@inproceedings{MaussLehtiniemiNetzell, author = {Mauß, Fabian and Lehtiniemi, Harry and Netzell, Karl}, title = {Calculating soot in a turbulent jet diffision flame using the unsteady flamelet model}, series = {Proceedings of the European Combustion Meeting, Louvain-la-Neuve, Belgium, April 3 - 6, 2005}, booktitle = {Proceedings of the European Combustion Meeting, Louvain-la-Neuve, Belgium, April 3 - 6, 2005}, publisher = {Combustion Inst.}, address = {Louvain-la-Neuve}, language = {en} } @inproceedings{MaussNetzellLehtiniemietal., author = {Mauß, Fabian and Netzell, Karl and Lehtiniemi, Harry and Gogan, Adina and Sund{\´e}n, Bengt}, title = {Aspects of Modeling Soot Formation in Turbulent Diffusion Flames}, language = {en} } @misc{WenzelGezginSteineretal., author = {Wenzel, Paul and Gezgin, A. and Steiner, R{\"u}diger and Kr{\"u}ger, Christian and Netzell, Karl and Lehtiniemi, Harry and Mauß, Fabian}, title = {Modeling of the soot particle size distribution in diesel engines}, series = {Conference proceedings, Conference on Thermo- and Fluid Dynamic Processes in Diesel Engines, September 12th - 15th 2006, Valencia, Spain}, journal = {Conference proceedings, Conference on Thermo- and Fluid Dynamic Processes in Diesel Engines, September 12th - 15th 2006, Valencia, Spain}, publisher = {Univ. Polit{\´e}cnica}, address = {Valencia}, isbn = {84-9705-982-4}, pages = {397 -- 410}, language = {en} } @inproceedings{SamuelssonGoganNetzelletal., author = {Samuelsson, K. and Gogan, Adina and Netzell, Karl and Lehtiniemi, Harry and Sund{\´e}n, Bengt and Mauß, Fabian}, title = {Modeling Diesel Engine Combustion and Pollutant Formation using a Stochastic Reactor Model Approach}, series = {Book of abstracts, Fifth Symposium Towards Clean Diesel Engines, 2 - 3 June 2005, Lund, Sweden}, booktitle = {Book of abstracts, Fifth Symposium Towards Clean Diesel Engines, 2 - 3 June 2005, Lund, Sweden}, publisher = {Univ., Lund Institute of Technology}, address = {Lund}, language = {en} } @article{MaussNetzellLehtiniemi, author = {Mauß, Fabian and Netzell, Karl and Lehtiniemi, Harry}, title = {"Aspects of Modeling Soot Formation in Turbulent Diffusion Flames"}, 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} }