@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} }