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Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry

  • 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, theWater 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.show moreshow less

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Metadaten
Author: Tim FrankenORCiD, Corinna NetzerORCiD, Fabian Mauss, Michal Pasternak, Lars Seidel, Anders Borg, Harry Lehtiniemi, Andrea Matrisciano, Andre Casal Kulzer
DOI:https://doi.org/10.1177/1468087419857602
ISSN:1468-0874
ISSN:2041-3149
Publisher:SAGE Publications
Place of publication:London, England
Document Type:Scientific article
Language:English
Date of first Publication:2019/12/01
Release Date:2022/11/30
Tag:Detailed chemistry; Genetic optimization; Spark-ignition engine; Stochastic reactor model; Water injection
GND Keyword:Ottomotor; Wassereinspritzung; Prozessoptimierung; Stochastisches Modell
Source:Erstveröffentlichung: Tim Franken, Corinna Netzer, Fabian Mauss, Michal Pasternak, Lars Seidel, Anders Borg, Harry Lehtiniemi, Andrea Matrisciano, Andre Casal Kulzer. Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry. International Journal of Engine Research, 2019, vol. 20(10) 1089-1100. DOI 10.1177/1468087419857602
Institutes:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Thermodynamik / Thermische Verfahrenstechnik
Licence (German):Keine Lizenz vergeben. Es gilt das deutsche Urheberrecht.
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