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Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

A detailed comparison of cylinder pressure derived combustion performance and engine-out emissions is made between an all-metal single-cylinder light-duty diesel engine and a geometrically equivalent engine designed for optical accessibility. The metal and optically accessible single-cylinder engines have the same nominal geometry, including cylinder head, piston bowl shape and valve cutouts, bore, stroke, valve lift profiles, and fuel injection system. The bulk gas thermodynamic state near TDC and load of the two engines are closely matched by adjusting the optical engine intake mass flow and composition, intake temperature, and fueling rate for a highly dilute, low temperature combustion (LTC) operating condition with an intake O2 concentration of 9%. Subsequent start of injection (SOI) sweeps compare the emissions trends of UHC, CO, NOx, and soot, as well as ignition delay and fuel consumption.

This paper investigates flow and combustion in a full-cycle simulation of a four-stroke, three-valve HCCI engine by visualizing the flow with pathlines. Pathlines trace massless particles in a transient flow field. In addition to visualization, pathlines are used here to trace the history, or evolution, of flow fields and species. In this study evolution is followed from the intake port through combustion. Pathline analysis follows packets of intake charge in time and space from induction through combustion. The local scalar fields traversed by the individual packets in terms of velocity magnitude, turbulence, species concentration and temperatures are extracted from the simulation results. The results show how the intake event establishes local chemical and thermal environments in-cylinder and how the species respond (chemically react) to the local field.

A computational, three-dimensional approach to investigate the behavior of diesel soot particles in the micro-channels of wall-flow Diesel Particulate Filters is presented. The KIVA3V CFD code, already extended to solve the 2D conservation equations for porous media materials [1], has been enhanced to solve in 2-D and 3-D the governing equations for reacting and compressible flows through porous media in non axes-symmetric geometries. With respect to previous work [1], a different mathematical approach has been followed in the implementation of the numerical solver for porous media, in order to achieve a faster convergency as source terms were added to the governing equations. The Darcy pressure drop has been included in the Navier-Stokes equations and the energy equation has been extended to account for the thermal exchange between the gas flow and the porous wall.

The development of the Diesel Exhaust Filtration Analysis system (DEFA), which utilizes a rectangular wafer of the same substrate material as used in a full-scale Diesel Particulate Filter (DPF), is presented in this paper. Washcoat variations of the wafer substrate (bare, washcoat, and catalyzed washcoat) were available for testing. With this setup, the complications of flow and temperature distribution that arise in the full-scale DPF can be significantly minimized while critical parameters that affect the filtration performance can be fully controlled. Cold flow experiments were performed to test the system's reliability, and determine the permeability of each wafer type. A Computational Fluid Dynamics (CFD) package was utilized to ensure the flow uniformity inside the filter holder during the cold flow test.

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control.

An investigation of the permeability evolution of a diesel particulate filter channel wall as a function of soot loading was conducted. This investigation examined the effects of varying particle characteristics and two filtration velocities (4 and 8 cm/s) on the wall permeability throughout a 1 g/L soot loading. This study was possible using the Diesel Exhaust Filtration Analysis (DEFA) system that was modified to perform temperature controlled in-situ flow tests. The DEFA system allows for isolation of the pressure drop due to the filter wall and soot cake layer greatly simplifying the permeability calculation. Permeability evolution fundamentals and the effects of loading conditions were studied by filling 18 filters with the DEFA system. The filters were loaded using one of four operating conditions of a single-cylinder heavy-duty diesel engine. These operating conditions were comprehensively characterized giving insight into the effects of varying particle characteristics.

An integrated system model containing sub-models for diesel engine, emissions, and aftertreatment devices has been developed. The objective is to study engine-device and device-device interactions. The emissions sub-models used are for NOx and PM (particulate matter) prediction. The aftertreatment sub-models used include a diesel oxidation catalyst (DOC) and a diesel particulate filter (DPF). Controllers have also been developed to allow for transient simulations, active DPF regeneration, and prevention/control of runaway DPF regenerations. The integrated system-level model has been used to simulate DPF regeneration via exhaust fuel injection ahead of the DOC. In addition, the controller model can use intake throttling to assist in active DPF regeneration if needed. Regeneration studies have been done for both steady engine load and with load transients. High to low engine load transients are of particular interest because they can lead to runaway DPF regeneration.

Three Cordierite diesel particulate filters (DPF) with variations in the washcoat† (bare, washcoat-only, and catalyzed washcoat) were filled with equal amounts of PM (∼2 g/l) from a single steady-state engine operating condition (30% load, 1800 rpm). Two regeneration systems were used: an electrical furnace to extract the kinetic parameters by performing Temperature Programmed Oxidation (TPO) experiments and an inline burner to study how DPF washcoat variations affect active regeneration performance. Detailed emissions measurements were performed upstream and downstream of the DPF during the filtration and regeneration processes to quantify DPF filtration and regeneration performance. These measurements included gaseous emission, PM mass concentration, and particle size distribution.

An experimental study was performed to investigate diesel particulate filter (DPF) performance during filtration with the use of real-time measurement equipment. Three operating conditions of a single-cylinder 2.3-liter D.I. heavy-duty diesel engine were selected to generate distinct types of diesel particulate matter (PM) in terms of chemical composition, concentration, and size distribution. Four substrates, with a range of geometric and physical parameters, were studied to observe the effect on filtration characteristics. Real-time filtration performance indicators such as pressure drop and filtration efficiency were investigated using real-time PM size distribution and a mass analyzer. Types of filtration efficiency included: mass-based, number-based, and fractional (based on particle diameter). In addition, time integrated measurements were taken with a Rupprecht & Patashnick Tapered Element Oscillating Microbalance (TEOM), Teflon and quartz filters.

Three distinct types of diesel particulate matter (PM) are generated in selected engine operating conditions of a single-cylinder heavy-duty diesel engine. The three types of PM are trapped using typical Cordierite diesel particulate filters (DPF) with different washcoat formulations and a commercial Silicon-Carbide DPF. Two systems, an external electric furnace and an in-situ burner, were used for regeneration. Furnace regeneration experiments allow the collected PM to be classified into two categories depending on oxidation mechanism: PM that is affected by the catalyst and PM that is oxidized by a purely thermal mechanism. The two PM categories prove to contribute differently to pressure drop and transient filtration efficiency during in-situ regeneration.

A first law based regression model for estimating mean value engine torque on-board a diesel engine is presented. The model uses first law terms across the engine control volume in a regression built from least squares to predict engine torque. Torque information is often required by the engine ECM for torque based control and torque broadcast purposes. In the absence of real-time torque measurement torque estimation is usually achieved through look-up tables or empirical models. Given the increase in engine operating parameters as well as engine operating regimes as a result of emission control and exhaust aftertreatment technologies, accurate torque estimation has become more challenging as well as necessary.

An integrated system model containing sub-models for a multi-cylinder diesel engine, NOx and soot(PM) emissions, diesel oxidation catalyst (DOC) and diesel particulate filter (DPF) has been developed to simulate the engine and aftertreatment systems at transient engine operating conditions. The objective of this work is two-fold; ensure correct implementation of the integrated system level model and apply the integrated model to understand the fuel economy trade-off for various DPF regeneration strategies. The current study focuses on a 1.9L turbocharged diesel engine and its exhaust system. The engine model was built in GT-Power and validated against experimental data at full-load conditions. The DPF model is calibrated for the current engine application by matching the clean DPF pressure drop for different mass flow rates. Load, boost pressure, speed and EGR controllers are tuned and linked with the current engine model.

This article presents nondestructive neutron computed tomography (nCT) measurements of Diesel Particulate Filters (DPFs) as a method to measure ash and soot loading in the filters. Uncatalyzed and unwashcoated 200cpsi cordierite DPFs exposed to 100% biodiesel (B100) exhaust and conventional ultra low sulfur 2007 certification diesel (ULSD) exhaust at one speed-load point (1500 rpm, 2.6 bar BMEP) are compared to a brand new (never exposed) filter. Precise structural information about the substrate as well as an attempt to quantify soot and ash loading in the channel of the DPF illustrates the potential strength of the neutron imaging technique.

An investigation of the effect of microflow machining on the spray characteristics of diesel injectors was undertaken. A collection of four VCO injector tips were tested prior to and after an abrasive flow process using a high viscosity media. The injector nozzles were tested on a spray fixture. Rate of injection measurements and high-speed digital images were used for the quantification of the air entrainment rate. Comparisons of the spray characteristics and A/F ratios were made for conditions of before and after the abrasive flow process. Results showed a significant decrease in the injection-to-injection variability and improvement of the spray symmetry. A link between the quantity of air entrained and potential differences in spray plume internal chemical composition and temperature is proposed via equilibrium calculations.

Multi-dimensional computational efforts using comprehensive and skeletal kinetics have been made to investigate the cool flame region in HCCI combustion. The work was done in parallel to an experimental study that showed the impact of the negative temperature coefficient and the cool flame on the start of combustion using different fuels, which is now the focus of the simulation work. Experiments in a single cylinder CFR research engine with n-butane and a primary reference fuel with an octane number of 70 (PRF 70) were modeled. A comparison of the pressure and heat release traces of the experimental and computational results shows the difficulties in predicting the heat release in the cool flame region. The behavior of the driving radicals for two-stage ignition is studied and is compared to the behavior for a single-ignition from the literature. Model results show that PRF 70 exhibits more pronounced cool flame heat release than n-butane.

Burning velocities for unleaded conventional gasoline (CR-87) and air mixtures were determined experimentally over an extensive range of equivalence ratios at 353 K and 500 K and at atmospheric pressure. Nitrogen dilution effects on the laminar flame speed were also studied for selected equivalence ratios at these same conditions. Experimental measurements employed the stagnation jet-wall flame configuration and Particle Image Velocimetry (PIV). The laminar burning velocity was obtained using linear extrapolation of stretched flame data to zero stretch rate. The measured flame speeds were compared with numerical predictions using a minimized detailed kinetic model for primary reference fuel (PRF) mixtures, which was developed based on stirred reactor, shock tube and flow reactor data.

Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

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.

Detailed characteristics of morphology, nanostructures, and sizes were analyzed for particulate matter (PM) emissions from low-temperature combustion (LTC) modes of a single-cylinder, light-duty diesel engine. The LTC engines have been widely studied in an effort to achieve high combustion efficiency and low exhaust emissions. Recent reports indicate that the number of nucleation mode particles increased in a broad engine operating range, which implies a negative impact on future PM emissions regulations in terms of the nanoparticle number. However, the size measurement of solid carbon particles by commercial instruments is indeed controversial due to the contribution of volatile organics to small nanoparticles. In this work, an LTC engine was operated with various biofuel blends, such as blends (B20) of soy bean oil (soy methyl ester, SME20) and palm oil (palm methyl ester, PME20), as well as an ultra-low-sulfur diesel fuel.