Yu Shi

Acceleration of the chemistry solver for modeling DI engine combustion using dynamic adaptive chemistry (DAC) schemes

Acceleration of the chemistry solver for engine combustion is of much interest due to the fact that in practical engine simulations extensive computational time is spent solving the fuel oxidation and emission formation chemistry. A dynamic adaptive chemistry (DAC) scheme based on a directed relation graph error propagation (DRGEP) method has been applied to study homogeneous charge compression ignition (HCCI) engine combustion with detailed chemistry (over 500 species) previously using an R-value-based breadth-first search (RBFS) algorithm, which significantly reduced computational times (by as much as 30-fold). The present paper extends the use of this on-the-fly kinetic mechanism reduction scheme to model combustion in direct-injection (DI) engines. It was found that the DAC scheme becomes less efficient when applied to DI engine simulations using a kinetic mechanism of relatively small size and the accuracy of the original DAC scheme decreases for conventional non-premixed combustion engine. The present study also focuses on determination of search-initiating species, involvement of the NOx chemistry, selection of a proper error tolerance, as well as treatment of the interaction of chemical heat release and the fuel spray. Both the DAC schemes were integrated into the ERC KIVA-3v2 code, and simulations were conducted to compare the two schemes. In general, the present DAC scheme has better efficiency and similar accuracy compared to the previous DAC scheme. The efficiency depends on the size of the chemical kinetics mechanism used and the engine operating conditions. For cases using a small n-heptane kinetic mechanism of 34 species, 30% of the computational time is saved, and 50% for a larger n-heptane kinetic mechanism of 61 species. The paper also demonstrates that by combining the present DAC scheme with an adaptive multi-grid chemistry (AMC) solver, it is feasible to simulate a direct-injection engine using a detailed n-heptane mechanism with 543 species with practical computer time.

Modelling Diesel Combustion

Contents Preface Phenomenology of Diesel Combustion and Modeling 1 Introduction Role of Internal Combustion Engines Developments in DI Diesel engines Modelling of combustion in DI diesel engines 2 Phenomenology of diesel Combustion and modelling Combustion Model Emission models Theme of the book 3 Experiments Studies in a bomb Real engine studies 4 Turbulent Structure of the Diesel Spray Vaporising spray Combusting sprays Summary of the model for vapourising and combusting sprays Modern view of the vaporising and burning spray 5 Ignition Delay in a Diesel Engine Definition and Measurement of Ignition Delay Classical model for Ignition Delay and its extension to other fuels Phenomenological model of Ignition delay 6 Heat Transfer 7 Heat Release in Indirect Injection engines Description of the Phenomenological model Experimental technique Results and discussions 8 Mixing correlations for smoke and fuel consumption of Direct Injection engines Characteristic parameter for air fuel mixing in a cross flow Validation of the mixing parameter Conclusion 9 Heat Release in Direct Injection Engines Heat Release Rate in Diesel Engines Model for Mixing Controlled Combustion Input rate and dissipation rate of turbulent kinetic energy of fuel spray Modelling three Regimes of heat release rate Steps to calculate Heat Release Rate using the new model Experimental Validation Heat Release Rate from the Experiments Estimation of heat transfer across the walls Results 10 Hydrocarbons from D I Diesel Engines HC model Predicting HC in the exhaust Discussions 11 Hydrocarbon Emissions from Spark Ignition Engines Description of the Engine Mode Comparison of the model prediction with engine experiments Conclusions 12 Smoke from DI Diesel engines Phenomenon of soot formation Application to engine conditions 13 Oxides of Nitrogen from Direct Injection Diesel Engines Exhaust gas recirculation (EGR) Phenomenology of Oxides of Nitrogen 14 Particulate Matter from Direct Injection Diesel engines Phenomenology of Particulate Matter (PM) Validation of Correlation 15 Multi-dimensional modelling of diesel combustion: Review Basic approach Turbulence modelling Spray and evaporation modelling Combustion modelling Pollutant emissions modelling Heat transfer modelling Efficient multi-dimensional simulation of diesel engine combustion with detailed chemistry CFD codes for engine simulation Future and challenge 16 Multi-dimensional modelling of diesel combustion: Applications Case studies Appendices I Estimation of products of combustion from the interferogram II Estimation of concentration of fuel vapour in the vapourising and combusting spray from the interferogram III Estimation of Mass and Heat transfer functions IV Vapour pressure of diesel and fuels A & B and B* V Calculation of tangential velocity of air in the piston cavity from the inlet swirl number VI Momentum of useful air of the three different combustion cavities described in Kuo et al (1988) VII Momentum of useful air for engines A8, B8, C8 and D8 VIII Estimation of spray properties and impingement parameters IX Calculation of fuel injection rate X Influence of nozzle features XI Henrys Constant Hc for Fuel (n-Octane) in Oil XII Evaluation of gF* and gG* XIII In-Cylinder Oxidation of HC XIV Estimation of Wall Surface Temperature XV Experimental Data on HC emissions from DI Diesel Engines Index

An adaptive multi-grid chemistry (AMC) model for efficient simulation of HCCI and DI engine combustion

There is a need to reduce the computational expense of practical multidimensional combustion simulations. Simulation of Homogeneous Charge Compression Ignition (HCCI) engine processes requires consideration of detailed chemistry in order to capture the ignition and combustion characteristics. Even with relatively coarse numerical meshes and reduced chemistry mechanisms, calculation times are still unacceptably long. For the simulation of Direct Injection (DI) engines, fine meshes are needed to achieve the resolution required by the spray and mixing models, and they are computationally expensive even with reduced chemistry. In addition, the increasing application of CFD for engine design optimization is pushing the demand to reduce computational time. In current design optimizations, depending on the size of the parametric space, hundreds of individual simulations are needed. This work presents an efficient Adaptive Multi-grid Chemistry (AMC) model that can be used in engine CFD codes for simulations of HCCI and DI engines with detailed chemistry. It was found that the number of cells computed with the chemistry solver can be reduced by two orders of magnitude for HCCI engines. The results predicted by the present KIVA AMC code are also consistent with those calculated by the original code using every cell. In the method, progressively coarser grids are used for cells with similar gas properties in the chemistry calculation (up to four neighbour levels) or in the global method, cells are grouped without regard for their locations in the cylinder. Averaged and gradient-preserving remapping techniques used in multi-zone engine simulations were also explored. A parametric study was conducted for determining the model variables, such as the degree of local homogeneity for the multi-grid solvers. The simulation results were compared with experimental data obtained from a Honda engine operated with n-heptane under HCCI conditions for which directly measured in-cylinder temperature and H2O mole fraction data are available. In addition, simulation results were found to agree well with experimental data from a DI diesel engine operated under PCCI conditions with ultra-high EGR rates. It was found that computer time was reduced by a factor of ten for HCCI cases and two to three for DI cases without losing prediction accuracy.

Optimization study of the effects of bowl geometry, spray targeting, and swirl ratio for a heavy-duty diesel engine operated at low and high load

Abstract Optimization tools are used to recommend low-emission engine combustion chamber designs, spray targeting, and swirl ratio levels for a heavy-duty diesel engine operated at low load and high load. A non-dominated sorting genetic algorithm (NSGA II) was coupled with the KIVA computational fluid dynamics (CFD) code, as well as with an automated grid generation technique to conduct the multi-objective optimizations with goals of low emissions and improved fuel economy. The study identifies the aspects of the combustion and pollution formation that are affected by mixing, and offers guidance for better matching of the piston geometry with the spray plume geometry for enhanced mixing. By comparing the optimal results of low-load and high-load cases, the study reveals that different injection strategies and matching of the piston geometry with the spray plume are needed for different operating conditions. A non-parametric regression analysis tool was also used to post-process the optimized results in order to provide an understanding of the effects of each optimized parameter on fuel economy and pollutant formation. It was found that an optimal combination of spray targeting, swirl ratio, and bowl geometry exists that simultaneously minimizes emissions formation and offers improved fuel consumption.

Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design

In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.Copyright

Multi-dimensional Modelling of Diesel Combustion: Review

With the exponentially increasing computational power of modern computers, multi-dimensional Computational Fluid Dynamics (CFD) has found more and more applications in diesel engine research, design and development since its initiation in the late 1970s. Enhanced understandings of the physical processes of diesel combustion and correspondingly improved numerical models and methods have both driven simulations using multi-dimensional CFD tools from qualitative description towards quantitative prediction. To numerically resolve the complex physics of diesel combustion requires modelling of turbulent flows, high-pressure spray development, as well as combustion chemistry and relevant mechanism of pollutants formation. This chapter reviews the basic approach of multi-dimensional CFD modelling of diesel combustion, and focus is placed on the description of advanced turbulence, spray, and combustion models, and the introduction of popular CFD codes for engine simulations. In addition, recent efforts for reducing the computational expense of multi-dimensional CFD modelling are also discussed.

Multi-dimensional Modelling of Diesel Combustion: Applications

Various successful applications have proven the reliability of using multi-dimensional CFD tools to assist in diesel engine research, design and development. Those applications can be categorized as follows: using CFD tools to reveal details about invisible (or technically difficult and/or costly) in-cylinder processes of diesel combustion, so that guidance can be provided to improve engine designs in terms of emissions reduction and fuel economy; innovative combustion concepts can be evaluated numerically prior to experimental tests to reduce the number of investigated parameters and thus costs; important design parameters can be discovered by modelling engines of different sizes to establish engine size-scaling relationships and thus non-dimensionalize engine designs; by integration with optimization methodologies, CFD tools can also directly impact the design of optimum engine systems, such as piston geometry and injection parameters. Each of these aspects is described by relevant case studies in this chapter. The corresponding simulations were conducted with an improved version of the KIVA-3v2 code.

A Two-Zone Multigrid Model for SI Engine Combustion Simulation Using Detailed Chemistry

An efficient multigrid (MG) model was implemented for spark-ignited (SI) engine combustion modeling using detailed chemistry. The model is designed to be coupled with a level-set-G-equation model for flame propagation (GAMUT combustion model) for highly efficient engine simulation. The model was explored for a gasoline direct-injection SI engine with knocking combustion. The numerical results using the MG model were compared with the results of the original GAMUT combustion model. A simpler one-zone MG model was found to be unable to reproduce the results of the original GAMUT model. However, a two-zone MG model, which treats the burned and unburned regions separately, was found to provide much better accuracy and efficiency than the one-zone MG model. Without loss in accuracy, an order of magnitude speedup was achieved in terms of CPU and wall times. To reproduce the results of the original GAMUT combustion model, either a low searching level or a procedure to exclude high-temperature computational cells from the grouping should be applied to the unburned region, which was found to be more sensitive to the combustion model details.