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Diesel particulate filters are designed to reduce the mass emissions of diesel particulate matter and have been proven to be effective in this respect. Not much is known, however, about their effects on other unregulated chemical species. This study utilized source dilution sampling techniques to evaluate the effects of a catalyzed diesel particulate filter on a wide spectrum of chemical emissions from a heavy-duty diesel engine. The species analyzed included both criteria and unregulated compounds such as particulate matter (PM), carbon monoxide (CO), hydrocarbons (HC), inorganic ions, trace metallic compounds, elemental and organic carbon (EC and OC), polycyclic aromatic hydrocarbons (PAHs), and other organic compounds. Results showed a significant reduction for the emissions of PM mass, CO, HC, metals, EC, OC, and PAHs.

The effect of the ring pack storage mechanism on the hydrocarbon (HC) emissions from an air-cooled utility engine has been studied using a simplified ring pack model. Tests were performed for a range of engine load, two engine speeds, varied air-fuel ratio and with a fixed ignition timing using a homogeneous, pre-vaporized fuel mixture system. The integrated mass of HC leaving the crevices from the end of combustion (the crank angle that the cumulative burn fraction reached 90%) to exhaust valve closing was taken to represent the potential contribution of the ring pack to the overall HC emissions; post-oxidation in the cylinder will consume some of this mass. Time-resolved exhaust HC concentration measurements were also performed, and the instantaneous exhaust HC mass flow rate was determined using the measured exhaust and cylinder pressure.

Although turbocharging can extend the high load limit of low temperature combustion (LTC) strategies such as reactivity controlled compression ignition (RCCI), the low exhaust enthalpy prevalent in these strategies necessitates the use of high exhaust pressures for improving turbocharger efficiency, causing high pumping losses and poor fuel economy. To mitigate these pumping losses, the divided exhaust period (DEP) concept is proposed. In this concept, the exhaust gas is directed to two separate manifolds: the blowdown manifold which is connected to the turbocharger and the scavenging manifold that bypasses the turbocharger. By separately actuating the exhaust valves using variable valve actuation, the exhaust flow is split between two manifolds, thereby reducing the overall engine backpressure and lowering pumping losses. In this paper, results from zero-dimensional and one-dimensional simulations of a multicylinder RCCI light-duty engine equipped with DEP are 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.

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.

A single cylinder, Cummins NH, direct-injection, diesel engine has been operated in order to evaluate the effects of aromatic content and aromatic structure on diesel engine particulates. Results from three fuels are shown. The first fuel, a low sulfur Chevron diesel fuel was used as a base fuel for comparison. The other fuels consisted of the base fuel and 10% by volume of 1-2-3-4 tetrahydronaphthalene (tetralin) a single-ring aromatic and naphthalene, a double-ring aromatic. The fuels were chosen to vary aromatic content and structure while minimizing differences in boiling points and cetane number. Measurements included exhaust particulates using a mini-dilution tunnel, exhaust emissions including THC, CO2, NO/NOx, O2, injection timing, two-color radiation, soluble organic fraction, and cylinder pressure. Particulate measurements were found to be sensitive to temperature and flow conditions in the mini-dilution tunnel and exhaust system.

Fuel-borne catalysts (FBC) have demonstrated efficacy as an important strategy for integrated diesel emission control. The research summarized herein provides new methodologies for the characterization of engine-out speciated emissions. These analytical tools provide new insights on the mode of action and chemical forms of metal emissions arising from use of a platinum and cerium based commercial FBC, both with and without a catalyzed diesel particulate filter. Characterization efforts addressed metal solubility (water, methanol and dichloromethane) and particle size and charge of the target species in the water and solvent extracts. Platinum and cerium species were quantified using state-of-the-art high resolution plasma mass spectrometry. Liquid-chromatography-triple quad mass spectrometry techniques were developed to quantify potential parent Pt-FBC in the PM extracts. Speciation was examined for emissions from cold and warm engine cycles collected from an engine dynamometer.

Many researchers have studied and developed methods for on-board engine combustion misfire detection in production vehicles. Misfiring can damage the catalytic converter within a short time and can lead to increased emission levels. For that reason, the on-board detection of engine misfire is one requirement of the On Board Diagnosis II (OBDII) Regulation and a recent interest for many researchers. One object in this paper is to propose a misfire detection method for multi-cylinder SI engines. The detection is achieved by examining the estimated cylinder pressures and combustion heat release rates in engine cylinders. The Sliding Observer methodology is applied in these estimations. This detection method provides a reliable and low-cost way to diagnose engine misfires. The other object of the paper is to eliminate large estimation errors due to system unobservability and reconstruct cylinder pressures.

Progress on the development and validation of a CFD model for diesel engine combustion and flow is described. A modified version of the KIVA code is used for the computations, with improved submodels for liquid breakup, drop distortion and drag, spray/wall impingement with rebounding, sliding and breaking-up drops, wall heat transfer with unsteadiness and compressibility, multistep kinetics ignition and laminar-turbulent characteristic time combustion models, Zeldovich NOx formation, and soot formation with Nagle Strickland-Constable oxidation. The code also considers piston-cylinder-liner crevice flows and allows computations of the intake flow process in the realistic engine geometry with two moving intake valves. Significant progress has been made using a modified RNG k-ε turbulence model, and a multicomponent fuel vaporization model and a flamelet combustion model have been implemented.

A numerical simulation and optimization study was conducted for a medium speed direct injection diesel engine. The engine's operating characteristics were first matched to available experimental data to test the validity of the numerical model. The KIVA-3V ERC CFD code was then modified to allow independent spray events from two rows of nozzle holes. The angular alignment, nozzle hole size, and injection pressure of each set of nozzle holes were optimized using a micro-genetic algorithm. The design fitness criteria were based on a multi-variable merit function with inputs of emissions of soot, NOx, unburned hydrocarbons, and fuel consumption targets. Penalties to the merit function value were used to limit the maximum in-cylinder pressure and the burned gas temperature at exhaust valve opening. The optimization produced a 28.4% decrease in NOx and a 40% decrease in soot from the baseline case, while giving a 3.1% improvement in fuel economy.

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.

Tier 2 Emission standards enacted by the U.S. Environmental Protection Agency (EPA) require substantial emission reductions for new vehicles, including those with diesel engines. The standards are fuel neutral, and all light duty vehicles must eventually meet a fleet averaged emission level of Bin 5. To improve the emission capability for diesel engines, several advanced technologies have been investigated. These technologies include: common rail FIE with multi-injection capability, enhanced cooled EGR system with increased flow capability, variable geometry turbo charger, and a lower compression ratio piston. A new combustion approach using premixed diesel combustion was applied in the low load area for improving NOx and soot emissions significantly in the FTP-75 test cycle. Applying these technologies, engine out NOx was substantially reduced while maintaining similar soot levels.

Recent advances in ANN (Artificial Neural Network) technology enable new methods to be developed in sensor technology. There are a large number of cases where there exists a causal relationship between one or more inputs and a physical quantity, but where an easily implemented analytical relationship between the inputs and the output can not easily be found. In such cases, machine learning techniques, such as artificial neural networks, are able to model that functional relationship. However, using conventional computing hardware, these methods, while theoretically attractive, are too computationally intensive for field deployment in real-time systems. Using a hardware implementation of an artificial neural network architecture, these computational restrictions can be eliminated.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

Time-resolved measurements were made of the gas composition at the exhaust port of a direct-injection two-stroke engine operating at 2000 rpm and an air-fuel ratio of 30:1. A high-speed sampling valve capable of 1.0 ms (12 CAD) time resolution was used to collect samples 1 cm downstream of the exhaust port of the engine. The time-resolved NOx, CO2 and CO concentrations decreased continuously during the scavenging process due to the dilution by short-circuited air. The hydrocarbon emissions, however, behaved significantly differently from the other species. At the time of exhaust port opening the concentration was low, it reached a maximum value by BDC, then decreased slightly in the latter part of the scavenging event. The dilution rates calculated for the hydrocarbon data gave negative values, indicating that there was a significant production of hydrocarbons during the gas exchange period.

We present an approach in which an open-source software infrastructure is used for testing the behavior of autonomous vehicles through computer simulation. This software infrastructure is called CAVE, from Connected Autonomous Vehicle Emulator. As a software platform that allows rapid, low-cost and risk-free testing of novel designs, methods and software components, CAVE accelerates and democratizes research and development activities in the field of autonomous navigation.

An investigation of several diesel injector nozzles that produced different engine emissions performance was performed. The nozzle styles used were two VCO type nozzles that were manufactured using two different techniques, and two mini-sac nozzles that provided comparison. Fired experiments were conducted on a Detroit Diesel Series 50 engine. Optical access was obtained by substituting a sapphire window for one exhaust valve. Under high speed, high load, retarded injection timing conditions, it was discovered that each nozzle produced different specific soot and NOx emissions. High-speed film images were obtained. It was discovered that the temperature and KL factor results from the 2-color optical pyrometry showed significant differences between the nozzles. The authors propose the possibility that differences in air entrainment, caused by potential differences in CD due to surface finish, may contribute to the variance in emissions performance.