Corinna Netzer
Brandenburg University of Technology
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Featured researches published by Corinna Netzer.
ASME 2016 Internal Combustion Engine Fall Technical Conference, ICEF 2016 | 2016
Lars Seidel; Corinna Netzer; Martin Hilbig; Fabian Mauss; Christian Klauer; Michal Pasternak; Andrea Matrisciano
Copyright
International Journal of Engine Research | 2018
Corinna Netzer; Lars Seidel; Michal Pasternak; Harry Lehtiniemi; Cathleen Perlman; Frederic Ravet; Fabian Mauss
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 unburned zone in three-dimensional Reynolds-averaged Navier–Stokes. Based on the detonation theory by Bradley et al., the character and the severity of the auto-ignition event are evaluated. Using the suggested tool chain, the impact of fuel properties can be efficiently studied, the transition from harmless deflagration to knocking combustion can be illustrated and the severity of the auto-ignition event can be quantified.
SAE World Congress 2017, Detroit, United States, 4-6 April 2017 | 2017
Tim Franken; Arnd Sommerhoff; Werner Willems; Andrea Matrisciano; Harry Lehtiniemi; Anders Borg; Corinna Netzer; Fabian Mauss
Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry. The advantage of the probability density function approach compared to mean value models is its capability to account for temperature and mixture inhomogeneities. Therefore, notional particles are introduced each with its own temperature and composition. The particle condition is changed by mixing, injection, vaporization, chemical reaction and heat transfer. Mixing is modeled using the one-dimensional Euclidean minimum spanning tree mixing model, which requires the scalar mixing frequency as input. Therefore, a turbulence model is proposed to calculate the mixing time depending on turbulent kinetic energy and its dissipation. The turbulence model accounts for density, swirl, squish and injection effects on turbulent kinetic energy within the combustion chamber. Finally, the 0D stochastic reactor model is tested for 40 different operating points distributed over the whole engine map. The results show a close match of experimental heat release rate and NOx emissions. The trends of measured CO and HC concentrations are captured qualitatively. Additionally, the 0D simulation results are compared to more detailed 3D CFD combustion simulation results for three operating points differing in engine speed and load. The comparison shows that the 0D stochastic reactor model is able to capture turbulence effects on local temperature and mixture distribution, which in turn affect NOx, CO and HC emission formation. Overall, the 0D stochastic reactor model has proven its predictive capability for the investigated diesel engine and can be assigned to tasks concerning engine map simulation and parameter sweeps.
International Conference on Knocking in Gasoline Engines | 2017
Michal Pasternak; Corinna Netzer; Fabian Mauss; Michael Fischer; Marc Sens; Michael Riess
Combustion in a spark ignition engine operated at high speed and load is investigated numerically with regard to knock behavior. The study focuses on the concurrent impact of spark timing and exhaust gas recirculation (EGR) on the severity of knock. Specifically, the possibility of knock reduction through the lowering of nitrogen oxide (NO) content in the rest-gas is examined. Simulations are carried out using a stochastic reactor model of engine in-cylinder processes along with a quasi-dimensional turbulent flame propagation model and multicomponent gas-phase chemistry as gasoline surrogate. The knock-limited conditions are detected using the detonation diagram. By lowering the NO content in the external EGR the end-gas auto-ignition is suppressed. This prevents a transition to knocking combustion and enables advancing of spark timing that yields better combustion phasing. As a result, fuel economy is improved and the potential benefits of cleaning the EGR are indicated.
Journal of Engineering for Gas Turbines and Power-transactions of The Asme | 2017
Lars Seidel; Corinna Netzer; Martin Hilbig; Fabian Mauss; Christian Klauer; Michal Pasternak; Andrea Matrisciano
WCX™ 17: SAE World Congress ExperienceSAE International | 2017
Corinna Netzer; Lars Seidel; Michal Pasternak; Christian Klauer; Cathleen Perlman; Frederic Ravet; Fabian Mauss
SAE Technical Paper Series | 2018
Corinna Netzer; Tim Franken; Lars Seidel; Harry Lehtiniemi; Fabian Mauss
Archive | 2018
Corinna Netzer; Lars Seidel; Harry Lehtiniemi; Frederic Ravet; Fabian Mauß
Archive | 2017
Corinna Netzer; Lars Seidel; Michal Patsernak; Christian Klauer; Cathleen Perlman; Frederic Ravet; Fabian Mauß
Archive | 2017
Corinna Netzer; Lars Seidel; Harry Lehtiniemi; Frederic Ravet; Fabian Mauß