@misc{NetzerSeidelPasternaketal., author = {Netzer, Corinna and Seidel, Lars and Pasternak, Michal and Klauer, Christian and Perlman, Cathleen and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Engine Knock Prediction and Evaluation Based on Detonation Theory Using a Quasi-Dimensional Stochastic Reactor Mode}, series = {SAE technical paper}, journal = {SAE technical paper}, number = {2017-01-0538}, issn = {0096-5170}, doi = {10.4271/2017-01-0538}, pages = {11 Seiten}, language = {en} } @misc{FrankenSommerhoffWillemsetal., author = {Franken, Tim and Sommerhoff, Arnd and Willems, Werner and Matrisciano, Andrea and Lehtiniemi, Harry and Borg, Anders and Netzer, Corinna and Mauß, Fabian}, title = {Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model}, series = {SAE technical paper}, journal = {SAE technical paper}, issn = {0148-7191}, doi = {10.4271/2017-01-0516}, language = {en} } @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} } @misc{NetzerFrankenLehtiniemietal., author = {Netzer, Corinna and Franken, Tim and Lehtiniemi, Harry and Mauß, Fabian and Seidel, Lars}, title = {Numerical Analysis of the Impact of Water Injection on Combustion and Thermodynamics in a Gasoline Engine using Detailed Chemistry}, series = {SAE technical papers}, journal = {SAE technical papers}, number = {2018-01-0200}, issn = {0148-7191}, doi = {10.4271/2018-01-0200}, pages = {14}, 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{NetzerSeidelRavetetal., author = {Netzer, Corinna and Seidel, Lars and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Impact of the surrogate formulation on 3D CFD engine knock prediction using detailed chemistry}, series = {Fuel}, volume = {Volume 254}, journal = {Fuel}, issn = {1873-7153}, doi = {10.1016/j.fuel.2019.115678}, pages = {13}, abstract = {For engine knock prediction, surrogate fuels are often composed of iso-octane and n-heptane since they are the components of the Primary Reference Fuel (PRF). By definition, a PRF has no octane sensitivity (S = RON-MON). However, for a commercial gasoline fuel holds RON > MON and therefor S > 0. More complex surrogates are Toluene Reference Fuels (TRF) and Ethanol containing Toluene Reference Fuels (ETRF). In this work, the impact of the surrogate formulation on the prediction of flame propagation and auto-ignition in the unburnt gases are investigated. The surrogates are composed such that the Research Octane Number is the same. The auto-ignition events ahead of the flame front are predicted using 3D CFD and a combustion model based on the ETRF mechanism by Seidel (2017). The strength of the auto-ignition is determined using the detonation diagram by Bradley and co-workers (2002, 2003). Applying the different surrogates, ignition kernels of different size and reactivity are predicted. The results indicate a dependency on the local temperature history and the low temperature chemistry of the fuel species. The comparison of homogenous constant volume reactor and transient simulations show that the analysis of ignition delay time and octane rating solely from homogenous simulations is not sufficient if the knock tendency of a surrogate in engine simulations needs to be characterized.}, language = {en} } @misc{NetzerSeidelRavetetal., author = {Netzer, Corinna and Seidel, Lars and Ravet, Fr{\´e}d{\´e}ric and Mauß, Fabian}, title = {Assessment of the validity of RANS knock prediction using the resonance theory}, series = {International Journal of Engine Research}, volume = {21}, journal = {International Journal of Engine Research}, number = {4}, issn = {2041-3149}, doi = {10.1177/1468087419846032}, pages = {610 -- 621}, abstract = {Following the resonance theory by Bradley and co-workers, engine knock is a consequence of an auto-ignition in the developing detonation regime. Their detonation diagram was developed using direct numerical simulations and was applied in the literature to engine knock assessment using large eddy simulations. In this work, it is analyzed if the detonation diagram can be applied for post-processing and evaluation of predicted auto-ignitions in Reynolds-averaged Navier-Stokes simulations even though the Reynolds-averaged Navier-Stokes approach cannot resolve the fine structures resolved in direct numerical simulations and large eddy simulations that lead to the prediction of a developing detonation. For this purpose, an engine operating point at the knock limit spark advance is simulated using Reynolds-averaged Navier-Stokes and large eddy simulations. The combustion is predicted using the G-equation and the well-stirred reactor model in the unburnt gases based on a detailed gasoline surrogate reaction scheme. All the predicted ignition kernels are evaluated using the resonance theory in a post-processing step. According to the different turbulence models, the predicted pressure rise rates and gradients differ. However, the predicted ignition kernel sizes and imposed gas velocities by the auto-ignition event are similar, which suggests that the auto-ignitions predicted by Reynolds-averaged Navier-Stokes simulations can be given a meaningful interpretation within the detonation diagram.}, 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{NetzerLiSeideletal., author = {Netzer, Corinna and Li, Tian and Seidel, Lars and Mauß, Fabian and L{\o}v{\aa}s, Terese}, title = {Stochastic Reactor-Based Fuel Bed Model for Grate Furnaces}, series = {Energy \& Fuels}, volume = {34}, journal = {Energy \& Fuels}, number = {12}, issn = {1520-5029}, doi = {10.1021/acs.energyfuels.0c02868}, pages = {16599 -- 16612}, abstract = {Biomass devolatilization and incineration in grate-fired plants are characterized by heterogeneous fuel mixtures, often incompletely mixed, dynamical processes in the fuel bed and on the particle scale, as well as heterogeneous and homogeneous chemistry. This makes modeling using detailed kinetics favorable but computationally expensive. Therefore, a computationally efficient model based on zero-dimensional stochastic reactors and reduced chemistry schemes, consisting of 83 gas-phase species and 18 species for surface reactions, is developed. Each reactor is enabled to account for the three phases: the solid phase, pore gas surrounding the solid, and the bulk gas. The stochastic reactors are connected to build a reactor network that represents the fuel bed in grate-fired furnaces. The use of stochastic reactors allows us to account for incompletely mixed fuel feeds, distributions of local temperature and local equivalence ratio within each reactor and the fuel bed. This allows us to predict the released gases and emission precursors more accurately than if a homogeneous reactor network approach was employed. The model approach is demonstrated by predicting pyrolysis conditions and two fuel beds of grate-fired plants from the literature. The developed approach can predict global operating parameters, such as the fuel bed length, species release to the freeboard, and species distributions within the fuel bed to a high degree of accuracy when compared to experiments.}, language = {en} } @misc{MatriscianoNetzerWerneretal., author = {Matrisciano, Andrea and Netzer, Corinna and Werner, Adina and Borg, Anders and Seidel, Lars and Mauß, Fabian}, title = {A Computationally Efficient Progress Variable Approach for In-Cylinder Combustion and Emissions Simulations}, series = {SAE Technical Paper}, journal = {SAE Technical Paper}, issn = {0148-7191}, doi = {10.4271/2019-24-0011}, abstract = {The use of complex reaction schemes is accompanied by high computational cost in 3D CFD simulations but is particularly important to predict pollutant emissions in internal combustion engine simulations. One solution to tackle this problem is to solve the chemistry prior the CFD run and store the chemistry information in look-up tables. The approach presented combines pre-tabulated progress variable-based source terms for auto-ignition as well as soot and NOx source terms for emission predictions. The method is coupled to the 3D CFD code CONVERGE v2.4 via user-coding and tested over various speed and load passenger-car Diesel engine conditions. This work includes the comparison between the combustion progress variable (CPV) model and the online chemistry solver in CONVERGE 2.4. Both models are compared by means of combustion and emission parameters. A detailed n-decane/α-methyl-naphthalene mechanism, comprising 189 species, is used for both online and tabulated chemistry simulations. The two chemistry solvers show very good agreement between each other and equally predict trends derived experimentally by means of engine performance parameters as well as soot and NOx engine-out emissions. The CPV model shows a factor 8 speed-up in run-time compared to the online chemistry solver without compromising the accuracy of the solution.}, language = {en} }