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Downsizing of modern gasoline engines with direct injection is a key concept for achieving future CO22 emission targets. However, high power densities and optimum efficiency are limited by an uncontrolled autoignition of the unburned air-fuel mixture, the so-called spark knock phenomena. By a combination of three-dimensional Computational Fluid Dynamics (3D-CFD) and experiments incorporating optical diagnostics, this work presents an integral approach for predicting combustion and autoignition in Spark Ignition (SI) engines. The turbulent premixed combustion and flame front propagation in 3D-CFD is modeled with the G-equation combustion model, i.e. a laminar flamelet approach, in combination with the level set method. Autoignition in the unburned gas zone is modeled with the Shell model based on reduced chemical reactions using optimized reaction rate coefficients for different octane numbers (ON) as well as engine relevant pressures, temperatures and EGR rates. The basic functionality and sensitivities of improved sub-models, e.g. laminar flame speed, are proven in simplified test cases followed by adequate engine test cases. It is shown that the G-equation combustion model performs well even on unstructured grids with polyhedral cells and coarse grid resolution. The validation of the knock model with respect to temporal and spatial knock onset is done with fiber optical spark plug measurements and statistical evaluation of individual knocking cycles with a frequency based pressure analysis. The results show a good correlation with the Shell autoignition relevant species in the simulation. The combined model approach with G-equation and Shell autoignition in an active formulation enables a realistic representation of thin flame fronts and hence the thermodynamic conditions prior to knocking by taking into account the ignition chemistry in unburned gas, temperature fluctuations and self-acceleration effects due to pre-reactions. By the modeling approach and simulation methodology presented in this work the overall predictive capability for the virtual development of future knockproof SI engines is improved.
Sebastian Hann describes the development of a quasi-dimensional burn rate model that enables the prediction of a fuel variation, without the need for a recalibration of the model. The model is valid for spark-ignition combustion engines powered by conventional and carbon-neutral fuels. Its high predictive ability was achieved by modeling the fuel-dependent laminar flame speed based on reaction kinetics calculations. In addition, the author discards a fuel influence on flame wrinkling by performing an engine measurement data analysis. He investigates the fuel influence on engine knock and models it via ignition delay times obtained from reaction kinetics calculations.
The virtual development of future Spark Ignition (SI) engine combustion processes in three-dimensional Computational Fluid Dynamics (3D-CFD) demands for the integration of detailed chemistry, enabling - additionally to the 3D-CFD modelling of flow and mixture formation - the prediction of fuel-dependent SI engine combustion in all of its complexity. This work presents an approach, which constitutes a coupled solution for flame propagation, auto-ignition, and emission formation modelling incorporating detailed chemistry, while exhibiting low computational costs. For modelling the regular flame propagation, a laminar flamelet approach, the G-equation is used. Auto-ignition phenomena are addressed using an integrated flamelet approach, which bases on the tabulation of fuel-dependent reaction kinetics. By introducing a progress variable for the auto-ignition - the Ignition Progress Variable (IPV) - detailed chemistry is integrated in 3D-CFD. The modelling of emission formation bases on an interactively coupled flamelet approach, the Transient Interactive Flamelet (TIF) model. The functionality of the combined approach to model the variety of SI engine combustion phenomena is proved first in terms of fundamentals and standalone sub-model functionality studies by introducing a simplified test case, which represents an adiabatic pressure vessel without moving meshes. Following the basic functionality studies, the sub-model functionalities are investigated and validated in adequate engine test cases. It is shown, that the approach allows to detect locally occurring auto-ignition phenomena in the combustion chamber, and to model their interaction with regular flame propagation. Moreover, the approach enables the prediction of emission formation on cell level.
This work develops a computational framework for modeling turbulent combustion in multi-feed systems that can be applied to internal combustion engines with multiple injections. In the first part of this work, the laminar flamelet equations are extended to two dimensions to enable the representation of a three-feed system that can be characterized by two mixture fractions. A coupling between the resulting equations and the turbulent flow field that enables the use of this method in unsteady simulations is then introduced. Models are developed to describe the scalar dissipation rates of each mixture fraction, which are the parameters that determine the influence of turbulent mixing on the flame structure. Furthermore, a new understanding of the function of the joint dissipation rate of both mixture fractions is discussed. Next, the extended flamelet equations are validated using Direct Numerical Simulations (DNS) of multi-stream ignition that employ detailed finite-rate chemistry. The results demonstrate that the ignition of the overall mixture is influenced by heat and mass transfer between the fuel streams and that this interaction is manifested as a front propagation in two-dimensional mixture fraction space. The flamelet model is shown to capture this behavior well and is therefore able to accurately describe the ignition process of each mixture. To provide closure between the flamelet chemistry and the turbulent flow field, information about the joint statistics of the two mixture fractions is required. An investigation of the joint probability density function (PDF) was carried out using DNS of two scalars mixing in stationary isotropic turbulence. It was found that available models for the joint PDF lack the ability to conserve all second-order moments necessary for an adequate description of the mixing field. A new five parameter bivariate beta distribution was therefore developed and shown to describe the joint PDF more accurately throughout the entire mixing time and for a wide range of initial conditions. Finally, the proposed model framework is applied in the simulation of a split-injection diesel engine and compared with experimental results. A range of operating points and different injection strategies are investigated. Comparisons with the experimental pressure traces show that the model is able to predict the ignition delay of each injection and the overall combustion process with good accuracy. These results indicate that the model is applicable to the range of regimes found in diesel combustion.
Developing a profound understanding of the combustion characteristics of the cold-start phase of a Direct Injection Spark Ignition (DISI) engine is critical to meeting the increasingly stringent emissions regulations. Computational Fluid Dynamics (CFD) modeling of gasoline DISI combustion under normal operating conditions has been discussed in detail using both the detailed chemistry approach and flamelet models (e.g., the G-Equation). However, there has been little discussion regarding the capability of the existing models to capture DISI combustion under cold-start conditions. Accurate predictions of cold-start behavior involves the efficient use of multiple models - spray modeling to capture the split injection strategies, models to capture the wall-film interactions, ignition modeling to capture the effects of retarded spark timings, combustion modeling to accurately capture the flame front propagation, and turbulence modeling to capture the effects of decaying turbulent kinetic energy. The retarded spark timing helps to generate high heat flux in the exhaust for a rapid catalyst light-off of the after-treatment system during cold-start. However, the adverse effect is a reduced turbulent flame speed due to decaying turbulent kinetic energy. Accordingly, developing an understanding of the turbulence-chemistry interactions is imperative for accurate modeling of combustion under cold-start conditions.This study introduces a modified version of the G-Equation combustion model called the GLR model (G-Equation for Lower Reynolds number regimes) that exhibits improved performance under cold-start conditions. The model attempts to estimate the turbulent flame speed based on the local conditions of fuel concentration and turbulence intensity. The local conditions and the associated turbulent-chemistry interactions are studied by tracking the flame front on the Borghi-Peters regime diagram. To accurately model the DISI combustion process, it is important to account for the effects of the spark energy discharge process. In this work, an ignition model is presented that is compatible with the G-Equation combustion model, and which accounts for the effects of plasma expansion and local mixture properties such as turbulence and the equivalence ratio on the early flame kernel growth. The model is referred to as the Plasma Velocity on G-Surface (PVG) model, and it uses the G-surface to capture the kernel growth. The model derives its theory from the DPIK model and applies its concepts onto an Eulerian framework, thereby removing the need for Lagrangian particles to track the kernel growth. Finally, a methodology of using machine learning (ML) techniques in combination with 3D CFD modeling to optimize the cold-start fast-idle phase of a DISI engine is presented. The optimization process implies the identification of the range of operating parameters, that will ensure the following criteria under cold-start conditions: (1) a fixed IMEP of 2 bar (BMEP of 0 bar), (2) a stoichiometric exhaust equivalence ratio (based on carbon-to-oxygen atoms) to ensure the efficient operation of the after-treatment system, (3) enough exhaust heat flux to ensure a rapid light-off of the after-treatment system, and (4) acceptable NOx and HC emissions. Gaussian Process Regression (GPR)-based ML models are employed to make predictions about DISI cold-start behavior with acceptable accuracy and a substantially reduced computational time.
This volume gathers the contributions of six world experts to a course on combustion modelling. Therefore, a pedagogical effort has been made in writing up these texts, which cover state of the art advances in most aspects of combustion science. The book is aimed at students, researches and engineers, as was the course.