Past Funded Projects
The four-year Integrated Project "New Integrated Combustion System for Future Passenger Car Engines (NICE)" is proposed by the European automotive industry, at its highest responsibility level. The main objective of NICE is to develop a new integrated combustion system that, independently on the type of fuel (i.e. fuel neutral), is able to achieve the today highest fuel conversion efficiency of the DI diesel engine (43%), while complying with a zero-impact emission level.
The main objective of the project is to develop a new integrated combustion system that, independently of the type of fuel (i.e. fuel neutral), is able to achieve today's highest fuel conversion efficiency of the DI diesel engine (43%), while complying with a zero-impact emission level. One aspect of the contributions to this project by the Lund Kinetics group is the development of complex hydrocarbon combustion mechanisms. Several of these mechanisms will be automatically generated. My fundamental contribution is the software behind the generation (REACTION system) and the co-authoring of the n-decane mechanism. The same procedures and reaction rules used in the generation of the n-decane mechanism will also be used to generate larger hydrocarbons.
Recent Relevant Publications:
Detailed Mechanism Generation 1: Generatlized Reactive Properties as Reaction Class Substructures E. S. Blurock,Journal of Chemical Information and Computer Sciences, 44 (4) 1336-1347 (2004).
Detailed Mechanism Generation 2: Aldehydes, Ketones and Olefins, E. S. Blurock, Journal of Chemical Information and Computer Sciences, 44 (4) 1348 -1357(2004).
Large Eddy Simulation Techniques for Simulating to Control by Design Cyclic Variability in Otto Cycle Engines
The objective of LESSCO2 is to give the car industry the ability to predict and thus control by design cyclic variability in internal combustion Otto cycle engines. These phenomena are detrimental to engine efficiency and pollutant emissions. Controlling them by design allows to take full profit of the benefits in terms of CO2, efficiency and pollutants that can be expected from novel concepts like CAI. As today's 3D CFD tools are not able to address these phenomena, LESSCO2 proposes a highly innovative work on the Large Eddy Simulation technique. Novel LES models and numerical techniques will be developed, including work on using realistic auto-ignition chemistry for gasoline and natural gas, tested and transferred to the projects industrial partners. They will validate the obtained industrial LES engine design tools by performing multi-cycle simulations aiming at predicting cyclic variability and estimate their impact on future designs.
Contribution of the Lund Kinetics group in this project is the integration of complex chemistry into CFD calculations through use of dynamic tabulation methods based on PRISM (Piecewise Implementation of Solution Mapping). This avoids the problems related to solving for chemical source terms and allows the speed up of CFD calculations. The fundamentatl principle is to use second degree polynomials (PRISM) and gradient (ISAT) approximations within local 'hypercubes' (small regions in n-dimensional chemical space) to replace the full mechanistic calculation (involving numerically solving a system of differential equations). The method formulates these approximations on an 'as-needed' basis. The deliverable is a FORTRAN software for various hydrocarbon mechanisms. My contribution is the FORTRAN software base for the methodology and the extension of the PRISM to ACCUM-PRISM (the points needed to calculate the 2nd degree polynomial are accumulated within each hypercube as they are needed, as opposed making addition calls to the mechanistic solver to gather the necessary points as is done in the original formation). The use of this accumulation strategy also allowed the combination with the ISAT method (not possible in the original formation).
Recent Relevant Publications:
Haus der Teknik. E.V. International Congress Engine Combustion Processes: Speed-up of a Stochastic Reactor Model for a Hydrogen Fueled SI Engine by PRISM, E. S. Blurock, H. Lehtiniemi, F. Mauss, 2005
Improving Engine Performance and Efficiency by Minimisation of Knock Probability
To date, the optimisation of existing engines and the development of new engine concepts like DGI, down-sizing, turbo charging or CAI is still a challenge for engine developers considering high loads over the entire speed range. This is mostly due to the knocking phenomenon, which is still not sufficiently understood. Engine knock is the limiting factor for increasing engine efficiency and is also responsible for increased emissions at high loads over the entire speed range. Describing and even predicting knocking is a tedious task, requiring detailed knowledge of all the processes occurring in the combustion chamber. The objective of the proposed project is to provide a better understanding of how engine knock is initiated and influenced by fuel and engine parameters. The results of the project complement current databases, instrumentation and simulation tools and empower development engineers to further improve engine performance and efficiency, to reduce engine pollutant and GHG emissions simultaneously and to reduce time and costs in the development process.
The contribution of the Lund Kinetics group in this project is the integeration of complex reduced (skeletal and QSSA) chemistry, coupled with t-PDF (probability density function) for use in CFD codes. The auto-ignition process wil be analysed and subdivided into phases using machine learning tools in a preprocessing stage. Each chemical phase will be reduced using techniques developed at lund. These POSM (phase optimized skeletal mechanisms) are then combined in a single module representing the entire chemistry of the system. My contribution is the fundamental development of the POSM method and the use of machine learning tools (and the associated software) and the integration of the technique (semi-automated) into FORTRAN modules representing the chemistry source terms. The use of the POSM method is transparent to the interfaced codes.
Recent Relevant Publications:
Characterizing complex reaction mechanisms using machine learning clustering techniques, E.S. Blurock, International Journal of Chemical Kinetics, 36(2), 107-118, (2004)
Automatic Characterization of Ignition Processes with Machine Learning Clustering Techniques, International Journal of Chemical Kinetics, Edward S. Blurock (accepted for publication).
18th International Colloquium on the Dynamics of Explosions and Reactive Systems, Steady State Reduced Mechanisms based on Domain Splitting, Edward S. Blurock, Terese Lovaas, Fabian Mauss