables. The approach presented combines pre-tabulated progress variable-based source terms for auto-ignition as well as soot and NOx source terms for emission predictions. The method is coupled to the 3D CFD code CONVERGE v2.4 via user-coding and tested over various speed and load passenger-car Diesel engine conditions. This work includes the comparison between the combustion progress variable (CPV) model and the online chemistry solver in CONVERGE 2.4. Both models are compared by means of combustion and emission parameters. A detailed n-decane/α-methyl-naphthalene mechanism, comprising 189 species, is used for both online and tabulated chemistry simulations. The two chemistry solvers show very good agreement between each other and equally predict trends derived experimentally by means of engine performance parameters as well as soot and NOx engine-out emissions. The CPV model shows a factor 8 speed-up in run-time compared to the online chemistry solver without compromising the accuracy of the solution. Y1 - 2019 U6 - https://doi.org/10.4271/2019-24-0011 SN - 0148-7191 SN - 2688-3627 ER -