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This thesis summarizes the author’s developments of combustion models and multi-objective optimization methods for gasoline and diesel engines. The combustion models belong to the family of zero-dimensional stochastic reactor models introduced in the 1990s to improve the prediction of emissions with detailed chemistry in partially stirred reactors.
The first part introduces the fundamentals of the physical and chemical models describing the combustion process. As a novelty, k−ε turbulence models were implemented in the stochastic reactor model to predict the turbulent time and length scales in gasoline and diesel engines. This development allowed an improvement of the models for convective heat transfer, fuel evaporation, gas exchange across the valves, turbulent flame propagation and crevice flow, which depend on the turbulent time and length scales.
In the second part, the multi-objective optimization platform for automatic training of the stochastic reactor model is presented. The optimization method considers multiple operating points to find a set of model parameters that predict performance and emissions over the entire engine map. The Non-domination Sorting Genetic Algorithm II is combined with the stochastic reactor model and response surface models to find the best Pareto front. Multi-criteria decision making is used to select the best designs from the Pareto front.
Finally, the third part of this thesis deals with the validation of the stochastic reactor model and the multi-objective optimization platform. For this purpose, experiments of two single-cylinder research engines with spark ignition, one passenger car engine with compression ignition and one heavy duty engine with compression ignition are used. For the spark ignition engines, a set of model parameters was found that predicts well the power and emissions over the whole engine map. The calculated turbulent kinetic energy, dissipation, and angular momentum follow the trends of the three-dimensional computational fluid dynamic simulations to a good approximation for various operating points. For the two compression ignition engines, the prediction of combustion progress and nitrogen oxide emissions are in good agreement with the experiments. Larger discrepancies were found for the prediction of carbon monoxide and unburned hydrocarbon. Optimization of the soot model parameters improves the prediction of soot mass for operating points throughout the engine map.
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.
Sophisticated engine knock modeling supports the optimization of the thermal efficiency of spark ignition engines. For this purpose the presented work introduces the resonance theory (Bradley and co-workers, 2002) for three-dimensional Reynolds-Averaged Navier-Stokes (RANS) and for the zero-dimensional Spark Ignition Stochastic Reactor Model (SI-SRM) simulations. Hereby, the auto-ignition in the unburnt gases is investigated directly instead of the resulting pressure fluctuations. Based on the detonation diagram auto-ignition events can be classified to be in acceptable deflagration regime or possibly turn to a harmful developing detonation.
Combustion is modeled using detailed chemistry and formulations for turbulent flame propagation. The use of detailed chemistry caters for the prediction of physical and chemical properties, such as the octane rating, C:H:O-ratio or dilution. For both models, the laminar flame speed is retrieved from surrogate specific look-up tables compiled using the reaction mechanism for Ethanol containing Toluene Reference Fuels by Seidel (2017). In the fresh gas zone, the scheme is used for auto-ignition prediction. For this purpose, the G-equation coupled with a Well-Stirred-Reactor model is applied in RANS. In analogy, in the SI-SRM the combustion is modeled using a two zone model with stochastic mixing between the particles.
RANS is used to develop the knock classification methodology and to analyze in detail location, size and shape of the auto-ignition kernels. RANS estimates the ensemble average of the process and therefore cannot reproduce a developing detonation. Hence, Large Eddy Simulation (LES) is used to verify the methodology. Studies using wide ranges of surrogates with different octane rating and cycle-to-cycle variations are carried out using the computationally efficient SI-SRM. Cyclic variations are predicted based on stochastic mixing, stochastic heat transfer to the wall, varying exhaust gas recirculation composition and imposed probability density functions for the inflammation time and the scaling of the mixing time retrieved from RANS.
The methodology is verified for spark timing and octane rating. It is shown that the surrogate formulation has an important impact on knock prediction.
RANS is suitable to predict the mean strength of auto-ignition in the unburnt gas if the thermodynamic and chemical state of the ignition kernel is analyzed instead of the pressure gradients. The probability of the transition to knocking combustion can be determined. Good agreement between RANS and SI-SRM are obtained. The combination of both tools gives insights of local effects using RANS and the distribution of auto-ignition in the whole pressure range of an operating point using SI-SRM with reasonable computationally cost for development purposes.