@misc{FrankenSeidelShresthaetal., author = {Franken, Tim and Seidel, Lars and Shrestha, Krishna Prasad and Gonzalez Mestre, Laura Catalina and Mauß, Fabian}, title = {Multi-objective Optimization of Gasoline, Ethanol, and Methanol in Spark Ignition Engines}, abstract = {In this study, an engine and fuel co-optimization is performed to improve the efficiency and emissions of a spark ignition engine utilizing detailed reaction mechanisms and stochastic combustion modelling. The reaction mechanism for gasoline surrogates (Seidel 2017), ethanol, and methanol (Shrestha et al. 2019) is validated for experiments at different thermodynamic conditions. Liquid thermophysical properties of the RON95E10 surrogate (iso-octane, n-heptane, toluene, and ethanol mixture), ethanol, and methanol are determined using the NIST standard reference database (NIST 2018) and Yaws database (Yaws 2014). The combustion chemistry, laminar flame speed, and thermophysical data are pre-compiled in look-up tables to speed up the simulations (tabulated chemistry). The auto-ignition in the stochastic reactor model is predicted by the detailed chemistry and subsequently evaluated using the Bradley Detonation Diagram (Bradley et al. 2002, Gu et al. 2003, Neter 2019), which assigns two dimensionless parameters (resonance parameter and reactivity parameter). According to the defined developing detonation limits, the auto-ignition is either in deflagration, sub-sonic auto-ignition, or developing detonation mode. Ethanol and methanol show a knock-reducing characteristic, which is mainly due to the high heat of vaporization. The multi-objective optimization process includes mathematical algorithms for design space exploration with Uniform Latin Hypercube, pareto front convergence with Non-dominated Sorting Genetic Algorithm II (NSGA-II), and multi-criteria decision making (Deb et al. 2002). The optimization input parameter ranges are selected according to the previous sensitivity analysis, and the objectives are to minimize specific CO2 and specific CO and maximize indicated efficiency. The performance study of different optimization algorithms shows that the incorporation of metamodels is beneficial to improve the design space exploration, while keeping the optimization duration low. The comparison of different reaction mechanisms, which are applied in the optimization process, shows a strong impact on the pareto front solutions. This is due to differences in the emission formation and auto-ignition between the different reaction schemes. Overall, the engine efficiency is increased by 3.5 \% points, and specific CO2 emissions are reduced by 99 g/kWh for ethanol and 142 g/kWh for methanol combustion compared to the base case. This is achieved by advanced spark timing, lean combustion, and reduced C:H ratio of ethanol and methanol in relation to RON95E10.}, language = {en} } @misc{MatriscianoFrankenGonzalezMestreetal., author = {Matrisciano, Andrea and Franken, Tim and Gonzalez Mestre, Laura Catalina and Borg, Anders and Mauß, Fabian}, title = {Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models}, series = {Applied Sciences}, volume = {10}, journal = {Applied Sciences}, number = {24}, issn = {2076-3417}, doi = {10.3390/app10248979}, abstract = {The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO2, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns.}, language = {en} } @misc{FrankenSeidelGonzalezMestreetal., author = {Franken, Tim and Seidel, Lars and Gonzalez Mestre, Laura Catalina and Shrestha, Krishna Prasad and Matrisciano, Andrea and Mauss, Fabian}, title = {Assessment of Auto-Ignition Tendency of Gasoline, Methanol, Toluene and Hydrogen Fuel Blends in Spark Ignition Engines}, series = {THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines}, journal = {THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines}, pages = {23}, abstract = {State of the art spark ignited gasoline engines achieve thermal efficiencies above 46 \% e.g. due to friction optimized crank trains, high in-cylinder tumble flow and direct fuel injection. Further improvements of thermal efficiency are expected from lean combustion, higher compression ratio and new knock-resistant fuel blends. One of the limitations to these improvements are set by the autoignition in the end gas, which can develop to knocking combustion and severely damage the internal combustion engine. The auto-ignition is enhanced by high cylinder gas temperatures and reactive species in the end gas composition. Quasi-dimensional Stochastic Reactor Model simulations with detailed chemistry allow to consider the thermochemistry properties of surrogates and complex end gas compositions. Based on the detailed reaction scheme and surrogate model, an innovative tabulated chemistry approach is utilized to generate dual-fuel laminar flame speed and combustion chemistry look-up tables. This reduces the simulation duration to seconds per cycle, while the loss in accuracy compared to solving the chemistry "online" is marginal. The auto-ignition events predicted by the tabulated chemistry simulation are evaluated using the Detonation Diagram developed by Bradley and co-workers. This advanced methodology for quasi-dimensional models evaluates the resonance between the shock wave and reactionfront velocity from auto-ignition in the end gas and determines if it is a harmful developing detonation or normal deflagration. The aim of this work is to evaluate the auto-ignition characteristics of different fuel blends. The Stochastic Reactor Model with tabulated chemistry is applied to perform a numerical analysis of the autoignition of the fuel blends and operating conditions. Experimental measurements of a single cylinder research engine operated with RON95 E10 fuel are used to train and validate the simulation model. The RON95 E10 fuel is blended with Methanol, Hydrogen and Toluene. The knock tendency based on the evaluation of auto-ignition events of the different fuel blends are analysed for three operating points at 1500 rpm 15 bar IMEP, 2000 rpm 20 bar IMEP and 2500 rpm 15 bar IMEP with advanced spark timings.}, language = {en} }