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Advanced combustion concepts such as reactivity controlled compression ignition (RCCI) have been proven to be capable of fundamentally improve the conventional Diesel combustion by mitigating or avoiding the soot-NOx trade-off, while delivering comparable or better thermal efficiency. To further facilitate the development of the RCCI technology, a robust and possibly computationally efficient simulation framework is needed. While many successful studies have been published using 3D-CFD coupled with detailed combustion chemistry solvers, the maturity level of the 0D/1D based software solution offerings is relatively limited. The close interaction between physical and chemical processes challenges the development of predictive numerical tools, particularly when spatial information is not available. The present work discusses a novel stochastic reactor model (SRM) based modeling framework capable of predicting the combustion process and the emission formation in a heavy-duty engine running under RCCI combustion mode. The combination of physical turbulence models, detailed emission formation sub-models and stateof-the-art chemical kinetic mechanisms enables the model to be computationally inexpensive compared to the 3D-CFD approaches. A chemical kinetic mechanism composed of 248 species and 1428 reactions was used to describe the oxidation of gasoline and diesel using a primary reference fuel (PRF)mixture and n-heptane, respectively. The model is compared to operating conditions from a single-cylinder research engine featuring different loads, speeds, EGR and gasoline fuel fractions. The model was found to be capable of reproducing the combustion phasing as well as the emission trends measured on the test bench, at some extent. The proposed modeling approach represents a promising basis towards establishing a comprehensive modeling framework capable of simulating transient operation as well as fuel property sweeps with acceptable accuracy.
This work presents the assessment of direct water injection in spark-ignition engines using single cylinder experiments and tabulated chemistry-based simulations. In addition, direct water injection is compared with cooled low-pressure exhaust gas recirculation at full load operation. The analysis of the two knock suppressing and exhaust gas cooling methods is performed using the quasi-dimensional stochastic reactor model with a novel dual fuel tabulated chemistry model. To evaluate the characteristics of the autoignition in the end gas, the detonation diagram developed by Bradley and coworkers is applied. The single cylinder experiments with direct water injection outline the decreasing carbon monoxide emissions with increasing water content, while the nitrogen oxide emissions indicate only a minor decrease. The simulation results show that the engine can be operated at l = 1 at full load using water–fuel ratios of up to 60% or cooled
low-pressure exhaust gas recirculation rates of up to 30%. Both technologies enable the reduction of the knock probability and the decrease in the catalyst inlet temperature to protect the aftertreatment system components. The strongest exhaust temperature reduction is found with cooled low-pressure exhaust gas recirculation. With stoichiometric air–fuel ratio and water injection, the indicated efficiency is improved to 40% and the carbon monoxide emissions are reduced. The nitrogen oxide concentrations are increased compared to the fuel-rich base operating conditions and the nitrogen oxide emissions decrease with higher water content. With stoichiometric air–fuel ratio and exhaust gas recirculation, the indicated efficiency is improved to 43% and the carbon monoxide emissions are decreased. Increasing the exhaust gas recirculation rate to 30% drops the nitrogen oxide emissions below the concentrations of the fuel-rich base operating conditions.
Water injection can be applied to spark ignited gasoline engines to increase the Knock Limit Spark Advance and improve the thermal efficiency. The Knock Limit Spark Advance potential of 6 °CA to 11 °CA is shown by many research groups for EN228 gasoline fuel using experimental and simulation methods. The influence of water is multi-layered since it reduces the in-cylinder temperature by vaporization and higher heat capacity of the fresh gas, it changes the chemical equilibrium in the end gas and increases the ignition delay and decreases the laminar flame speed. The aim of this work is to extend the analysis of water addition to different octane ratings. The simulation method used for the analysis consists of a detailed reaction scheme for gasoline fuels, the Quasi-Dimensional Stochastic Reactor Model and the Detonation Diagram. The detailed reaction scheme is used to create the dual fuel laminar flame speed and combustion chemistry look-up tables. The Detonation Diagram is used as a novel approach in the Quasi-Dimensional Stochastic Reactor Model to evaluate the auto-ignition characteristic in the end gas and determine if it is a harmless deflagration or developing detonation. First, the Quasi-Dimensional Stochastic Reactor Model is trained for three engine operating points and a RON95 E10 fuel. Its performance is evaluated based on experimental results of a single cylinder research engine. Subsequently, different spark timings and water-fuel ratios are investigated for different Primary Reference Fuels. The results outline that water addition can effectively reduce the strength of auto-ignition in the end gas for different Primary Reference Fuels. Thereby, it can be stated that the reduction of the auto-ignition strength through water addition by 50 – 80 % water-fuel ratio for high octane number fuels corresponds to the spark timing delay of 6 °CA or an increase of research octane number by 10 points.
Highly fuel-efficient Diesel engines, combined with effective exhaust aftertreatment systems, enable an economic and low-emission operation of heavy-duty vehicles. The challenge of its development arises from the present engine complexity, which is expected to increase even more in the future. The approved method of test bench measurements is stretched to its limits, because of the high demand for large parameter variations. The introduction of a physics-based quasi-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of these Diesel engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during …
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.
A Computationally Efficient Combustion Progress Variable (CPV) Approach for Engine Applications
(2018)
In this work, we apply a sequence of concepts for mechanism reduction on one reaction mechanism including novel quality control. We introduce a moment-based accuracy rating method for species profiles. The concept is used for a necessity-based mechanism reduction utilizing 0D reactors. Thereafter a stochastic reactor model for internal combustion engines is applied to control the quality of the reduced reaction mechanism during the expansion phase of the engine. This phase is sensitive on engine out emissions, and is often not considered in mechanism reduction work. The proposed process allows to compile highly reduced reaction schemes for computational fluid dynamics application for internal combustion engine simulations. It is demonstrated that the resulting reduced mechanisms predict combustion and emission formation in engines with accuracies comparable to the original detailed scheme.
Abstract: Large two-stroke marine Diesel engines have special injector geometries, which differ substantially from the configurations used in most other Diesel engine applications.
One of the major differences is that injector orifices are distributed in a highly non-symmetric fashion affecting the spray characteristics. Earlier investigations demonstrated the dependency of the spray morphology on the location of the spray orifice and therefore on the resulting flow conditions at the nozzle tip. Thus, spray structure is directly influenced by the flow formation within the orifice. Following recent Large Eddy Simulation resolved spray primary breakup studies, the present paper focuses on spray secondary breakup odelling of asymmetric spray structures in Euler-Lagrangian framework based on previously obtained droplet distributions of primary breakup. Firstly, the derived droplet distributions were …
Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model
(2017)
In this work we apply a sequence of concepts for mechanism reduction on one reaction mechanism including novel quality control. We introduce a moment based accuracy rating method for species profiles. The concept is used for a necessity based mechanism reduction utilizing 0D reactors. Thereafter a stochastic reactor model (SRM) for internal combustion engines is applied to control the quality of the reduced reaction mechanism during the expansion phase of the engine. This phase is sensitive on engine out emissions, and is often not considered in mechanism reduction work. The proposed process allows to compile highly reduced reaction schemes for CFD application for internal combustion engine simulations. It is demonstrated that the resulting reduced mechanisms predict combustion and emission formation in engines with accuracies comparable to the original detailed scheme.
In this work a soot source term tabulation strategy for soot predictions under Diesel engine conditions within the zero-dimensional Direct Injection Stochastic Reactor Model (DI-SRM) framework is presented. The DI-SRM accounts for detailed chemistry, in-homogeneities in the combustion chamber and turbulence-chemistry interactions. The existing implementation [1] was extended with a framework facilitating the use of tabulated soot source terms. The implementation allows now for using soot source terms provided by an online chemistry calculation, and for the use of a pre-calculated flamelet soot source term library. Diesel engine calculations were performed using the same detailed kinetic soot model in both configurations. The chemical mechanism for n-heptane used in this work is taken from Zeuch et al. [2] and consists of 121 species and 973 reactions including PAH and thermal NO chemistry. The engine case presented in [1] is used also for this work. The case is a single-injection part-load passenger car Diesel engine with 27 % EGR fueled with regular Diesel fuel. The two different approaches are analyzed and a detailed comparison is presented for the different soot processes globally and in the mixture fraction space. The contribution of the work presented in this paper is that a method which allows for a direct comparison of soot source terms - calculated online or retrieved from a flamelet table - without any change in the simulation setup has been developed within the SRM framework. It is a unique tool for model development. Our analysis supports our previous conclusion [1] that flamelet soot source terms libraries can be used for multi-dimensional modeling of soot formation in Diesel engines.
A chemical species lumping approach for reduction
of large hydrocarbons and oxygenated fuels is presented. The methodology is based on an a priori analysis of the Gibbs free energy of the isomer species which is then used as main criteria for the evaluation of lumped group. Isomers with similar Gibbs free energy are lumped assuming they present equal concentrations when applied to standard reactor conditions. Unlike several lumping approaches found in literature, no calculation results from the primary mechanism have been employed prior to the application of our chemical lumping strategy.
In this work we apply various concepts of mechanism reduction with a PDF based method for species profile
conservation. The reduction process is kept time efficient by only using 0D and 1D reactors. To account for the expansion phase in internal combustion engines a stochastic engine tool is used to validate the reduction steps.
The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low. This work presents a novel methodology to couple the stochastic reactor model and the Non-dominated Sorting Genetic Algorithm. First, the stochastic reactor model is calibrated for 10 low, medium and high load operating points at various engine speeds. Second, each operating point is optimized to find the lowest fuel consumption and specific NOx emissions. The optimization input parameters are the temperature at intake valve closure, the compression ratio, the start of injection, the injection pressure and exhaust gas recirculation rate. Additionally, it is ensured that the maximum peak cylinder pressure and turbine inlet temperature are not exceeded. This enables a safe operation of the engine and exhaust aftertreatment system under the optimized conditions. Subsequently, a reliability analysis is performed to estimate the effect of off-nominal conditions on the objectives and constraints. The novel multi-objective optimization methodology has proven to deliver reasonable results. The zero-dimensional stochastic reactor model with tabulated chemistry is a fast running physics-based model that allow to run large optimization problems in a short amount of time. The combination with the reliability analysis also strengthens the confidence in the simulation-based optimized engine operation parameters.
The use of complex reaction schemes is accompanied by high computational cost in 3D CFD simulations but is particularly important to predict pollutant emissions in internal combustion engine simulations. One solution to tackle this problem is to solve the chemistry prior the CFD run and store the chemistry information in look-up tables. The approach presented combines pre-tabulated progress variable-based source terms for auto-ignition as well as soot and NOx source terms for emission predictions. The method is coupled to the 3D CFD code CONVERGE v2.4 via user-coding and tested over various speed and load passenger-car Diesel engine conditions. This work includes the comparison between the combustion progress variable (CPV) model and the online chemistry solver in CONVERGE 2.4. Both models are compared by means of combustion and emission parameters. A detailed n-decane/α-methyl-naphthalene mechanism, comprising 189 species, is used for both online and tabulated chemistry simulations. The two chemistry solvers show very good agreement between each other and equally predict trends derived experimentally by means of engine performance parameters as well as soot and NOx engine-out emissions. The CPV model shows a factor 8 speed-up in run-time compared to the online chemistry solver without compromising the accuracy of the solution.
State of the art spark ignited gasoline engines achieve thermal efficiencies above 46 % e.g. due to friction optimized crank trains, high in-cylinder tumble flow and direct fuel injection. Further improvements of thermal efficiency are expected from lean combustion, higher compression ratio and new knock-resistant fuel blends. One of the limitations to these improvements are set by the autoignition in the end gas, which can develop to knocking combustion and severely damage the internal combustion engine. The auto-ignition is enhanced by high cylinder gas temperatures and reactive species in the end gas composition. Quasi-dimensional Stochastic Reactor Model simulations with detailed chemistry allow to consider the thermochemistry properties of surrogates and complex end gas compositions. Based on the detailed reaction scheme and surrogate model, an innovative tabulated chemistry approach is utilized to generate dual-fuel laminar flame speed and combustion chemistry look-up tables. This reduces the simulation duration to seconds per cycle, while the loss in accuracy compared to solving the chemistry “online” is marginal. The auto-ignition events predicted by the tabulated chemistry simulation are evaluated using the Detonation Diagram developed by Bradley and co-workers. This advanced methodology for quasi-dimensional models evaluates the resonance between the shock wave and reactionfront velocity from auto-ignition in the end gas and determines if it is a harmful developing detonation or normal deflagration. The aim of this work is to evaluate the auto-ignition characteristics of different fuel blends. The Stochastic Reactor Model with tabulated chemistry is applied to perform a numerical analysis of the autoignition of the fuel blends and operating conditions. Experimental measurements of a single cylinder research engine operated with RON95 E10 fuel are used to train and validate the simulation model. The RON95 E10 fuel is blended with Methanol, Hydrogen and Toluene. The knock tendency based on the evaluation of auto-ignition events of the different fuel blends are analysed for three operating points at 1500 rpm 15 bar IMEP, 2000 rpm 20 bar IMEP and 2500 rpm 15 bar IMEP with advanced spark timings.
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.