Open Access

A pressure dependency is included in a quadratic temperature dependent binary interaction parameter of the UNIQUAC model. Beside the new introduced Temperature-Pressure-Dependent (TPD) model, a suggestion for the binary interaction parameter from Islam and Carlson was investigated. The performances are analyzed by using methanol-water mixtures over a wide temperature and pressure range. First, only experimental vapor-liquid equilibrium data were used to obtain the needed model parameters via an optimization. Both models were able to predict the vapor-liquid equilibrium but failed in the prediction of the excess volume and the excess enthalpy. The consideration of experimental excess volume data beside the experimental vapor-liquid equilibrium data lead to good predictions of the vapor-liquid equilibrium as well as excess volumes. Again, the excess enthalpy was not correctly calculated. Additional experimental excess enthalpy data were introduced into the optimization process. An analysis of the influence of the weighting factors in the objective function of the optimization was performed. The aim was to improve the prediction of the vapor-liquid equilibrium, the molar excess volume and the molar excess enthalpy so that it is finally possible to predict all quantities within an acceptable deviation.

The increasing global energy demand, driven by technological advancements and population growth, has led to a significant rise in crude oil consumption. This trend is expected to continue until 2040 due to the lack of viable alternatives for sectors such as road freight, aviation, and petrochemicals. The combustion of fossil fuels, however, results in the emission of toxic and carcinogenic pollutants, including particulate matter (PM) and nitrogen oxides (NOx). This thesis aims to enhance the understanding of the formation and decomposition pathways of these pollutants under engine-like conditions using surrogate fuels. The research was conducted at the Brandenburg University of Technology Cottbus-Senftenberg, funded by the EU FP7 Marie Skłodowska Curie action, Initial Training Network (ITN) project ECCO-MATE. The project involved collaboration with 11 key partners from academia and industry across Europe and Japan. The primary objective was to develop and implement novel combustion technologies for marine and automotive engines to improve efficiency and meet stringent emission standards. Experimental studies were carried out using various analytical techniques, including gas chromatography, gas analysis, and luminescence techniques. These methods were optimized for engine-like conditions to investigate the formation of PM and NOx. The research utilized a range of experimental facilities, such as shock tubes and rapid compression machines, to measure ignition delay times and flame speciation. The study also involved the characterization of commercial fuels and the selection of surrogate fuels to represent their properties. The findings indicate that the addition of diluents, such as nitrogen and carbon dioxide, can significantly reduce the formation of NOx and PM. The use of oxygenated fuels, like dimethyl ether (DME), was found to enhance combustion efficiency and reduce pollutant emissions. The research also highlighted the importance of understanding the chemical kinetics of fuel decomposition and the role of polycyclic aromatic hydrocarbons (PAHs) in soot formation. Overall, this thesis provides valuable insights into the combustion behavior of surrogate fuels under engine-like conditions. The results contribute to the development of cleaner and more efficient combustion technologies for the automotive and marine sectors, aligning with global efforts to reduce the environmental impact of fossil fuel consumption.

This thesis summarizes the author’s developments of combustion models and multi-objective optimization methods for gasoline and diesel engines. The combustion models belong to the family of zero-dimensional stochastic reactor models introduced in the 1990s to improve the prediction of emissions with detailed chemistry in partially stirred reactors. The first part introduces the fundamentals of the physical and chemical models describing the combustion process. As a novelty, k−ε turbulence models were implemented in the stochastic reactor model to predict the turbulent time and length scales in gasoline and diesel engines. This development allowed an improvement of the models for convective heat transfer, fuel evaporation, gas exchange across the valves, turbulent flame propagation and crevice flow, which depend on the turbulent time and length scales. In the second part, the multi-objective optimization platform for automatic training of the stochastic reactor model is presented. The optimization method considers multiple operating points to find a set of model parameters that predict performance and emissions over the entire engine map. The Non-domination Sorting Genetic Algorithm II is combined with the stochastic reactor model and response surface models to find the best Pareto front. Multi-criteria decision making is used to select the best designs from the Pareto front. Finally, the third part of this thesis deals with the validation of the stochastic reactor model and the multi-objective optimization platform. For this purpose, experiments of two single-cylinder research engines with spark ignition, one passenger car engine with compression ignition and one heavy duty engine with compression ignition are used. For the spark ignition engines, a set of model parameters was found that predicts well the power and emissions over the whole engine map. The calculated turbulent kinetic energy, dissipation, and angular momentum follow the trends of the three-dimensional computational fluid dynamic simulations to a good approximation for various operating points. For the two compression ignition engines, the prediction of combustion progress and nitrogen oxide emissions are in good agreement with the experiments. Larger discrepancies were found for the prediction of carbon monoxide and unburned hydrocarbon. Optimization of the soot model parameters improves the prediction of soot mass for operating points throughout the engine map.

The oxidation of fuel molecules can be described by using a reaction mechanism, a tool that combines thermodynamic and transport properties with reaction rates to predict the behavior and sub-products at different temperatures, pressures and equivalence ratios. A detailed reaction mechanism helps to understand the fuel-specific pollutant formation process. The aim of this doctoral thesis is to generate a hierarchically-detailed chemical reaction mechanism from C3 to C5 hydrocarbons that can be used to understand the reaction decomposition pathways for different fuels at high temperature regime, e.g. propene, propane, butane isomers, butene isomers and pentene isomers. A new nomenclature based in the IUPAC rules, has been developed and implemented as part of this work. The naming follows the order of priority for choosing a principal characteristic group. These naming rules and some examples are explained here. As starting point for this investigation, the chemical model presented in Schenk et al. (2013) has been used. Thermodynamic data for sensitive species from C3 chemistry were revised and updated. Updates in reaction rates for n-butane (C4H10) and iso-butane (C4H10-Me2) are shown. The chemistry of the butene (C4H8) isomers have been revised and a correction taking into account the H-atom allyl abstraction is implemented. Laminar flame speeds and ignition delay times for the different isomers are presented and discussed together with experiments in similar conditions for burner-stabilized flame for the three butene and butane isomers. The high-temperature chemistry for branched and linear C5H10 species is implemented in the model. 2-Methyl-2-butene (C5H10-D2Me2) is the most interesting isomer because 9 of its 10 C-H atoms are in allylic position and it is compared to n-Pentane as an example of a linear molecule. The validation of a burner-stabilized flame, ignition delay time, and laminar flame speed experiments for these fuels are presented and discussed. The compilation strategy was used and it aims to continuously increase the number and type of targets for mechanism validation.

In this thesis, a detailed chemical kinetic mechanism is developed to predict the oxidation of ammonia. The main aim is to cover the most important features of ammonia combustion - laminar flame speed, auto-ignition timing, emission formation, speciation in different reactors, and subsequently to study fuel/NOₓ interaction. Each elementary reaction in the mechanism is carefully reviewed based on several published literature, both experimental and theoretical rate parameters are selected accordingly. A wide range of published experimental data in multi-setup experiments are selected - in freely propagating and burner stabilized premixed flames and in shock tubes and jet-stirred and flow, reactor to assess the performance of the developed mechanism. The reaction mechanism also considers the formation of nitrogen oxides and the reduction of nitrogen oxides depending on the conditions of the surrounding gas phase. The experimental data from the literature are interpreted with the help of the kinetic model developed in this thesis.