@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} }