Search Results

The reduction of cycle-to-cycle variations (CCV) is a prerequisite for the development and control of spark-ignition engines with increased efficiency and reduced engine-out emissions. To this end, Large-Eddy Simulations (LES) can improve the understanding of stochastic in-cylinder phenomena during the engine design process, if the employed modeling approach is sufficiently accurate. In this work, an inhouse code has been used to investigate CCV in a direct-injected spark ignition (DISI) engine under fuel-lean conditions with respect to a stoichiometric baseline operating point. It is shown that the crank angle when a characteristic fuel mass fraction is burned, e.g. MFB50, correlates with the equivalence ratio computed as a local average in the vicinity of the spark plug. The lean operating point exhibits significant CCV, which are shown to be correlated also with the in-cylinder subfilter-scale (SFS) kinetic energy.

It is well know that the internal flow field and nozzle geometry affected the spray behavior, but without high-speed microscopic visualization, it is difficult to characterize the spray structure in details. Single-hole diesel injectors have been used in fundamental spray research, while most direct-injection engines use multi-hole nozzle to tailor to the combustion chamber geometry. Recent engine trends also use smaller orifice and higher injection pressure. This paper discussed the quasi-steady near-nozzle diesel spray structures of an axisymmetric single-hole nozzle and a symmetric two-hole nozzle configuration, with a nominal nozzle size of 130 μm, and an attempt to correlate the observed structure to the internal flow structure using computational fluid dynamic (CFD) simulation. The test conditions include variation of injection pressure from 30 to 100 MPa, using both diesel and biodiesel fuels, under atmospheric condition.

Direct injection (DI) of compressed natural gas (CNG) is a promising technology to increase the indicated thermal efficiency of internal combustion engines (ICE) while reducing exhaust emissions and using a relatively low-cost fuel. However, design and analysis of DI-CNG engines are challenging because supersonic gas jet emerging from the DI injector results in a very complex in-cylinder flow field containing shocks and discontinuities affecting the fuel-air mixing. In this article, numerical simulations are used supported by validation to investigate the direct gas injection and its influence on the flow field and mixing in an optically accessible ICE. The simulation approach involves computation of the in-nozzle flow with highly accurate Large-Eddy Simulations, which are then used to obtain a mapped boundary condition. The boundary condition is applied in Unsteady Reynolds Averaged Navier-Stokes simulations of the engine to investigate the in-cylinder velocity and mixing fields.

The performance of Gasoline Direct Injection (GDI) engines is governed by multiple physical processes such as the internal nozzle flow and the mixing of the liquid stream with the gaseous ambient environment. A detailed knowledge of these processes even for complex injectors is very important for improving the design and performance of combustion engines all the way to pollutant formation and emissions. However, many processes are still not completely understood, which is partly caused by their restricted experimental accessibility. Thus, high-fidelity simulations can be helpful to obtain further understanding of GDI injectors. In this work, advanced simulation and experimental methods are combined in order to study the spray characteristics of two different 3-hole GDI injectors.

Gasoline direct injection (GDI) has the potential to greatly improve internal combustion engine performance through precise control of the injection rate, timing, and combustion of the fuel. A thorough characterization of the hydrodynamics of fuel injection has to come from a precise, quantitative analysis of the sprays, especially in the near-nozzle region. A lack of knowledge of the fuel-spray dynamics has severely limited computational modeling of the sprays and design of improved injection systems. Previously, the structure and dynamics of highly transient fuel sprays have never been visualized or reconstructed in three dimensions (3D) due to numerous technical difficulties. By using an ultrafast x-ray detector and intense monochromatic x-ray beams from synchrotron radiation, the fine structures and dynamics of 1-ms GDI fuel sprays from an outwardly opening nozzle were elucidated by a newly developed, ultrafast, microsecond computed microtomography (CT) technique.

This paper presents the utilization of alternative correlation functions in the Kriging method for generating surrogate models (metamodels) for the performance of the bearings in an internal combustion engine. Originally, in the Kriging method an anisotropic exponential covariance function is developed by selecting optimal correlation parameters through optimization. In this paper an alternative nonparametric isotropic covariance approach is employed instead for generating the correlation functions. In this manner the covariance for spatial data is evaluated in a more straightforward manner. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space.

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine. The metamodels are employed for performing probabilistic analyses for the engine bearings. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space. An integrated system-level engine simulation model, consisting of a flexible crankshaft dynamics model and a flexible engine block model connected by a detailed hydrodynamic lubrication model, is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space.

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine without performing time consuming analyses. The metamodels are developed based on results from actual simulation solvers computed at a limited number of sample points, which sample the design space. A finite difference bearing solver is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric Latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual bearing performance solver over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses.

Although spark-ignited engines have a considerable development history, the relevant flow physics and geometry design implications are still not fully understood. One reason is the lack of experimental and numerical methods with sufficiently high resolution or capabilities of capturing stochastic phenomena which could be used as part of the development cycle. More recently, Large-Eddy simulation (LES) has been identified as a promising technique to establish a better understanding of in-cylinder flow variations. However, simulations of engine configurations are challenging due to resolution as well as modeling requirements and computational cost for these unsteady multi-physics problems. LES on full engine geometries can even be prohibitively expensive. For this reason, the size of the computational LES domain is here reduced to the region of physical interest and boundary conditions are obtained from a RANS simulation of the whole experimental flow domain.

In the foreseeable future, the transportation sector will continue to rely on internal combustion engines. Therefore, reduction of engine-out emissions and increase in engine efficiency are important goals to meet future legislative regulations and restricted fuel resources. One viable option, which provides lower peak temperatures and increased mixture homogeneity and thus simultaneously reduces nitric oxide as well as soot, is a low-temperature combustion (LTC) concept. However, this might result in an increase of unburnt hydrocarbon, carbon monoxide, and combustion noise due to early combustion phasing and lower engine efficiency. Various studies show that these drawbacks can be compensated by advanced injection strategies, e.g. by employing multiple injections. The aim of this work is to identify the optimum injection strategy, which enables a wide range of engine operating points in LTC mode with reduced engine-out emissions.