Refine
Document Type
- Doctoral thesis (3)
- Scientific article (1)
Has Fulltext
- yes (4)
Is part of the Bibliography
- no (4)
Language
- English (4)
Keywords
- Stochastic reactor model (3)
- Detailed chemistry (2)
- Ottomotor (2)
- Stochastisches Modell (2)
- Stochastisches Reaktormodell (2)
- Abgasemission (1)
- Benzin (1)
- Combustion modelling (1)
- Computational fluid dynamics (1)
- Detaillierte Chemie (1)
Institute
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.
