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The light-medium load operating range (4-7 bar net IMEP) presents many challenges for advanced low temperature combustion strategies utilizing low cetane fuels (specifically, 87-octane gasoline) in light-duty, high-speed engines. The overly lean overall air-fuel ratio (Φ≺0.4) sometimes requires unrealistically high inlet temperatures and/or high inlet boost conditions to initiate autoignition at engine speeds in excess of 1500 RPM. The objective of this work is to identify and quantify the effects of variation in input parameters on overall engine operation. Input parameters including inlet temperature, inlet pressure, injection timing/duration, injection pressure, and engine speed were varied in a ~0.5L single-cylinder engine based on a production General Motors 1.9L 4-cylinder high-speed diesel engine.

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.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

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.

The objective of this study is to increase fundamental understanding of the effects of fuel composition and properties on low temperature combustion (LTC) and to identify major properties that could enable engine performance and emission improvements, especially under high load conditions. A series of experiments and computational simulations were conducted under LTC conditions using 67% EGR with 9.5% inlet O₂ concentration on a single-cylinder version of the General Motors Corporation 1.9L direct injection diesel engine. This research investigated the effects of Cetane number (CN), volatility and total aromatic content of diesel fuels on LTC operation. The values of CN, volatility, and total aromatic content studied were selected in a DOE (Design of Experiments) fashion with each variable having a base value as well as a lower and higher level. Timing sweeps were performed for all fuels at a lower load condition of 5.5 bar net IMEP at 2000 rpm using a single-pulse injection strategy.

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 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.

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.

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.

The applicability of several popular diesel particulate matter (PM) measurement techniques to low temperature combustion is examined. The instruments' performance in measuring low levels of PM from advanced diesel combustion is evaluated. Preliminary emissions optimization of a high-speed light-duty diesel engine was performed for two conventional and two advanced low temperature combustion engine cases. A low PM (<0.2 g/kg_fuel) and NOx (<0.07 g/kg_fuel) advanced low temperature combustion (LTC) condition with high levels of exhaust gas recirculation (EGR) and early injection timing was chosen as a baseline. The three other cases were selected by varying engine load, injection timing, injection pressure, and EGR mass fraction. All engine conditions were run with ultra-low sulfur diesel fuel. An extensive characterization of PM from these engine operating conditions is presented.

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 Soot-NOx Trap (SNT) is a combinatorial aftertreatment device intended to decrease both particulate and NOx emissions simultaneously. A system-level Soot-NOx Trap model was developed by adding Lean NOx Trap kinetics to a 1D Diesel Particulate Filter model. The hybrid model was validated against each parent model for the limiting cases, then exercised to investigate the interacting redox behavior. Modulations in temperature and exhaust air-fuel ratio were investigated for their ability to facilitate particulate oxidation and NOx reduction in the trap.

An integrated system model containing sub-models for a diesel engine, NOx and soot emissions, and a diesel particulate filter (DPF) has been used to simulate stead-state engine operating conditions. The simulation results have been used to investigate the effect of DPF loading and passive regeneration on engine performance and emissions. This work is the continuation of previous work done to create an overall diesel engine/exhaust system integrated model. As in the previous work, a diesel engine, exhaust system, engine soot emissions, and diesel particulate filter (DPF) sub-models have been integrated into an overall model using Matlab Simulink. For the current work new sub-models have been added for engine-out NOx emissions and an engine feedback controller. The integrated model is intended for use in simulating the interaction of the engine and exhaust aftertreatment components.

A two dimensional CFD model has been developed to study the mechanism of soot deposition on the porous wall surface in a Diesel Particulate Filter (DPF). The goal is to improve understanding of the soot deposition and its interaction with the hydrodynamic behaviour of the device. The KIVA3V CFD code and pre-processor were modified to simulate a single channel in a DPF. The code was extended to solve the conservation equations in porous media materials, to account for the sticking of particles on a porous surface and to evaluate the increasing resistance to the flow as the soot inside the trap accumulates. The code is already configured to track Lagrangian particles and these were modified to represent the soot particles in the flow. The code pre-processor was modified to allow definition of a double-symmetric geometry and to specify porous cells.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

A procedure has been developed to build system level predictive models that incorporate physical laws as well as information derived from experimental data. In particular a soot model was developed, trained and tested using experimental data. It was seen that the model could fit available experimental data given sufficient training time. Future accuracy on data points not encountered during training was estimated and seen to be good. The approach relies on the physical phenomena predicted by an existing system level phenomenological soot model coupled with ‘weights’ which use experimental data to adjust the predicted physical sub-model parameters to fit the data. This approach has developed from attempts at incorporating physical phenomena into neural networks for predicting emissions. Model training uses neural network training concepts.

A modeling study has been conducted in order to characterize the heat transfer in an automotive diesel exhaust system. The exhaust system model, focusing on 2 exhaust pipes, has been created using a transient 1-D engine flow network simulation program. Model results are in excellent agreement with experimental data gathered before commencement of the modeling study. Predicted pipe exit stream temperatures are generally within one percent of experimental values. Sensitivity analysis of the model was the major focus of this study. Four separate variables were chosen for the sensitivity analysis. These being the external convective heat transfer coefficient, external emissivity, mass flow rate of exhaust gases, and amplitude of incoming pressure fluctuations. These variables were independently studied to determine their contribution to changes in exhaust gas stream temperature and system heat flux. There are two primary benefits obtained from conducting this analysis.