@inproceedings{NetzerSeidelLehtiniemietal., author = {Netzer, Corinna and Seidel, Lars and Lehtiniemi, Harry and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Impact of Formulation of Fuel Surrogates on Engine Knock Prediction}, series = {International Multidimensional Engine Modeling User's Group Meeting at the SAE Congress, April 9th , 2018, Detroit, USA}, booktitle = {International Multidimensional Engine Modeling User's Group Meeting at the SAE Congress, April 9th , 2018, Detroit, USA}, pages = {6}, language = {en} } @misc{FrankenDugganFengetal., author = {Franken, Tim and Duggan, Alexander and Feng, Tao and Borg, Anders and Lehtiniemi, Harry and Matrisciano, Andrea and Mauß, Fabian}, title = {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}, 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} } @inproceedings{NetzerSeidelLehtiniemietal., author = {Netzer, Corinna and Seidel, Lars and Lehtiniemi, Harry and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Impact of gasoline surrogates with different fuel sensitivity (RON-MON) on knock prediction}, series = {Proceedings of the 6th European Conference on Computational Mechanics (Solids, Structures and Coupled Problems) ECCM 6 and 7th European Conference on Computational Fluid Dynamics ECFD 7, Glasgow, Scotland, UK June 11 - 15, 2018}, booktitle = {Proceedings of the 6th European Conference on Computational Mechanics (Solids, Structures and Coupled Problems) ECCM 6 and 7th European Conference on Computational Fluid Dynamics ECFD 7, Glasgow, Scotland, UK June 11 - 15, 2018}, pages = {906 -- 917}, language = {en} } @misc{FrankenDugganTaoetal., author = {Franken, Tim and Duggan, Alexander and Tao, Feng and Matrisciano, Andrea and Lehtiniemi, Harry and Borg, Anders and Mauß, Fabian}, title = {Multi-Objective Optimization of Fuel Consumption and NOx Emissions of a heavy-duty Diesel engine using a Stochastic Reactor Model}, series = {SAE technical paper}, journal = {SAE technical paper}, number = {2019-01-1173}, issn = {0096-5170}, abstract = {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 …}, language = {en} } @misc{WernerMatriscianoNetzeretal., author = {Werner, Adina and Matrisciano, Andrea and Netzer, Corinna and Lehtiniemi, Harry and Borg, Anders and Seidel, Lars and Mauß, Fabian}, title = {Further Application of the Fast Tabulated CPV Approach}, doi = {10.13140/RG.2.2.18689.71529}, language = {en} } @misc{WernerNetzerLehtiniemietal., author = {Werner, Adina and Netzer, Corinna and Lehtiniemi, Harry and Borg, Anders and Matrisciano, Andrea and Seidel, Lars and Mauß, Fabian}, title = {A Computationally Efficient Combustion Progress Variable (CPV) Approach for Engine Applications}, doi = {10.13140/RG.2.2.15334.27209}, 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{FrankenDugganMatriscianoetal., author = {Franken, Tim and Duggan, Alexander and Matrisciano, Andrea and Lehtiniemi, Harry and Borg, Anders and Mauß, Fabian}, title = {Multi-Objective Optimization of Fuel Consumption and NO x Emissions with Reliability Analysis Using a Stochastic Reactor Model}, series = {SAE Technical Paper}, journal = {SAE Technical Paper}, issn = {0148-7191}, doi = {10.4271/2019-01-1173}, abstract = {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.}, language = {en} }