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This experimental work focuses on defining the detailed morphology of secondary emission products derived from the combustion of cerium (Ce) fuel-borne catalyst (FBC) doped with diesel fuel. Cerium is often used to promote the oxidation of diesel particulates collected in diesel aftertreatment systems, such as diesel particulate filters (DPFs). However, it is suspected that the secondary products could be emitted from the vehicle tailpipe without being effectively filtered by the aftertreatment systems. In this work, these secondary emissions were identified by means of a high-resolution transmission electron microscope (TEM), and their properties were examined in terms of morphology and chemistry. In preparation for fuel doping, a cerium-based aliphatic organic compound solution was mixed with a low-sulfur (110 ppm) diesel fuel at 50 ppm in terms of weight concentration.

The classical methods of measuring acoustic absorption coefficient using an impedance tube and a reverberation chamber are well established [1, 2]. However, these methods are not suitable for in-situ applications. The two in-situ methods; single channel microphone (P- probe) and dual channel acoustic pressure and particle velocity (Pu-probe) methods based on measurement of impulse response functions of the material surface under test, provide considerable advantage in data acquisition, signal processing, ease and mobility of measurement setup. This paper evaluates the measurement techniques of these two in-situ methods and provides results of acoustic absorption coefficient of a commercial artificial Astroturf, a Dow quash material, and a grass surface.

The Time Variant Discrete Fourier Transform was implemented as a real-time order tracking method using developed software and commercially available hardware. The time variant discrete Fourier transform (TVDFT) with the application of the orthogonality compensation matrix allows multiple tachometers to be tracked with close and/or crossing orders to be separated in real-time. Signal generators were used to create controlled experimental data sets to simulate tachometers and response channels. Computation timing was evaluated for the data collection procedure and each of the data processing steps to determine how each part of the process affects overall performance. Many difficulties are associated with a real-time data collection and analysis tool and it becomes apparent that an understanding of each component in the system is required to determine where time consuming computation is located.

As part of an ongoing vehicle benchmarking effort at Argonne National Laboratory, four different power-split HEVs were tested on a chassis dynamometer to analyze their operational behavior and understand the control strategy and its relationship to the individual features of the vehicles tested. The controls that select the way in which engine operation matches best engine efficiency load points appears to have evolved From the Gen 1 to the Gen 2 Toyota Prius. The Ford Escape HEV and Lexus RX400h were also analyzed by using similar methods, although the data are not as extensive as those for the Prius hybrids. Whereas the Escape HEV appeared to operate in a manner similar to that of the Gen 1 Prius, the RX400h (with its relatively large engine) loads the engine with excess battery charge to keep it operating at higher power levels - apparently to improve overall efficiency.

Detailed diesel particulates morphology, nanostructures, fractal geometry, and nitrogen oxides (NOx) emissions were analyzed for five different test fuels in a 1.7-L, common-rail direct-injection diesel engine. The accurately formulated fuels permit the effects of sulfur, paraffins, aromatics, and naphthene concentrations to be determined. A novel thermophoretic sampling technique was used to collect particulates immediately after the exhaust valves. The morphology and nanostructures of particulate samples were examined, imaged with a high-resolution transmission electron microscope (HRTEM), and quantitatively analyzed with customized digital image processing/data acquisition systems. The results show that the particle sizes and the total mass of particulate matter (PM) emissions correlate most strongly with the fuels' aromatics and sulfur content.

The physical and morphological properties of the particulate matter emitted from a 1.7-liter light-duty diesel engine were characterized by observing its evolution in size and fractal geometry along the exhaust system. A common-rail direct-injection diesel engine, the exhaust system of which was equipped with a turbocharger, EGR, and two oxidation catalysts, was powered with a California low-sulfur diesel fuel at various engine-operating conditions. A unique thermophoretic sampling system, a high-resolution transmission electron microscope (TEM), and customized image processing/data acquisition systems were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively. The measurements were carried out at four different positions along the exhaust pipe.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

A thermophoretic particulate sampling device was used to investigate the detailed morphology and microstructure of diesel particulates at various engine-operating conditions. A 75 HP Caterpillar single-cylinder direct-injection diesel engine was operated to sample particulate matter from the high-temperature exhaust stream. The morphology and microstructure of the collected diesel particulates were analyzed using a high-resolution transmission electron microscope and subsequent image processing/data acquisition system. The analysis revealed that spherical primary particles were agglomerated together to form large aggregate clusters for most of engine speed and load conditions. Measured primary particle sizes ranged from 34.4 to 28.5 nm at various engine-operating conditions. The smaller primary particles observed at high engine-operating conditions were believed to be caused by particle oxidation at the high combustion temperature.

Advanced numerical techniques, such as fuzzy logic and neural networks have been applied in this work to digital images acquired on a mono-component fuel spray (iso-octane), in order to define, in a stochastic way, the gas-liquid interface evolution. The image is a numerical matrix and so it is possible to characterize geometrical parameters and the time evolution of the jet by using deterministic, statistical stochastic and other several kinds of approach. The algorithm used works with the fuzzy logic concept to binarize the shades gray of the pixel, depending them, by using the schlieren technique, on the gas density. Starting from a primary fixed threshold, the applied technique, can select the ‘gas’ pixel from the ‘liquid’ pixel and so it is possible define the first most probably boundary lines of the spray.

The effect of flow direction towards the spark plug electrodes on ignition parameters is analyzed using an innovative spark aerodynamics fixture that enables adjustment of the spark plug gap orientation and plug axis tilt angle with respect to the incoming flow. The ignition was supplied by a long discharge high energy 110 mJ coil. The flow was supplied by compressed air and the spark was discharged into the flow at varying positions relative to the flow. The secondary ignition voltage and current were measured using a high speed (10MHz) data acquisition system, and the ignition-related metrics were calculated accordingly. Six different electrode designs were tested. These designs feature different positions of the electrode gap with respect to the flow and different shapes of the ground electrodes. The resulting ignition metrics were compared with respect to the spark plug ground strap orientation and plug axis tilt angle about the flow direction.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

The paper reports an innovative method of analysis based on an advanced statistical techniques applied to images captured by a high-speed camera that allows highlighting phenomena and anomalies hardly detectable by conventional optical diagnostic techniques. The images, previously elaborated by neural network tools in order for clearly identifying the contours, have been analyzed in their time evolution as pseudo-chaotic variables that may have internal periodic components. In addition to the Fourier analysis, tools as Lyapunov and Hurst exponents and average Kω permitted to detect the chaos level of the signals. The use of this technique has permitted to distinguish periodic oscillations from chaotic variations and to detect those parameters that actually determine the spray behavior.

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.

This study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.

The detailed morphological properties of diesel particulate matter were analyzed along the exhaust system at various engine operating conditions (in a range of 1000 - 2500 rpm and 10 - 75 % loads of maximum torques). A 1.7-L turbocharged light-duty diesel engine was powered with California low-sulfur diesel fuel injected by a common-rail injection system, of which particulate emissions were controlled by an exhaust gas recirculation (EGR) system and two oxidation catalysts. A unique thermophoretic sampling system first developed for internal combustion engine research, a high-resolution transmission electron microscope (TEM), and a customized image processing/data acquisition system were key instruments that were used for the collection of particulate matter, subsequent imaging of particle morphology, and detailed analysis of particle dimensions and fractal geometry, respectively.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques.

The analysis of a spray behavior is confined to study the fluid dynamic parameters such as axial and radial velocity of the droplets, size distribution of the droplets, and geometrical aspect as the penetration length. In this paper, the spray is considered like a dynamic system and consequently it can be described by a number of parameters that characterize its dynamic behavior. The parameter chosen to describe the dynamic behavior is the external cone angle. This parameter has been detected by using an experimental injection chamber, a multi-hole (8 holes) injector for GDI applications and recorded by a high-speed C-Mos camera. The images have been elaborated by a fuzzy logic and neural network algorithm and are processed by using a chaos deterministic theory. This procedure carries out a map distribution of the working point of the spray and determines the stable (signature of the spray) and instable behavior.

In recent years, great interest on NVH characteristics of vehicles has been paid by all the big automotive manufacturers. Interior acoustic comfort is now one of the main key factors in vehicle development process, since it contributes to improved product overall quality. Therefore, in automotive industry advanced NVH refinement needs to work in synergy with all research activities. Assessing the level of experienced noise in interior cabin requires particular arrangements for ensuring adequate measurement accuracy (AC system off, closed window, etc.). The use of parameters such as the level of seat vibration, not affected by the acoustic field conditions inside the vehicle, could facilitate experiments in parallel with engine/vehicle calibration activities.

In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine.