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The development of future gasoline engines is dominated by the study of new technologies aimed at reducing the engine negative environmental impact and increase its thermal efficiency. One common trend is to develop smaller engines able to operate in stoichiometric conditions across the whole engine map for better efficiency, lower fuel consumption, and optimal conversion rate of the three-way catalyst (TWC). Water injection is one promising technique, as it significantly reduces the engine knock tendency and avoids fuel enrichment for exhaust temperature mitigation at high power operation.
With the focus on reducing the carbon footprint of the automotive sector, another vital topic of research is the investigation of new alternative CO2-neutral fuels or so-called eFuels. Several studies have already shown how these new synthetic fuels can be produced by exploiting renewable energy sources and can significantly reduce engine emissions.
This work is part of the FVV project number 1367, “Water Injection in Spark-Ignition Engines II”, which investigates the coexistence of two different engine technologies that heads towards the same direction of sustainability of the internal combustion engine: water injection and eFuels. The goal is to assess the advantages that the adoption of an eFuel in combination with water injection strategies can bring, with respect to the same strategies applied in combination with commercial gasoline.
Several water injection strategies, including direct water injection, indirect water injection, injection timing, and pressure variations, are considered for a better understanding of the benefits brought by the combination of an eFuel with water injection. For this purpose, experimental tests and 3D-CFD engine simulations have been performed. The physical properties of the investigated eFuel have been experimentally measured for accurate modelling in the simulation environment, and the thermodynamic properties have been evaluated utilizing detailed chemistry calculations.
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.
The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low. This work presents a novel methodology to couple the stochastic reactor model and the Non-dominated Sorting Genetic Algorithm. First, the stochastic reactor model is calibrated for 10 low, medium and high load operating points at various engine speeds. Second, each operating point is optimized to find the lowest fuel consumption and specific NOx emissions. The optimization input parameters are the temperature at intake valve closure, the compression ratio, the start of injection, the injection pressure and exhaust gas recirculation rate. Additionally, it is ensured that the maximum peak cylinder pressure and turbine inlet temperature are not exceeded. This enables a safe operation of the engine and exhaust aftertreatment system under the optimized conditions. Subsequently, a reliability analysis is performed to estimate the effect of off-nominal conditions on the objectives and constraints. The novel multi-objective optimization methodology has proven to deliver reasonable results. The zero-dimensional stochastic reactor model with tabulated chemistry is a fast running physics-based model that allow to run large optimization problems in a short amount of time. The combination with the reliability analysis also strengthens the confidence in the simulation-based optimized engine operation parameters.
It is important to understand the low-temperature chemistry of 1-hexene as it is used as a representative alkene component in gasoline surrogate fuels. Ignition delay times (IDTs) of 1-hexene measured in rapid compression machines (RCMs) can be used to validate its low-temperature chemistry. However, volume history profiles are not available for published RCM IDT data. This has restricted the validation of the low-temperature chemistry of 1-hexene at engine-relevant conditions (i.e. at low temperatures and high pressures). Thus, new RCM IDT data with associated volume history profiles are needed. In this study, both an RCM and a high-pressure shock tube (ST) are employed to measure IDTs of 1-hexene at equivalence ratios of 0.5, 1.0 and 2.0 in ‘air’ and at pressures of 15 and 30 atm. A cool-flame (first stage) and total (second stage) ignition was observed in the RCM experiments. Moreover, carbon monoxide and water versus time histories produced during 1-hexene oxidation at highly diluted conditions were measured in a ST. A new detailed chemical kinetic model describing 1-hexene oxidation is proposed and validated using these new measured data together with various experimental data available in the literature. The kinetic model can predict well the auto-ignition behavior and oxidation processes of 1-hexene at various conditions. The rate constants and branching ratio for hydroxyl radical addition to the double bond of 1-hexene are particularly important and discussed based on the experimental and theoretically calculated results from previous studies as well as validation results from jet-stirred reactor (JSR) species profiles. Flux and sensitivity analyses are performed to determine the important reaction classes for 1-hexene oxidation and show that the reactions associated with hydroxy radical addition to the double bond contribute most to the low-temperature reactivity of 1-hexene. In the negative temperature coefficient (NTC) regime, the isomerization of hexenyl-peroxy radicals promotes fuel reactivity due to its associated chain branching pathways.
The opinion dynamics model introduced by Deffuant and Weisbuch as well as the one by Hegselmann and Krause are rather similar. In both models individuals are assumed to have opinions about an issue, they meet and discuss, and they may adapt their opinions towards the other agents` opinions or may ignore each other if their positions are too different. Both models differ with respect to the number of peers they meet at once. Furthermore the model by Deffuant and Weisbuch has a convergence parameter that controls how fast agents adapt their opinions. By defining the reversed parameter as self-support we can extend the applicability of this parameter to scenarios with more than one interaction partner. We investigate the effects of changing the number of peers met at once, which is done for different population sizes, and the effects of changing the self-support. For describing the dynamics we look at different statistics, i.e. number of cluster, number of major clusters, and Gini coefficient.