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Diesel particulate samples were collected from a light duty engine operated at a single speed-load point with a range of biodiesel and conventional fuel blends. The oxidation reactivity of the samples was characterized in a laboratory reactor, and BET surface area measurements were made at several points during oxidation of the fixed carbon component of both types of particulate. The fixed carbon component of biodiesel particulate has a significantly higher surface area for the initial stages of oxidation, but the surface areas for the two particulates become similar as fixed carbon oxidation proceeds beyond 40%. When fixed carbon oxidation rates are normalized to total surface area, it is possible to describe the oxidation rates of the fixed carbon portion of both types of particulates with a single set of Arrhenius parameters. The measured surface area evolution during particle oxidation was found to be inconsistent with shrinking sphere oxidation.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

Many commercial fuels, including gasoline and diesel, are multicomponent hydrocarbons. During the fuel vaporization process, the volatile components evaporate first, which dominate the region near the nozzle exit. The lately evaporated vapor with high penetration has high molecular weight. Thus, ignition and combustion of multicomponent fuels are not only influenced by distribution of fuel vapor mass fraction, but also by distribution of the components. This paper presents a spark ignition and combustion model with consideration of such multicomponent effects for GDI engines. Ignition kernel growth due to flame front propagation is considered in the model to eliminate the sensitivity of the numerical mesh size on the results.

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer.

Ignition dwell is defined as the interval between end of fuel injection and start of combustion in early injection diesel combustion that exhibits HCCI-like characteristics. In this project, the impact of in-cylinder temperature and fuel-air mixing on the ignition dwell was investigated. The engine cycle was simulated using the 3-D CFD code KIVA-3V. Work done by Klingbeil (2002) has shown that ignition dwell allows more time for fuel and air to mix and drastically reduces emissions of NOX and particulate matter. Temperature is known to have a direct impact on the duration of ignition dwell. However, initial fuel-air distribution and mixing (i.e. at the end of fuel injection) may also impact the duration of ignition dwell. To investigate this, variations in EGR, fuel injection timing, engine valve actuation and swirl were simulated. The aim was to use these techniques to generate varying levels of fuel-air mixing and to check if ignition dwell was affected.

In most large-eddy simulation (LES) applications to two-phase engine flows, the liquid-air interactions need to be accounted for as source terms in the respective governing equations. Accurate calculation of these source terms requires the relative velocity “seen” by liquid droplets as they move across the flow, which generally needs to be estimated using a turbulent dispersion model. Turbulent dispersion modeling in LES is very scarce in the literature. In most studies on engine spray flows, sub-grid scale (SGS) models for the turbulent dispersion still follow the same stochastic approach originally proposed for Reynolds-averaged Navier-Stokes (RANS). In this study, an SGS dispersion model is formulated in which the instantaneous gas velocity is decomposed into a deterministic part and a stochastic part. The deterministic part is reconstructed using the approximate deconvolution method (ADM), in which the large-scale flow can be readily calculated.