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A simulation method is presented for the analysis of combustion in spark ignition (SI) engines operated at elevated exhaust gas recirculation (EGR) level and employing multiple spark plug technology. The modeling is based on a zero-dimensional (0D) stochastic reactor model for SI engines (SI-SRM). The model is built on a probability density function (PDF) approach for turbulent reactive flows that enables for detailed chemistry consideration. Calculations were carried out for one, two, and three spark plugs. Capability of the SI-SRM to simulate engines with multiple spark plug (multiple ignitions) systems has been verified by comparison to the results from a three-dimensional (3D) computational fluid dynamics (CFD) model. Numerical simulations were carried for part load operating points with 12.5%, 20%, and 25% of EGR. At high load, the engine was operated at knock limit with 0%, and 20% of EGR and different inlet valve closure timing.

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.

This paper reports on a fast predictive combustion tool employing detailed chemistry. The model is a stochastic reactor based, discretised probability density function model, without spatial resolution. Employing detailed chemistry has the potential of predicting emissions, but generally results in very high CPU costs. Here it is shown that CPU times of a couple of minutes per cycle can be reached when applying detailed chemistry, and CPU times below 10 seconds per cycle can be reached when using reduced chemistry while still catching in-cylinder in-homogeneities. This makes the tool usable for efficient engine performance mapping and optimisation. To meet CPU time requirements, automatically load balancing parallelisation was included in the model. This allowed for an almost linear CPU speed-up with number of cores available.

This paper reports on a turbulent flame propagation model combined with a zero-dimensional two-zone stochastic reactor model (SRM) for efficient predictive SI in-cylinder combustion calculations. The SRM is a probability density function based model utilizing detailed chemistry, which allows for accurate knock prediction. The new model makes it possible to - in addition - study the effects of fuel chemistry on flame propagation, yielding a predictive tool for efficient SI in-cylinder calculations with all benefits of detailed kinetics. The turbulent flame propagation model is based on a recent analytically derived formula by Kolla et al. It was simplified to better suit SI engine modelling, while retaining the features allowing for general application. Parameters which could be assumed constant for a large spectrum of situations were replaced with a small number of user parameters, for which assumed default values were found to provide a good fit to a range of cases.

The solution mapping method Adaptive Polynomial Tabulation (APT) for complex chemistry is presented. The method has the potential of reducing the computational time required for stochastic reactor model simulations of the HCCI combustion process. In this method the solution of the initial value chemical rate equation system is approximated in real-time with zero, first and second order polynomial expressions. These polynomials are algebraic functions of a progress variable, pressure and total enthalpy. The chemical composition space is divided a priori into block-shaped regions (hypercubes) of the same size. Each hypercube may be divided in real-time into adaptive hypercubes of different sizes. During computations, initial conditions are stored in the adaptive hypercubes. Two concentric Ellipsoids of Accuracy (EOA) are drawn around each stored initial condition.

Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.

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.

Several models for ignition, combustion and emission formation under diesel engine conditions for multi-dimensional computational fluid dynamics have been proposed in the past. It has been recognized that the use of a reasonably detailed chemistry model improves the combustion and emission prediction especially under low temperature and high exhaust gas recirculation conditions. The coupling of the combustion chemistry and the turbulent flow can be achieved with different assumptions. In this paper we investigate a selection of n-heptane spray experiments published by the Engine Combustion Network (ECN spray H) with three different combustion models: well-stirred reactor model, transient interactive flamelet model and progress variable based conditional moment closure. All models cater for the use of detailed chemistry, while the turbulence-chemistry interaction modeling and the ability to consider local effects differ.

This paper explains the principle and advantages of the Ignition Progress Variable Library (IPV-Library) approach and its use in predicting engine related premixed, non-premixed and compression ignited combustion events. The implementation of IPV-Library model in the engine-focused CFD code VECTIS is described. To demonstrate the application of the model in predicting various types of combustion, computational results from a 2-stroke HCCI engine, a premixed spark ignition engine and an HSDI diesel engine are presented, together with some comparisons with engine test data.

Stringent exhaust emission limits and new vehicle test cycles require sophisticated operating strategies for future diesel engines. Therefore, a methodology for predictive combustion simulation, focused on multiple injection operating points is proposed in this paper. The model is designated for engine performance map simulations, to improve prediction of NOx, CO and HC emissions. The combustion process is calculated using a zero dimensional direct injection stochastic reactor model based on a probability density function approach. Further, the formation of exhaust emissions is described using a detailed reaction mechanism for n-heptane, which involves 56 Species and 206 reactions. The model includes the interaction between turbulence and chemistry effects by using a variable mixing time profile. Thus, one is able to capture the effects of mixture inhomogeneities on NOx, CO and HC emission formation.

A novel 0-D Probability Density Function (PDF) based approach for the modelling of Diesel combustion using tabulated chemistry is presented. The Direct Injection Stochastic Reactor Model (DI-SRM) by Pasternak et al. has been extended with a progress variable based framework allowing the use of a pre-calculated auto-ignition table. Auto-ignition is tabulated through adiabatic constant pressure reactor calculations. The tabulated chemistry based implementation has been assessed against the previously presented DI-SRM version by Pasternak et al. where chemical reactions are solved online. The chemical mechanism used in this work for both, online chemistry run and table generation, is an extended version of the scheme presented by Nawdial et al. The main fuel species are n-decane, α-methylnaphthalene and methyl-decanoate giving a size of 463 species and 7600 reactions.

An investigation on reducing the set of modeling parameters for engine cycle simulation is presented. The investigation considers a detailed kinetic model for combustion and emissions predictions coupled to a complete cycle simulation tool applied to a modern Diesel engine. The analysis is based on a previously developed method that combines a 1-D gas dynamics model with a stochastic reactor model for direct injection engines (SRM-DI). Initially, the global and instantaneous performance parameters of a Diesel engine were simulated at different operating conditions. The model was validated and the simulated results were compared to experimental data to assess the quality of the model. Afterwards, the influence of the chosen modeling parameters on engine performance, such as in-cylinder pressure, emissions and global performances, were analyzed. The mixing time proved to be the most important modeling parameter for the stochastic reactor model.

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.

A transient interactive flamelet model and a transient flamelet library based model are used to model a medium-duty diesel fueled engine operating in PCCI mode. The simulations are performed with and without the source term accounting for evaporation in the mixture fraction variance equation. Reasonable agreement is found with the experiments with both models. The effect of the evaporation source term in the mixture fraction variance equation is different for the different transient flamelet approaches. For the transient interactive flamelet model the ignition onset is delayed as a consequence of the higher mixture fraction variance, which leads to a higher scalar dissipation rate. The evaporation source term does not affect the global characteristics of the ignition event for the transient flamelet progress variable model, but locally the initial combustion is occurring differently.

We present a computational tool to develop an exhaust gas recirculation (EGR) - air-fuel ratio (AFR) operating range for homogeneous charge compression ignition (HCCI) engines. A single cylinder Ricardo E-6 engine running in HCCI mode, with external EGR is simulated using an improved probability density function (PDF) based engine cycle model. For a base case, the in-cylinder temperature and unburned hydrocarbon emissions predicted by the model show a satisfactory agreement with measurements [Oakley et al., SAE Paper 2001-01-3606]. Furthermore, the model is applied to develop the operating range for various combustion parameters, emissions and engine parameters with respect to the air-fuel ratio and the amount of EGR used. The model predictions agree reasonably well with the experimental results for various parameters over the entire EGR-AFR operating range thus proving the robustness of the PDF based model.

Meeting strict current and future emissions legislation necessitates development of computational tools capable of predicting the behaviour of combustion and emissions with an accuracy sufficient to make correct design decisions while keeping computational cost of the simulations amenable for large-scale design space exploration. While detailed kinetics modelling is increasingly seen as a necessity for accurate simulations, the computational cost can be often prohibitive, prompting interest in simplified approaches allowing fast simulation of reduced mechanisms at coarse grid resolutions appropriate for internal combustion engine simulations in design context. In this study we present a simplified Well-stirred Reactor (WSR) implementation coupled with 3D CFD Ricardo VECTIS solver.

Spray modelling plays a key role in engine simulations to understand fuel propagation and mixing, combustion, pollutant formation and energy efficiency. The grid dependency, need of calibration of several spray parameters, complexity associated with validation and high computational demand associated with Spray modelling are addressed with 1-dimentional SprayLet model. This work focuses on enhancing the SprayLet model approach with a dual emphasis on computational efficiency and grid independence for advanced engine simulations. Key spray characteristics, such as vapor and liquid penetration lengths, have been systematically evaluated as they play pivotal roles in understanding fuel evaporation, spray-wall interactions, and mixture formation within engines.

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.

The relatively new combustion concept known as partially premixed combustion (PPC) has high efficiency and low emissions. However, there are still challenges when it comes to fully understanding and implementing PPC. Thus a predictive combustion tool was used to gain further insight into the combustion process in late cycle mixing. The modeling tool is a stochastic reactor model (SRM) based on probability density functions (PDF). The model requires less computational time than a similar study using computational fluid dynamics (CFD). A novel approach with a two-zone SRM was used to capture the behavior of the partially premixed or stratified zones prior to ignition. This study focuses on PPC mixing conditions and the use of an efficient analysis approach.

A model based on an ionization equilibrium analysis, that can relate the ion current to the state of the gas inside the combustion volume, has been presented earlier. This paper introduces several additional models, that together with the previous model have the purpose of improving the pressure predictions. One of the models is a chemistry model that enables us to realistically consider the current contribution from the most relevant species. A second model can predict the crank angle of the peak pressure and thereby substantially increase the accuracy of the pressure predictions. Several other additions and improvements have been introduced, including support for part load engine conditions.