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

2019 ◽  
Vol 20 (10) ◽  
pp. 1089-1100 ◽  
Author(s):  
Tim Franken ◽  
Corinna Netzer ◽  
Fabian Mauss ◽  
Michal Pasternak ◽  
Lars Seidel ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Injection ◽  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Multi Objective Optimization ◽  
Reactor Model ◽  
Engine Efficiency ◽  
Fast Running ◽  
Tabulated Chemistry ◽  
Simulation Based

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.

scholarly journals Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models

2020 ◽  
Vol 10 (24) ◽  
pp. 8979
Author(s):  
Andrea Matrisciano ◽  
Tim Franken ◽  
Laura Catalina Gonzales Mestre ◽  
Anders Borg ◽  
Fabian Mauss
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Reactor Model ◽  
Progress Variable ◽  
Tabulated Chemistry ◽  
Computer Aided ◽  
Reactor Models

The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO2, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns.

Multi-objective optimization of operating parameters for hydrogen-fueled spark-ignition engines

2016 ◽  
Vol 41 (40) ◽  
pp. 18291-18299 ◽  
Author(s):  
Abdulhakim I. Jabbr ◽  
Warren S. Vaz ◽  
Hassan A. Khairallah ◽  
Umit O. Koylu
Keyword(s):  
Spark Ignition ◽  
Operating Parameters ◽  
Spark Ignition Engines ◽  
Multi Objective Optimization ◽  
Multi Objective

Effect of relative humidity on water injection technique in downsized spark ignition engines

2020 ◽  
pp. 146808742094085
Author(s):  
Jayesh Khatri ◽  
Nikhil Sharma ◽  
Petter Dahlander ◽  
Lucien Koopmans
Keyword(s):  
Relative Humidity ◽  
Water Injection ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Injection Technique ◽  
Injection Timing ◽  
Particulate Emissions ◽  
Spark Ignition Engines ◽  
General Applicability ◽  
Major Barrier

Combustion knock is a major barrier to achieving high thermal efficiency in spark ignition engines. Water injection was recently identified as a potential way of overcoming this barrier. To evaluate its general applicability, experiments were performed on a downsized three-cylinder spark ignition engine, varying the humidity of the intake air, the water injection timing, and the engine speed. The minimum quantity of injected water required to maintain a given load (and thus level of engine performance) was determined under each set of tested conditions. The knock-suppressing effects of water injection were found to be related to changes in the fuel–air mixture’s specific heat ratio (kappa) rather than evaporative cooling, and to therefore depend on the total quantity of water in the cylinder rather than the relative humidity per se. The total quantity of water in the cylinder was also shown to be a key determinant of advancement in combustion phasing and particulate emissions under various conditions.

scholarly journals Top Ten Blendstocks Derived From Biomass For Turbocharged Spark Ignition Engines: Bio-blendstocks With Potential for Highest Engine Efficiency

2019 ◽  
Author(s):  
Daniel J. Gaspar ◽  
Brian H. West ◽  
Danial Ruddy ◽  
Trenton J. Wilke ◽  
Evgueni Polikarpov ◽  
...  
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Engine Efficiency

Water Injection to Improve Direct Injection Spark Ignition Engine Efficiency

2019 ◽  
Author(s):  
Matthieu Cordier ◽  
Matthieu Lecompte ◽  
Louis-Marie Malbec ◽  
Benjamin Reveille ◽  
Cedric Servant ◽  
...  
Keyword(s):  
Direct Injection ◽  
Water Injection ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Engine Efficiency ◽  
Direct Injection Spark Ignition

Optimization of Design and Operational Parameters Using Genetic Algorithm for a Spark Ignition Engine With CNG/H2 Mixtures

2005 ◽  
Author(s):  
G. Anand ◽  
R. Balamurugan
Keyword(s):  
Genetic Algorithm ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Nox Emissions ◽  
Engine Fuel ◽  
Operational Parameters ◽  
Si Engine ◽  
Engine Efficiency ◽  
Wide Range

The present contribution describes the potential of using gaseous fuels like Hythane (CNG/H2 mixtures) as a spark ignition (SI) engine fuel. Genetic Algorithm (GA) is used to optimize the design and operational parameters of a CNG/H2 fueled spark ignition engine for maximizing the engine efficiency subjected to NOx emission constraint. This research deals with quasi-dimensional, two-zone thermodynamic simulation of four-stroke SI engine fueled with CNG/H2 blended fuel for the prediction of the combustion and emission characteristics. The validity of the model has been carried out by comparing the computed results with experimental data obtained under same engine setup and operating conditions. A wide range of engine parameters were optimized using a simple GA regarding both engine efficiency and NOx emissions. The five parameters chosen were compression ratio, engine speed, equivalence ratio, H2 fraction in the fuel, and spark plug position in cylinder head. The amount of NOx emissions was being kept under the constrained value of 750 ppm (< 5 g/kWh), which is less than permissible limit for heavy-duty engines.

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