@misc{SiddareddyFrankenLeondeSyniawaetal., author = {Siddareddy, Reddy Babu and Franken, Tim and Leon de Syniawa, Larisa and Pasternak, Michal and Prehn, Sascha and Buchholz, Bert and Mauß, Fabian}, title = {Simulation of CNG Engine in Agriculture Vehicles. Part 1: Prediction of Cold Start Engine-Out Emissions Using Tabulated Chemistry and Stochastic Reactor Model}, series = {SAE Technical Paper}, journal = {SAE Technical Paper}, issn = {0148-7191}, doi = {10.4271/2023-24-0006}, abstract = {Worldwide, there is the demand to reduce harmful emissions from non-road vehicles to fulfill European Stage V+ and VI (2022, 2024) emission legislation. The rules require significant reductions in nitrogen oxides (NOx), methane (CH4) and formaldehyde (CH2O) emissions from non-road vehicles. Compressed natural gas (CNG) engines with appropriate exhaust aftertreatment systems such as threeway catalytic converter (TWC) can meet these regulations. An issue remains for reducing emissions during the engine cold start where the CNG engine and TWC yet do not reach their optimum operating conditions. The resulting complexity of engine and catalyst calibration can be efficiently supported by numerical models. Hence, it is required to develop accurate simulation models which can predict cold start emissions. This work presents a real-time engine model for transient engine-out emission prediction using tabulated chemistry for CNG. The engine model is based on a stochastic reactor model (SRM) which describes the in-cylinder processes of spark ignition (SI) engines including large-scale and lowscale turbulence, convective heat transfer, turbulent flame propagation and chemistry. Chemistry is described using a tabulated chemistry model which calculates the major exhaust gas emissions of CNG engines such as CO2, NOx, CO, CH4 and CH2O. By best practice, the engine model parameters are optimized by matching the experimental cylinder pressure and engine-out emissions from steady-state operating points. The engine model is trained for a non-road transient cycle (NRTC) cold start at 25°C ambient temperature and validated for a NRTC cold start at 10°C ambient temperature. The trained model is evaluated regarding their feasibility and accuracy predicting transient engineout emissions.}, language = {en} } @misc{PasternakSiddareddydeSyniawaetal., author = {Pasternak, Michał and Siddareddy, Reddy Babu and de Syniawa, Larisa Le{\´o}n and Guenther, Vivien and Picerno, Mario and Andert, Jakob and Franken, Tim and Mauss, Fabian and Adamczyk, Wojciech}, title = {Plant modelling of engine and aftertreatment systems for X-in-the-loop simulations with detailed chemistry}, series = {CONAT 2024 International Congress of Automotive and Transport Engineering.}, journal = {CONAT 2024 International Congress of Automotive and Transport Engineering.}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-77626-7}, doi = {10.1007/978-3-031-77627-4_14}, pages = {151 -- 163}, abstract = {Use of numerical simulations at early stage of engine and aftertreatment systems development helps in evaluating their different concepts and reducing the need for costly building of prototypes. In this work, we explore the feasibility of fully physical and chemical-based tool-chain for co-simulating engine in-cylinder and aftertreatment processes. Detailed gas-phase reaction kinetics and surface chemistry mechanisms are applied for the modeling of combustion, pollutants formation and aftertreatment, respectively. Engine in-cylinder performance parameters are simulated using a stochastic reactor model and multi-component fuel surrogate. The engine model is coupled with an aftertreatment model capable of simulating diesel oxidation catalyst (DOC), selective catalytic reduction catalyst, lean NOx trap, ammonia slip catalyst, and three-way catalyst. Both the engine and aftertreatment models are embedded within the Simulink framework. They work in co-simulation and are coupled using Functional Mock-up Interface (FMI) technology. The coupled framework acts as a virtual test bench that is developed given its application for X-in-the-Loop (XiL) simulations. The framework can be applied to engine steady state or transient operating conditions. Here, exemplary calculations are performed using a Model-in-the-Loop (MiL) approach. Simulations are conducted under transient conditions of Worldwide Harmonized Light Vehicle Test Cycle for a compression ignition engine coupled with a DOC. The presented framework is considered a first step towards complex engine plant modeling using detailed chemistry for the virtualization of the development of engine, fuels and aftertreatment systems.}, language = {en} } @misc{SiddareddyPasternakdeSyniawaetal., author = {Siddareddy, Reddy Babu and Pasternak, Michał and de Syniawa, Larisa Le{\´o}n and Guenther, Vivien and Seidel, Lars and Mauss, Fabian and Przybyła, Grzegorz and Adamczyk, Wojciech}, title = {Simulations of the SCR catalyst in ammonia-biodiesel fuelled CI engine using virtual test bench with detailed chemistry}, series = {Renewable energy}, volume = {251}, journal = {Renewable energy}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0960-1481}, doi = {10.1016/j.renene.2025.123169}, pages = {1 -- 12}, abstract = {The use of ammonia as an alternative fuel in the automotive industry is not yet fully established. Further research and development are required to account for both engine and aftertreatment systems, as well as their integration and control to ensure the most efficient use of ammonia. In this work, we present a fully physics and chemistry-based toolchain for co-simulating an ammonia-biodiesel fuelled compression ignition engine with a selective catalytic reduction catalyst. The investigations refer to experimental data from a single-cylinder research engine. This is a direct injection engine that was retrofitted to run on ammonia and biodiesel, the latter acting as a combustion promoter. Engine in-cylinder processes were simulated using a stochastic reactor model. Detailed gas phase chemistry is used to simulate the combustion process and pollutants formation. The catalyst model employs detailed surface chemistry that is trained using available data from literature. Eventually, the co-simulation toolchain was applied to investigate numerically the impact of the properties of the catalyst on ammonia reduction under engine-relevant operating conditions}, language = {en} }