TY - GEN A1 - Picerno, Mario A1 - Lee, Sung-Yong A1 - Pasternak, Michal A1 - Siddareddy, Reddy Babu A1 - Franken, Tim A1 - Mauß, Fabian A1 - Andert, Jakob T1 - Real-Time Emission Prediction with Detailed Chemistry under Transient Conditions for Hardware-in-the-Loop Simulations T2 - Energies N2 - The increasing requirements to further reduce pollutant emissions, particularly with regard to the upcoming Euro 7 (EU7) legislation, cause further technical and economic challenges for the development of internal combustion engines. All the emission reduction technologies lead to an increasing complexity not only of the hardware, but also of the control functions to be deployed in engine control units (ECUs). Virtualization has become a necessity in the development process in order to be able to handle the increasing complexity. The virtual development and calibration of ECUs using hardware-in-the-loop (HiL) systems with accurate engine models is an effective method to achieve cost and quality targets. In particular, the selection of the best-practice engine model to fulfil accuracy and time targets is essential to success. In this context, this paper presents a physically- and chemically-based stochastic reactor model (SRM) with tabulated chemistry for the prediction of engine raw emissions for real-time (RT) applications. First, an efficient approach for a time-optimal parametrization of the models in steady-state conditions is developed. The co-simulation of both engine model domains is then established via a functional mock-up interface (FMI) and deployed to a simulation platform. Finally, the proposed RT platform demonstrates its prediction and extrapolation capabilities in transient driving scenarios. A comparative evaluation with engine test dynamometer and vehicle measurement data from worldwide harmonized light vehicles test cycle (WLTC) and real driving emissions (RDE) tests depicts the accuracy of the platform in terms of fuel consumption (within 4% deviation in the WLTC cycle) as well as NOx and soot emissions (both within 20%). KW - hardware-in-the-loop KW - virtual calibration KW - diesel powertrain KW - tabulated chemistry Y1 - 2022 U6 - https://doi.org/10.3390/en15010261 SN - 1996-1073 VL - 15 IS - 1 SP - 1 EP - 21 ER - TY - GEN A1 - Franken, Tim A1 - Srivastava, Vivek A1 - Lee, Sung-Yong A1 - Heuser, Benedikt A1 - Shrestha, Krishna Prasad A1 - Seidel, Lars A1 - Mauß, Fabian ED - Xandra, Margot ED - Payri, Raúl ED - Serrano, José Ramón T1 - Numerical Analysis of the Combustion of Diesel, Dimethyl Ether, and Polyoxymethylene Dimethyl Ethers (OMEn, n=1-3) Using Detailed Chemistry T2 - THIESEL 2022 : Conference on Thermo- and Fluid-Dynamics of Clean Propulsion Powerplants, 13th-16th September 2022 : conference proceedings N2 - New types of synthetic fuels are introduced in internal combustion engine applications to achieve carbon-neutral and ultra-low emission combustion. Dimethyl Ether (DME) and Polyoxymethylene Dimethyl Ethers (OMEn) belong to such kind of synthetic fuels. Recently, Shrestha et al. (2022) have developed a novel detailed chemistry model for OMEn (n=1-3) to predict the ignition delay time, laminar flame speed and species formation for various thermodynamic conditions. The detailed chemistry model is applied in the zero dimensional (0D) stochastic reactor model (DI-SRM) to investigate the non-premixed combustion in a 2-liter diesel engine. Further insights in the formation of unburned hydrocarbons (HC), carbon monoxide and nitrogen oxides during the combustion of OMEn fuels are obtained in this work. The combustion and emission formation of DME and OMEn (n=1-3) are investigated and compared to conventional Diesel combustion. The mixture formation is governed by an earlier vaporization of the DME and OMEn fuels, faster homogenization of the respective air-fuel mixture and higher reactivity. At the same injection pressure, the OMEn fuels obtain higher NOx but lower CO and HC emissions. High amounts of aromatics, ethene, methane formaldehyde and formic acid are found within the Diesel exhaust gas. The DME and OMEn exhaust gas contains higher fractions of formaldehyde and formic acid, and fractions of methane, methyl formate and nitromethane. KW - Polyoxymethylene Dimethyl Ethers KW - Stochastic Reactor Model KW - Detailed Chemistry KW - Modelling KW - Emissions Y1 - 2022 UR - https://www.lalibreria.upv.es/portalEd/UpvGEStore/products/p_6328-1-1 SN - 978-84-1396-055-5 U6 - https://doi.org/10.4995/Thiesel.2022.632801 PB - Editorial Universitat Politècnica de València CY - València ER - TY - GEN A1 - Franken, Tim A1 - Shrestha, Krishna Prasad A1 - Seidel, Lars A1 - Mauß, Fabian ED - Sens, Marc T1 - Effect of Gasoline–Ethanol–Water Mixtures on Auto-Ignition in a Spark Ignition Engine T2 - International Conference on Knocking in Gasoline Engines N2 - The climate protection plan of the European Union requires a significant reduction of CO2 emissions from the transportation sector by 2030. Today ethanol is already blended by 10vol-% in gasoline and further increase of the ethanol content to 20vol-% is discussed. During the ethanol production process, distillation and molecular sieving is required to remove the water concentration to achieve high-purity ethanol. However, hydrous ethanol can be beneficial to suppress knock of spark ignition engines. The hygroscopic nature of ethanol can allow to increase the water content in gasoline – water emulsions even more, without adding additional surfactants, and improve the thermal efficiency by optimized combustion phasing, while keeping the system complexity low. Hence, the effect of gasoline – ethanol – water mixtures on the auto-ignition in a single-cylinder spark ignition engine is investigated by using multi-dimensional simulation and detailed chemistry. The gasoline – ethanol mixtures are defined to keep the Research Octane Number constant, while the Motored Octane Number is decreasing. In total five surrogates are defined and investigated: E10 (10vol-% ethanol-in-gasoline), E20, E30, E70 and E100. The water content is determined according to experimentally defined ternary diagrams that evaluated stable gasoline – ethanol – water emulsion at different gasoline – ethanol blending ratios. The auto-ignition modes of the surrogates are analyzed using the diagram, which determines if hotspots are within harmless deflagration or harmful developing detonation regime. The strongest auto-ignition is observed for the E10 surrogate, while increasing ethanol content reduces the surrogate reactivity and increases the resonance parameter. No auto-ignition of the unburnt mixture is observed for the E70 and E100 surrogates. The addition of hydrous ethanol decreased the excitation time of the surrogates, especially at low ethanol content, wherefor the reactivity parameter is significantly increased. The hotspots for E10, E20 and E30 surrogates with hydrous ethanol are found within the developing detonation regime, while hotspots of the E70 surrogate with hydrous ethanol are found in the transition regime. For the hydrous E100 surrogate no auto-ignition is predicted because of reduced temperature of the unburnt mixture due to water vaporization, which outweighs the increased reactivity due to water vapor addition. KW - Knock KW - Gasoline KW - Ethanol KW - Simulation KW - Detailed Chemistry KW - Spark Ignition Y1 - 2022 SN - 978-3-8169-3544-5 U6 - https://doi.org/10.24053/9783816985440 SP - 175 EP - 222 PB - expert CY - Tübingen ER -