TY - GEN A1 - Franken, Tim A1 - Klauer, Christian A1 - Kienberg, Martin A1 - Matrisciano, Andrea A1 - Mauß, Fabian T1 - Prediction of thermal stratification in an engine-like geometry using a zero-dimensional stochastic reactor model T2 - International Journal of Engine Research KW - Prediction of thermal stratification Y1 - 2019 UR - https://journals.sagepub.com/eprint/5AKgEhAVA8RDS2tQWdGK/full SN - 2041-3149 U6 - https://doi.org/10.1177/1468087418824217 SN - 1468-0874 ER - TY - GEN A1 - Siddareddy, Reddy Babu A1 - Franken, Tim A1 - Pasternak, Michal A1 - Leon de Syniawa, Larisa A1 - Oder, Johannes A1 - Rottengruber, Hermann A1 - Mauß, Fabian T1 - Real-Time Simulation of CNG Engine and After-Treatment System Cold Start. Part 1: Transient Engine-Out Emission Prediction Using a Stochastic Reactor Model T2 - SAE Technical Paper N2 - 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. KW - Spark Ignition Engines KW - Gas Engines KW - Alternative Fuel Engines KW - Natural Gas KW - Nitrogen Oxides KW - Cold Start KW - Carbon Monoxide KW - Methane KW - Formaldehyde KW - Simulation KW - Stochastic Reactor Model KW - Tabulated Chemistry Y1 - 2023 U6 - https://doi.org/10.4271/2023-01-0183 SN - 2688-3627 SN - 0148-7191 ER - TY - GEN A1 - Leon de Syniawa, Larisa A1 - Siddareddy, Reddy Babu A1 - Oder, Johannes A1 - Franken, Tim A1 - Günther, Vivien A1 - Rottengruber, Hermann A1 - Mauß, Fabian T1 - Real-Time Simulation of CNG Engine and After-Treatment System Cold Start. Part 2: Tail-Pipe Emissions Prediction Using a Detailed Chemistry Based MOC Model T2 - SAE Technical Report N2 - In contrast to the currently primarily used liquid fuels (diesel and gasoline), methane (CH4) as a fuel offers a high potential for a significant reduction of greenhouse gas emissions (GHG). This advantage can only be used if tailpipe CH4 emissions are reduced to a minimum, since the GHG impact of CH4 in the atmosphere is higher than that of carbon dioxide (CO2). Three-way catalysts (TWC - stoichiometric combustion) and methane oxidation catalysts (MOC - lean combustion) can be used for post-engine CH4 oxidation. Both technologies allow for a nearly complete CH4 conversion to CO2 and water at sufficiently high exhaust temperatures (above the light-off temperature of the catalysts). However, CH4 combustion is facing a huge challenge with the planned introduction of Euro VII emissions standard, where stricter CH4 emission limits and a decrease of the cold start starting temperatures are discussed. The aim of the present study is to develop a reliable kinetic catalyst model for MOC conversion prediction in order to optimize the catalyst design in function of engine operation conditions, by combining the outputs from the predicted transient engine simulations as inputs to the catalyst model. Model development and training has been performed using experimental engine test bench data at stoichiometric conditions as well as engine simulation data and is able to reliably predict the major emissions under a broad range of operating conditions. Cold start (-7°C and +20°C) experiments were performed for a simplified worldwide light vehicle test procedure (WLTP) driving cycle using a prototype gas engine together with a MOC. For the catalyst simulations, a 1-D catalytic converter model was used. The model includes detailed gas and surface chemistry that are computed together with catalyst heat up. In a further step, a virtual transient engine cold start cycle is combined with the MOC model to predict tail-pipe emissions at transient operating conditions. This method allows to perform detailed emission investigations in an early stage of engine prototype development. KW - Exhaust Emissions KW - Tail Pipe Emissions KW - Three Way Catalyst KW - Gas Engines KW - Cold Start KW - Simulation KW - Detailed Chemistry KW - Methane Oxidation Catalyst KW - Methane KW - Co-Simulation KW - Catalysts Y1 - 2023 U6 - https://doi.org/10.4271/2023-01-0364 SN - 2688-3627 SN - 0148-7191 ER - TY - CHAP A1 - Franken, Tim A1 - Netzer, Corinna A1 - Pasternak, Michal A1 - Mauß, Fabian A1 - Seidel, Lars A1 - Matrisciano, Andrea A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Kulzer, André Casal T1 - Assessment of Water Injection in a SI Engine using a Fast Running Detailed Chemistry Based Combustion Model T2 - Symposium of Combustion Control 2018, Aachen KW - Assessment of Water Injection Y1 - 2018 UR - https://www.researchgate.net/publication/326059620 UR - http://logesoft.com/loge-16/wp-content/uploads/2018/07/2018-06-19-SCC_-1.pdf CY - Aachen ER - TY - CHAP A1 - Franken, Tim A1 - Duggan, Alexander A1 - Feng, Tao A1 - Mauß, Fabian T1 - Multi-Objective Optimization of Fuel Consumption and NOx Emissions for a Heavy-Duty Direct Injection Diesel Engine T2 - European ESTECO User Meeting 2018, Trieste, Italy KW - Multi-Objective Optimization of Fuel Consumption Y1 - 2018 UR - https://www.researchgate.net/publication/325477898 ER - TY - GEN A1 - Leon de Syniawa, Larisa A1 - Siddareddy, Reddy Babu A1 - Prehn, Sascha A1 - Günther, Vivien A1 - Franken, Tim A1 - Buchholz, Bert A1 - Mauß, Fabian T1 - Simulation of CNG Engine in Agriculture Vehicles. Part 2: Coupled Engine and Exhaust Gas Aftertreatment Simulations Using a Detailed TWC Model T2 - SAE Technical Paper N2 - In more or less all aspects of life and in all sectors, there is a generalized global demand to reduce greenhouse gas (GHG) emissions, leading to the tightening and expansion of existing emissions regulations. Currently, non-road engines manufacturers are facing updates such as, among others, US Tier 5 (2028), European Stage V (2019/2020), and China Non-Road Stage IV (in phases between 2023 and 2026). For on-road applications, updates of Euro VII (2025), China VI (2021), and California Low NOx Program (2024) are planned. These new laws demand significant reductions in nitrogen oxides (NOx) and particulate matter (PM) emissions from heavy-duty vehicles. When equipped with an appropriate exhaust aftertreatment system, natural gas engines are a promising technology to meet the new emission standards. Gas engines require an appropriate aftertreatment technology to mitigate additional GHG releases as natural gas engines have challenges with methane (CH4) emissions that have 28 times more global warming potential compared to CO2. Under stoichiometric conditions a three-way catalytic converter (TWC - stoichiometric combustion) can be used to effectively reduce emissions of harmful pollutants such as nitrogen oxides and carbon monoxide (CO) as well as GHG like methane. The aim of the present study is to understand the performance of the catalytic converter in function of the engine operation and coolant temperature in order to optimize the catalyst operating conditions. Different cooling temperatures are chosen as the initial device temperature highly affects the level of warm up emissions such that low coolant temperatures entail high emissions. In order to investigate the catalyst performance, experimental and virtual transient engine emissions are coupled with a TWC model to predict tail-pipe emissions at transient operating conditions. Engine experiments are conducted at two initial engine coolant temperatures (10°C and 25°C) to study the effects on the Non-Road Transient Cycle (NRTC) emissions. Engine simulations of combustion and emissions with acceptable accuracy and with low computational effort are developed using the Stochastic Reactor Model (SRM). Catalyst simulations are performed using a 1D catalytic converter model including detailed gas and surface chemistry. The initial section covers essential aspects including the engine setup, definition of the engine test cycle, and the TWC properties and setup. Subsequently, the study introduces the transient SI-SRM, 1D catalyst model, and kinetic model for the TWC. The TWC model is used for the validation of a NRTC at different coolant temperatures (10°C and 25°C) during engine start. Moving forward, the next section includes the coupling of the TWC model with measured engine emissions. Finally, a virtual engine parameter variation has been performed and coupled with TWC simulations to investigate the performance of the engine beyond the experimental campaign. Various engine operating conditions (lambda variation for this paper) are virtually investigated, and the performance of the engine can be extrapolated. The presented virtual development approach allows comprehensive emission evaluations during the initial stages of engine prototype development KW - CNG KW - Cold start KW - Afterteatment KW - Three-Way Catalyst KW - Surface chemistry KW - Simulation Y1 - 2023 U6 - https://doi.org/10.4271/2023-24-0112 SN - 0148-7191 SN - 2688-3627 ER - TY - GEN A1 - Siddareddy, Reddy Babu A1 - Franken, Tim A1 - Leon de Syniawa, Larisa A1 - Pasternak, Michal A1 - Prehn, Sascha A1 - Buchholz, Bert A1 - Mauß, Fabian T1 - Simulation of CNG Engine in Agriculture Vehicles. Part 1: Prediction of Cold Start Engine-Out Emissions Using Tabulated Chemistry and Stochastic Reactor Model T2 - SAE Technical Paper N2 - 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. KW - CNG engine KW - Cold start KW - Stochastic reactor model KW - Tabulated chemistry KW - Natural gas KW - Driving cycle Y1 - 2023 U6 - https://doi.org/10.4271/2023-24-0006 SN - 0148-7191 SN - 2688-3627 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 - 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 - Matrisciano, Andrea A1 - Sari, Rafael A1 - Robles, Alvaro Fogue A1 - Monsalve-Serrano, Javier A1 - Pintor, Dario Lopez A1 - Pasternak, Michal A1 - Garcia, Antonio A1 - Mauß, Fabian T1 - Modeling of Reactivity Controlled Compression Ignition Combustion Using a Stochastic Reactor Model Coupled with Detailed Chemistry T2 - SAE technical papers : 15th International Conference on Engines & Vehicles N2 - 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. KW - Stochastic Reactor Models KW - RCCI KW - Chemical Kinetics KW - Low Temperature Combustion Y1 - 2021 UR - https://www.sae.org/publications/technical-papers/content/2021-24-0014/ U6 - https://doi.org/10.4271/2021-24-0014 SN - 0148-7191 SN - 2688-3627 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 - TY - GEN A1 - Franken, Tim A1 - Seidel, Lars A1 - Shrestha, Krishna Prasad A1 - Gonzalez Mestre, Laura Catalina A1 - Mauß, Fabian T1 - Multi-objective Optimization of Gasoline, Ethanol, and Methanol in Spark Ignition Engines N2 - 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. KW - Optimization KW - Methanol KW - Ethanol KW - Spark Ignition Engine KW - Gasoline KW - Simulation Y1 - 2021 UR - https://www.researchgate.net/publication/351688526_Multi-objective_Optimization_of_Gasoline_Ethanol_and_Methanol_in_Spark_Ignition_Engines ER - TY - GEN A1 - Franken, Tim A1 - Mauß, Fabian A1 - Seidel, Lars A1 - Gern, Maike Sophie A1 - Kauf, Malte A1 - Matrisciano, Andrea A1 - Kulzer, Andre Casal T1 - Gasoline engine performance simulation of water injection and low-pressure exhaust gas recirculation using tabulated chemistry T2 - International Journal of Engine Research N2 - This work presents the assessment of direct water injection in spark-ignition engines using single cylinder experiments and tabulated chemistry-based simulations. In addition, direct water injection is compared with cooled low-pressure exhaust gas recirculation at full load operation. The analysis of the two knock suppressing and exhaust gas cooling methods is performed using the quasi-dimensional stochastic reactor model with a novel dual fuel tabulated chemistry model. To evaluate the characteristics of the autoignition in the end gas, the detonation diagram developed by Bradley and coworkers is applied. The single cylinder experiments with direct water injection outline the decreasing carbon monoxide emissions with increasing water content, while the nitrogen oxide emissions indicate only a minor decrease. The simulation results show that the engine can be operated at l = 1 at full load using water–fuel ratios of up to 60% or cooled low-pressure exhaust gas recirculation rates of up to 30%. Both technologies enable the reduction of the knock probability and the decrease in the catalyst inlet temperature to protect the aftertreatment system components. The strongest exhaust temperature reduction is found with cooled low-pressure exhaust gas recirculation. With stoichiometric air–fuel ratio and water injection, the indicated efficiency is improved to 40% and the carbon monoxide emissions are reduced. The nitrogen oxide concentrations are increased compared to the fuel-rich base operating conditions and the nitrogen oxide emissions decrease with higher water content. With stoichiometric air–fuel ratio and exhaust gas recirculation, the indicated efficiency is improved to 43% and the carbon monoxide emissions are decreased. Increasing the exhaust gas recirculation rate to 30% drops the nitrogen oxide emissions below the concentrations of the fuel-rich base operating conditions. KW - Water Injection KW - Exhaust Gas Recirculation KW - Efficiency KW - Spark Ignition Engine KW - Stochastic Reactor Model KW - Emissions Y1 - 2020 UR - https://journals.sagepub.com/doi/abs/10.1177/1468087420933124 U6 - https://doi.org/10.1177/1468087420933124 SN - 2041-3149 SN - 1468-0874 VL - 21 IS - 10 SP - 1857 EP - 1877 ER - TY - GEN A1 - Franken, Tim A1 - Seidel, Lars A1 - Matrisciano, Andrea A1 - Mauß, Fabian A1 - Kulzer, Andre Casal A1 - Schuerg, Frank T1 - Analysis of the Water Addition Efficiency on Knock Suppression for Different Octane Ratings T2 - SAE World Congress N2 - Water injection can be applied to spark ignited gasoline engines to increase the Knock Limit Spark Advance and improve the thermal efficiency. The Knock Limit Spark Advance potential of 6 °CA to 11 °CA is shown by many research groups for EN228 gasoline fuel using experimental and simulation methods. The influence of water is multi-layered since it reduces the in-cylinder temperature by vaporization and higher heat capacity of the fresh gas, it changes the chemical equilibrium in the end gas and increases the ignition delay and decreases the laminar flame speed. The aim of this work is to extend the analysis of water addition to different octane ratings. The simulation method used for the analysis consists of a detailed reaction scheme for gasoline fuels, the Quasi-Dimensional Stochastic Reactor Model and the Detonation Diagram. The detailed reaction scheme is used to create the dual fuel laminar flame speed and combustion chemistry look-up tables. The Detonation Diagram is used as a novel approach in the Quasi-Dimensional Stochastic Reactor Model to evaluate the auto-ignition characteristic in the end gas and determine if it is a harmless deflagration or developing detonation. First, the Quasi-Dimensional Stochastic Reactor Model is trained for three engine operating points and a RON95 E10 fuel. Its performance is evaluated based on experimental results of a single cylinder research engine. Subsequently, different spark timings and water-fuel ratios are investigated for different Primary Reference Fuels. The results outline that water addition can effectively reduce the strength of auto-ignition in the end gas for different Primary Reference Fuels. Thereby, it can be stated that the reduction of the auto-ignition strength through water addition by 50 – 80 % water-fuel ratio for high octane number fuels corresponds to the spark timing delay of 6 °CA or an increase of research octane number by 10 points. KW - Gasoline KW - Knock KW - Water KW - Engines KW - Combustion KW - Simulation Y1 - 2020 U6 - https://doi.org/10.4271/2020-01-0551 SN - 2688-3627 SN - 0148-7191 ER - TY - GEN A1 - Franken, Tim A1 - Duggan, Alexander A1 - Tao, Feng A1 - Matrisciano, Andrea A1 - Lehtiniemi, Harry A1 - Borg, Anders A1 - Mauß, Fabian T1 - Multi-Objective Optimization of Fuel Consumption and NOx Emissions of a heavy-duty Diesel engine using a Stochastic Reactor Model T2 - SAE technical paper N2 - Highly fuel-efficient Diesel engines, combined with effective exhaust aftertreatment systems, enable an economic and low-emission operation of heavy-duty vehicles. The challenge of its development arises from the present engine complexity, which is expected to increase even more in the future. The approved method of test bench measurements is stretched to its limits, because of the high demand for large parameter variations. The introduction of a physics-based quasi-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of these Diesel engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during … KW - Highly fuel-efficient Diesel engines Y1 - 2019 SN - 0096-5170 SN - 0148-7191 IS - 2019-01-1173 ER - TY - GEN A1 - Vacca, Antonino A1 - Bargende, Michael A1 - Chiodi, Marco A1 - Netzer, Corinna A1 - Gern, Maike Sophie A1 - Kauf, Georg Malte A1 - Kulzer, André Casal A1 - Franken, Tim T1 - Analysis of Water Injection Strategies to Exploit the Thermodynamic Effects of Water in Gasoline Engines by Means of a 3D-CFD Virtual Test Bench N2 - CO2 emission constraints taking effect from 2020 lead to further investigations of technologies to lower knock sensitivity of gasoline engines, main limiting factor to increase engine efficiency and thus reduce fuel consumption. Moreover the RDE cycle demands for higher power operation, where fuel enrichment is needed for component protection. To achieve high efficiency, the engine should be run at stoichiometric conditions in order to have better emission control and reduce fuel consumption. Among others, water injection is a promising technology to improve engine combustion efficiency, by mainly reducing knock sensitivity and to keep high conversion rates of the TWC over the whole engine map. The comprehension of multiple thermodynamic effects of water injection through 3D-CFD simulations and their exploitation to enhance the engine combustion efficiency is the main purpose of the analysis. As basis for the research a single cylinder engine derived from a 1l turbocharged 3-cylinders engine is used to evaluate indirect and direct water injection. The entire engine flow field is reproduced and analyzed with 3D-CFD simulations and numerical models are employed to separate the influence of chemical and thermodynamic properties. Measurements are performed with different injectors for indirect/direct water injection in the single-cylinder engine in order to assess water break-up, wall wetting, spray interaction and penetration. Several injection strategies, such as varying start of injection, injection pressure, and water to fuel ratio, are tested at the single-cylinder engine test bench. Detailed gas phase chemistry is employed to link flame front speed with water concentration and knocking occurrence. These results are correlated with the 3D-CFD simulation of mixture formation, in-cylinder flow and water distribution for two different operating points (part load and maximum power) in order to study water behavior, with focus on the evaporation process, in-cylinder pressure and temperature profile, as well as the combustion development, during multiple engine cycles. KW - Water Injection KW - Computational Fluid Dynamics KW - Simulation KW - Virtual Test Bench KW - Thermodynamics Y1 - 2019 UR - https://saemobilus.sae.org/content/2019-24-0102 U6 - https://doi.org/10.4271/2019-24-0102 PB - SAE International CY - Neapel ER - TY - GEN A1 - Franken, Tim A1 - Netzer, Corinna A1 - Mauß, Fabian A1 - Pasternak, Michal A1 - Seidel, Lars A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Matrisciano, Andrea A1 - Kulzer, André Casal T1 - Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry T2 - International Journal of Engine Research N2 - 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. KW - Water Injection KW - Genetic Optimization KW - Spark Ignition Engine KW - Stochastic Reactor Model KW - Detailed Chemistry Y1 - 2019 UR - https://journals.sagepub.com/doi/full/10.1177/1468087419857602 U6 - https://doi.org/10.1177/1468087419857602 SN - 2041-3149 VL - 20 IS - 10 SP - 1089 EP - 1100 ER - TY - GEN A1 - Gern, Maike Sophie A1 - Kauf, Georg Malte A1 - Vacca, Antonino A1 - Franken, Tim A1 - Kulzer, André Casal T1 - Ganzheitliche Methode zur Bewertung der Wassereinspritzung T2 - MTZ - Motortechnische Zeitschrift N2 - Die künftige Effizienzsteigerung bei Ottomotoren mit Direkteinspritzung sowie die Reduktion von gas- und partikelförmigen Emissionen erfordern innovative Ansätze. Durch die neue RDEGesetzgebung für leichte Kraftfahrzeuge verschiebt sich der Fokus hin zu dynamischen Testzyklen mit 12 % mehr Hochlastbetriebspunkten im Vergleich zum WLTC (Worldwide harmonized Light Duty Test Cycle). Die Steigerung des Mitteldrucks bei Ottomotoren führt zu einem effizienten Betrieb, der jedoch durch das Risiko irregulärer Verbrennung und hohen Abgastemperaturen limitiert ist. Durch den Einsatz von Wassereinspritzung können sowohl die Klopfneigung als auch die Abgastemperatur reduziert werden, was den Schutz der Abgasnachbehandlungskomponenten bei gleichzeitiger Wirkungsgradsteigerung ermöglicht. Im Rahmen des FVV-Projekts „Wassereinspritzung bei Ottomotoren“ wird der Einfluss der NiederdruckWassereinspritzung (NDWE) und Hochdruck-Wassereinspritzung (HDWE) untersucht. Insbesondere die Auswirkungen auf Gemischbildung, Verbrennung und Abgasnachbehandlung werden durch die Vernetzung von Experiment und Simulation analysiert. KW - Water Injection KW - Wassereinspritzung KW - Gasoline KW - Simulation KW - Efficiency Y1 - 2019 U6 - https://doi.org/10.1007/s35146-019-0070-x SN - 2192-8843 VL - 80 IS - 7-8 SP - 124 EP - 129 ER - TY - GEN A1 - Netzer, Corinna A1 - Franken, Tim A1 - Lehtiniemi, Harry A1 - Mauß, Fabian A1 - Seidel, Lars T1 - Numerical Analysis of the Impact of Water Injection on Combustion and Thermodynamics in a Gasoline Engine using Detailed Chemistry T2 - SAE technical papers Y1 - 2018 U6 - https://doi.org/10.4271/2018-01-0200 SN - 0148-7191 SN - 0096-5170 IS - 2018-01-0200 ER - TY - GEN A1 - Franken, Tim A1 - Duggan, Alexander A1 - Feng, Tao A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Matrisciano, Andrea A1 - Mauß, Fabian T1 - Multi-Objective Optimization of Fuel Consumption and NOx Emissions using a Stochastic Reactor Model, THIESEL 2018 Conference on Thermo- and Fluid Dynamic Processes in Direct Injection Engines KW - Multi-Objective Optimization Y1 - 2018 UR - https://www.researchgate.net/publication/328265385 ER - TY - GEN A1 - Franken, Tim A1 - Netzer, Corinna A1 - Pasternak, Michal A1 - Mauß, Fabian A1 - Seidel, Lars A1 - Matrisciano, Andrea A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Kulzer, André Casal T1 - Simulation of Spark-Ignited Engines with Water Injection using the Stochastic Reactor Model, 37th International Symposium on Combustion Y1 - 2018 UR - https://www.researchgate.net/publication/328265636 ER - TY - GEN A1 - Franken, Tim A1 - Netzer, Corinna A1 - Mauß, Fabian T1 - Water Injection in Spark-Ignition Engines, FVV Autumn Conference 2018 Y1 - 2018 UR - https://www.researchgate.net/publication/328265540 ER - TY - GEN A1 - Matrisciano, Andrea A1 - Franken, Tim A1 - Perlman, Cathleen A1 - Borg, Anders A1 - Lehtiniemi, Harry A1 - Mauß, Fabian T1 - Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model T2 - SAE technical papers KW - Development of a Computationally Efficient Progress Y1 - 2017 U6 - https://doi.org/10.4271/2017-01-0512 SN - 0148-7191 SN - 0096-5170 IS - 2017-01-0512 SP - 18 Seiten ER - TY - GEN A1 - Franken, Tim A1 - Sommerhoff, Arnd A1 - Willems, Werner A1 - Matrisciano, Andrea A1 - Lehtiniemi, Harry A1 - Borg, Anders A1 - Netzer, Corinna A1 - Mauß, Fabian T1 - Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model T2 - SAE technical paper KW - Advanced Predictive Y1 - 2017 UR - http://papers.sae.org/2017-01-0516 U6 - https://doi.org/10.4271/2017-01-0516 SN - 0148-7191 SN - 0096-5170 N1 - WCX™ 17: SAE World Congress Experience ER - TY - GEN A1 - Franken, Tim A1 - Mauß, Fabian T1 - Development of Methodology for Predictive Diesel Combustion Simulation Using 0D Stochastic Reactor Model T2 - SAE Technical Papers N2 - Stringent exhaust emission limits and new vehicle test cycles require sophisticated operating strategies for future diesel engines. Therefore, a methodology for predictive combustion simulation, focused on multiple injection operating points is proposed in this paper. The model is designated for engine performance map simulations, to improve prediction of NOx, CO and HC emissions.The combustion process is calculated using a zero dimensional direct injection stochastic reactor model based on a probability density function approach. Further, the formation of exhaust emissions is described using a detailed reaction mechanism for n-heptane, which involves 56 Species and 206 reactions. The model includes the interaction between turbulence and chemistry effects by using a variable mixing time profile. Thus, one is able to capture the effects of mixture inhomogeneities on NOx, CO and HC emission formation.The mixing time model is parameterized using transfer functions for engine operating parameters, e.g., injection mass, injection duration, air fuel ratio, start of injection and speed. These functions are calibrated for nine operating points using multi objective simulated annealing optimization combined with fast running metamodels that speed up the optimization process. The calibrated transfer functions are validated for nine additional operating points. The results for the calibration and validation points show a good match of the combustion heat release rate. Especially the main injection heat release rate is well predicted by the model. The NOx and CO emissions reflect the experimental trends and are in close range to the measurements. Finally, the model is tested for triple injection operating points. The results match the measurements, which show the applicability of the stochastic reactor model in conjunction with the mixing time transfer functions for engine performance map simulations. KW - Mathematical analysis KW - Diesel / Compression Ignition engines Y1 - 2016 U6 - https://doi.org/10.4271/2016-01-0566 SN - 0148-7191 SN - 0096-5170 IS - 2016-01-0566 SP - 14 Seiten ER - TY - GEN A1 - Franken, Tim A1 - Duggan, Alexander A1 - Matrisciano, Andrea A1 - Lehtiniemi, Harry A1 - Borg, Anders A1 - Mauß, Fabian T1 - Multi-Objective Optimization of Fuel Consumption and NO x Emissions with Reliability Analysis Using a Stochastic Reactor Model T2 - SAE Technical Paper N2 - The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low. This work presents a novel methodology to couple the stochastic reactor model and the Non-dominated Sorting Genetic Algorithm. First, the stochastic reactor model is calibrated for 10 low, medium and high load operating points at various engine speeds. Second, each operating point is optimized to find the lowest fuel consumption and specific NOx emissions. The optimization input parameters are the temperature at intake valve closure, the compression ratio, the start of injection, the injection pressure and exhaust gas recirculation rate. Additionally, it is ensured that the maximum peak cylinder pressure and turbine inlet temperature are not exceeded. This enables a safe operation of the engine and exhaust aftertreatment system under the optimized conditions. Subsequently, a reliability analysis is performed to estimate the effect of off-nominal conditions on the objectives and constraints. The novel multi-objective optimization methodology has proven to deliver reasonable results. The zero-dimensional stochastic reactor model with tabulated chemistry is a fast running physics-based model that allow to run large optimization problems in a short amount of time. The combination with the reliability analysis also strengthens the confidence in the simulation-based optimized engine operation parameters. Y1 - 2019 U6 - https://doi.org/10.4271/2019-01-1173 SN - 0148-7191 SN - 2688-3627 ER - TY - GEN A1 - Franken, Tim A1 - Seidel, Lars A1 - Gonzalez Mestre, Laura Catalina A1 - Shrestha, Krishna Prasad A1 - Matrisciano, Andrea A1 - Mauss, Fabian T1 - Assessment of Auto-Ignition Tendency of Gasoline, Methanol, Toluene and Hydrogen Fuel Blends in Spark Ignition Engines T2 - THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines N2 - 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. Y1 - 2020 UR - https://hal.science/hal-03573870 ER - TY - GEN A1 - Matrisciano, Andrea A1 - Franken, Tim A1 - Gonzales Mestre, Laura Catalina A1 - Borg, Anders A1 - Mauß, Fabian T1 - Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models T2 - Applied Sciences N2 - 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. Y1 - 2020 U6 - https://doi.org/10.3390/app10248979 SN - 2076-3417 VL - 10 IS - 24 ER -