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Partially Premixed Combustion (PPC) engines have demonstrated a potential for high efficiency and low emissions operation. To be able to study the combustion in detail but also to perform parametric studies on the potential of the PPC concept a one dimensional (1D) engine simulation tool was used with 1; a prescribed burn rate 2; predictive combustion tool with reduced chemical model and 3; predictive combustion tool with detailed chemical models. Results indicate that fast executing reduced chemistry work reasonably well in predicting PPC performance and that n-decane is possibly a suitable diesel substitute in PPC modeling while n-heptane is not.

In order to further improve the energy conversion efficiency in reciprocating engines, detailed knowledge about the involved processes is required. One major loss source in internal combustion engines is heat loss through the cylinder walls. In order to increase the understanding of heat transfer processes and to validate and generate new heat transfer correlation models it is desirable, or even necessary, to have crank-angle resolved data on in-cylinder wall temperature. Laser-Induced Phosphorescence has proved to be a useful tool for surface thermometry also in such harsh environments as running engines. However, the ceramic structure of most phosphor coatings might introduce an error, due to its thermal insulation properties, when being exposed to rapidly changing temperatures. In this article the measurement technique is evaluated concerning the impact from the thickness of the phosphorescent layer on the measured temperature.

The Technical University of Denmark, DTU, has designed, built and tested a gasifier [1, 8] that is fuelled with wood chips and achieves a 93% conversion efficiency from wood to producer gas. By combining the gasifier with an ICE and an electric generator a co-generative system can be realized that produces electricity and heat. The gasifier uses the waste heat from the engine for drying and pyrolysis of the wood chips while the gas produced is used to fuel the engine. To achieve high efficiency in converting biomass to electricity an engine is needed that is adapted to high efficiency operation using the specific producer gas from the DTU gasifier. So far the majority of gas engines have been designed and optimized for operation on natural gas. The presented work uses a modern and highly efficient truck sized natural gas engine to investigate efficiency, emissions and general performance while operating on producer gas compared to natural gas operation.

The Technical University of Denmark, DTU, has constructed, built and tested a gasifier [1, 11] that is fueled with wood chips and achieves a 93% conversion efficiency from wood to producer gas. By combining the gasifier with an internal combustion engine and a generator, a co-generative system can be realized that produces electricity and heat. The gasifier uses the waste heat from the engine for drying and pyrolysis of the wood chips while the produced gas is used to fuel the engine. To achieve high efficiency in converting biomass to electricity it necessitates an engine that is adapted to high efficiency operation using the specific producer gas from the DTU gasifier. So far the majority of gas engines of today are designed and optimized for SI-operation on natural gas.

The main losses in internal combustion engines are the heat losses to the cylinder walls and to the exhaust gases. Adiabatic, or low heat rejection engines, have received interest and been studied in several periods in history. Typically, however, these attempts have had to be abandoned when problems with lubrication and overheating components could not be solved satisfactorily. The latest years have seen the emerging of low temperature combustion in engines as well as computational powers that provide new options for highly efficient engines with low heat rejection. Stochastic Reactor Models (SRM) are highly efficient in modeling the kinetics decided low temperature combustion in HCCI and PPC engines. Containing the means to define the variations within the cylinder while employing detailed chemistry, micro mixing and heat transfer modeling, the interaction between heat transfer, exhaust gas energy and the combustion process can be studied with the SRM.

The formation of exothermic centers was modeled with a Stochastic Reactor Model (SRM) to investigate their impact on HCCI combustion. By varying the exhaust valve temperature, and thus assigning more realistic wall temperatures, the formation of exothermic centers and the ignition timing was shifted in time. To be able to study the exothermic centers, their formation and their distribution, Scatter plots, standard deviation plots and Probability Density Function (PDF) plots were constructed on the basis of the data the SRM calculations provided. The standard deviation for the particle temperatures was found to be an useful indicator of the degree of homogeneity within the combustion chamber, and thus of how efficient the combustion process was. It was observed that when the standard deviation of the temperature was higher, the emissions of CO and of hydrocarbons present at the end of the closed cycle were higher.

The ability to predict cyclic variations is certainly useful in studying engine operating regimes, especially under unstable operating conditions where one single cycle may differ from another substantially and a single simulation may give rather misleading results. PDF based models such as Stochastic Reactor Models (SRM) are able to model cyclic variations, but these may be overpredicted if discretization is too coarse. The range of cyclic variations and the dependence of the ability to correctly assess their mean values on the number of cycles simulated were investigated. In most cases, the average values were assessed correctly on the basis of as few as 10 cycles, but assessing the complete range of cyclic variations could require a greater number of cycles. In studying average values, variations due too coarse discretization being employed are smaller than variations originating from changes in physical parameters, such as heat transfer and mixing parameters.

In one-dimensional engine simulation programs the simulation of engine performance is mostly done by parameter fitting in order to match simulations with experimental data. The extensive fitting procedure is especially needed for emissions formation - CO, HC, NO, soot - simulations. An alternative to this approach is, to calculate the emissions based on detailed kinetic models. This however demands that the in-cylinder combustion-flow interaction can be modeled accurately, and that the CPU time needed for the model is still acceptable. PDF based stochastic reactor models offer one possible solution. They usually introduce only one (time dependent) parameter - the mixing time - to model the influence of flow on the chemistry. They offer the prediction of the heat release, together with all emission formation, if the optimum mixing time is given.

Concentrations of hydroxyl radicals and formaldehyde were calculated using homogeneous (HRM) and stochastic reactor models (SRM), and the result was compared to LIF-measurements from an optically accessed iso-octane / n-heptane fuelled homogeneous charge compression ignition (HCCI) engine. The comparison was at first conducted from averaged total concentrations / signal strengths over the entire combustion volume, which showed a good qualitative agreement between experiments and calculations. Time- and the calculation inlet temperature resolved concentrations of formaldehyde and hydroxyl radicals obtained through HRM are presented. Probability density plots (PDPs) through SRM calculations and LIF-measurements are presented and compared, showing a very good agreement considering their delicate and sensitive nature.

By dividing the combustion process into several phases with phase optimized skeletal mechanisms (POSM), gains in calculation speed were realized with virtually no loss in accuracy. A skeletal mechanism is a reduced mechanism where only the significant species, determined through a set of parameters (one for each species), remain with respect to a detailed mechanism. The parameter is based on a combination of sensitivity and flow analysis. Within the POSM method machine learning algorithms are used to automatically determine and recognize the major phases. Reduction is achieved by keeping only the significant species with respect to each phase. Each phase has a different mechanism, derived from the original and each is smaller than the original.