@misc{SeidelNetzerHilbigetal., author = {Seidel, Lars and Netzer, Corinna and Hilbig, Martin and Mauß, Fabian and Klauer, Christian and Pasternak, Michal and Matrisciano, Andrea}, title = {Systematic reduction of detailed chemical reaction mechanisms for engine applications}, series = {Journal of Engineering for Gas Turbines and Power}, volume = {139}, journal = {Journal of Engineering for Gas Turbines and Power}, number = {9}, issn = {1528-8919}, doi = {10.1115/1.4036093}, pages = {091701-1 -- 091701-9}, abstract = {In this work, we apply a sequence of concepts for mechanism reduction on one reaction mechanism including novel quality control. We introduce a moment-based accuracy rating method for species profiles. The concept is used for a necessity-based mechanism reduction utilizing 0D reactors. Thereafter a stochastic reactor model for internal combustion engines is applied to control the quality of the reduced reaction mechanism during the expansion phase of the engine. This phase is sensitive on engine out emissions, and is often not considered in mechanism reduction work. The proposed process allows to compile highly reduced reaction schemes for computational fluid dynamics application for internal combustion engine simulations. It is demonstrated that the resulting reduced mechanisms predict combustion and emission formation in engines with accuracies comparable to the original detailed scheme.}, language = {en} } @inproceedings{FrankenNetzerPasternaketal., author = {Franken, Tim and Netzer, Corinna and Pasternak, Michal and Mauß, Fabian and Seidel, Lars and Matrisciano, Andrea and Borg, Anders and Lehtiniemi, Harry and Kulzer, Andr{\´e} Casal}, title = {Assessment of Water Injection in a SI Engine using a Fast Running Detailed Chemistry Based Combustion Model}, series = {Symposium of Combustion Control 2018, Aachen}, booktitle = {Symposium of Combustion Control 2018, Aachen}, address = {Aachen}, pages = {10}, language = {en} } @misc{FrankenNetzerPasternaketal., author = {Franken, Tim and Netzer, Corinna and Pasternak, Michal and Mauß, Fabian and Seidel, Lars and Matrisciano, Andrea and Borg, Anders and Lehtiniemi, Harry and Kulzer, Andr{\´e} Casal}, title = {Simulation of Spark-Ignited Engines with Water Injection using the Stochastic Reactor Model, 37th International Symposium on Combustion}, language = {en} } @misc{NetzerSeidelPasternaketal., author = {Netzer, Corinna and Seidel, Lars and Pasternak, Michal and Lehtiniemi, Harry and Perlman, Cathleen and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Three-dimensional computational fluid dynamics engine knock prediction and evaluation based on detailed chemistry and detonation theory}, series = {International Journal of Engine Research}, volume = {19}, journal = {International Journal of Engine Research}, number = {1}, issn = {1468-0874}, doi = {10.1177/1468087417740271}, pages = {33 -- 44}, abstract = {Engine knock is an important phenomenon that needs consideration in the development of gasoline-fueled engines. In our days, this development is supported using numerical simulation tools to further understand and predict in-cylinder processes. In this work, a model tool chain which uses a detailed chemical reaction scheme is proposed to predict the auto-ignition behavior of fuels with different octane ratings and to evaluate the transition from harmless auto-ignitive deflagration to knocking combustion. In our method, the auto-ignition characteristics and the emissions are calculated using a gasoline surrogate reaction scheme containing pathways for oxidation of ethanol, toluene, n-heptane, iso-octane and their mixtures. The combustion is predicted using a combination of the G-equation based flame propagation model utilizing tabulated laminar flame speeds and well-stirred reactors in the burned and …}, language = {en} } @misc{NetzerPasternakSeideletal., author = {Netzer, Corinna and Pasternak, Michal and Seidel, Lars and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Computationally efficient prediction of cycle-to-cycle variations in spark-ignition engines}, series = {International Journal of Engine Research}, volume = {21}, journal = {International Journal of Engine Research}, number = {4}, issn = {2041-3149}, doi = {10.1177/1468087419856493}, pages = {649 -- 663}, abstract = {Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels' tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier-Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier-Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier-Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier-Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.}, language = {en} } @misc{FrankenNetzerMaussetal., author = {Franken, Tim and Netzer, Corinna and Mauß, Fabian and Pasternak, Michal and Seidel, Lars and Borg, Anders and Lehtiniemi, Harry and Matrisciano, Andrea and Kulzer, Andr{\´e} Casal}, title = {Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry}, series = {International Journal of Engine Research}, volume = {20}, journal = {International Journal of Engine Research}, number = {10}, issn = {2041-3149}, doi = {10.1177/1468087419857602}, pages = {1089 -- 1100}, abstract = {Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.}, language = {en} } @misc{FrankenMatriscianoSarietal., author = {Franken, Tim and Matrisciano, Andrea and Sari, Rafael and Robles, Alvaro Fogue and Monsalve-Serrano, Javier and Pintor, Dario Lopez and Pasternak, Michal and Garcia, Antonio and Mauß, Fabian}, title = {Modeling of Reactivity Controlled Compression Ignition Combustion Using a Stochastic Reactor Model Coupled with Detailed Chemistry}, series = {SAE technical papers : 15th International Conference on Engines \& Vehicles}, journal = {SAE technical papers : 15th International Conference on Engines \& Vehicles}, issn = {0148-7191}, doi = {10.4271/2021-24-0014}, pages = {18}, abstract = {Advanced combustion concepts such as reactivity controlled compression ignition (RCCI) have been proven to be capable of fundamentally improve the conventional Diesel combustion by mitigating or avoiding the soot-NOx trade-off, while delivering comparable or better thermal efficiency. To further facilitate the development of the RCCI technology, a robust and possibly computationally efficient simulation framework is needed. While many successful studies have been published using 3D-CFD coupled with detailed combustion chemistry solvers, the maturity level of the 0D/1D based software solution offerings is relatively limited. The close interaction between physical and chemical processes challenges the development of predictive numerical tools, particularly when spatial information is not available. The present work discusses a novel stochastic reactor model (SRM) based modeling framework capable of predicting the combustion process and the emission formation in a heavy-duty engine running under RCCI combustion mode. The combination of physical turbulence models, detailed emission formation sub-models and stateof-the-art chemical kinetic mechanisms enables the model to be computationally inexpensive compared to the 3D-CFD approaches. A chemical kinetic mechanism composed of 248 species and 1428 reactions was used to describe the oxidation of gasoline and diesel using a primary reference fuel (PRF)mixture and n-heptane, respectively. The model is compared to operating conditions from a single-cylinder research engine featuring different loads, speeds, EGR and gasoline fuel fractions. The model was found to be capable of reproducing the combustion phasing as well as the emission trends measured on the test bench, at some extent. The proposed modeling approach represents a promising basis towards establishing a comprehensive modeling framework capable of simulating transient operation as well as fuel property sweeps with acceptable accuracy.}, language = {en} } @misc{PicernoLeePasternaketal., author = {Picerno, Mario and Lee, Sung-Yong and Pasternak, Michal and Siddareddy, Reddy Babu and Franken, Tim and Mauß, Fabian and Andert, Jakob}, title = {Real-Time Emission Prediction with Detailed Chemistry under Transient Conditions for Hardware-in-the-Loop Simulations}, series = {Energies}, volume = {15}, journal = {Energies}, number = {1}, issn = {1996-1073}, doi = {10.3390/en15010261}, pages = {1 -- 21}, abstract = {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\%).}, language = {en} } @misc{SiddareddyFrankenPasternaketal., author = {Siddareddy, Reddy Babu and Franken, Tim and Pasternak, Michal and Leon de Syniawa, Larisa and Oder, Johannes and Rottengruber, Hermann and Mauß, Fabian}, title = {Real-Time Simulation of CNG Engine and After-Treatment System Cold Start. Part 1: Transient Engine-Out Emission Prediction Using a Stochastic Reactor Model}, series = {SAE Technical Paper}, journal = {SAE Technical Paper}, issn = {2688-3627}, doi = {10.4271/2023-01-0183}, abstract = {During cold start of natural gas engines, increased methane and formaldehyde emissions can be released due to flame quenching on cold cylinder walls, misfiring and the catalyst not being fully active at low temperatures. Euro 6 legislation does not regulate methane and formaldehyde emissions. New limits for these two pollutants have been proposed by CLOVE consortium for Euro 7 scenarios. These proposals indicate tougher requirements for aftertreatment systems of natural gas engines. In the present study, a zero-dimensional model for real-time engine-out emission prediction for transient engine cold start is presented. The model incorporates the stochastic reactor model for spark ignition engines and tabulated chemistry. The tabulated chemistry approach allows to account for the physical and chemical properties of natural gas fuels in detail by using a-priori generated laminar flame speed and combustion chemistry look-up tables. The turbulence-chemistry interaction within the combustion chamber is predicted using a K-k turbulence model. The optimum turbulence model parameters are trained by matching the experimental cylinder pressure and engine-out emissions of nine steady-state operating points. Subsequently, the trained engine model is applied for predicting engine-out emissions of a WLTP passenger car engine cold start. The predicted engine-out emissions comprise nitrogen oxide, carbon monoxide, carbon dioxide, unburnt methane, formaldehyde, and hydrogen. The simulation results are validated by comparing to transient engine measurements at different ambient temperatures (-7°C, 0°C, 8°C and 20°C). Additionally, the sensitivity of engine-out emissions towards air-fuel-ratio (λ=1.0 and λ=1.3) and natural gas quality (H-Gas and L-Gas) is investigated.}, language = {en} } @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} }