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Global demand for alternative fuels to combat rising energy costs has sparked a renewed interest in catalysts that can effectively remediate NOx emissions resulting from combustion of a range of HC based fuels. Because many of these new engine technologies rely on lean operating environments to produce efficient power, the resulting emissions are also present in a lean atmosphere. While HCs are easily controlled in such environments, achieving high NOx conversion to N2 has continued to elude fully satisfactory solution. Until recently, most approaches have relied on catalysts with precious metals to either store NOx and subsequently release it as N2 under rich conditions, or use NH3 SCR catalysts with urea injection to reduce NOx under lean conditions. However, new improvements in Ag based technologies also look very promising for NOx reduction in lean environments.

Turbulent Jet Ignition is an advanced pre-chamber initiated combustion system for an otherwise standard spark ignition engine found in current on-road vehicles. This next-generation pre-chamber design overcomes previous packaging obstacles by simply replacing the spark plug in a modern four-valve, pent roof spark ignition engine. Turbulent Jet Ignition enables very fast burn rates due to the ignition system producing multiple, distributed ignition sites, which consume the main charge rapidly and with minimal combustion variability. The fast burn rates allow for increased levels of dilution (lean burn and/or EGR) when compared to conventional spark ignition combustion, with dilution levels being comparable to other low temperature combustion technologies (homogeneous charge compression ignition - HCCI) without the complex control drawbacks.

This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bézier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

Turbulent jet ignition is a pre-chamber ignition enhancement method that produces a distributed ignition source through the use of a chemically active turbulent jet which can replace the spark plug in a conventional spark ignition engine. In this paper combustion visualization and characterization was performed for the combustion of a premixed propane/air mixture initiated by a pre-chamber turbulent jet ignition system with no auxiliary fuel injection, in a rapid compression machine. Three different single orifice nozzles with orifice diameters of 1.5 mm, 2 mm, and 3 mm were tested for the turbulent jet igniter pre-chamber over a range of air to fuel ratios. The performance of the turbulent jet ignition system based on nozzle orifice diameter was characterized by considering both the 0-10 % and the 10-90 % burn durations of the pressure rise due to combustion.

Autoignition delay times of two full blend gasoline fuels (high and low RON) were explored in a rapid compression machine. CO2 dilution by mass was introduced at 0%, 15%, and 30% levels with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy is used to represent exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines by using CO2 as a surrogate for major EGR constituents(N2, CO2, H2O). Experiments were conducted over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. The full blend fuels were admitted directly into the combustion chamber for mixture preparation using the direct test chamber (DTC) approach. CO2 addition retarded the autoignition times for the fuels studied here. The retarding effect of the CO2 dilution was more pronounced in the NTC region when compared to the lower and higher temperature range.

Continuous efforts to improve thermal efficiency and reduce exhaust emissions of internal combustion engines have resulted in development of various solutions towards improved lean burn ignition systems in spark ignition engines. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is one of the leading technologies in that regard which offers higher thermal efficiency and reduced NOx emissions due to its ability to operate with very lean or highly dilute mixtures. Compared to other pre-chamber ignition technologies, the DM-TJI system has the distinct capability to work with a very high level of EGR dilution (up to ~40%). Thus, this system enables the use of a three-way catalyst (TWC). Auxiliary air supply for pre-chamber purge allows this system to work with such high EGR dilution rate. This work presents the results of experimental investigation carried out with a Dual Mode, Turbulent Jet Ignition (DM-TJI) optical engine equipped with a cooled EGR system.

Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.

Over the years, many studies have examined the natural gas flame characteristics with either CO2, H2O, or N2 dilution in order to investigate the exhaust gas recirculation (EGR) effect on the performance of natural gas vehicles. However, studies analyzing the actual EGR concentration are very scarce. In the present study, spherically expanding flames were employed to investigate the EGR effect on the laminar flame speed (LFS) and the burned gas Markstein length (Lb) of premixed CH4/air flames at 373 K and 3 bar. The EGR mixture was imitated with a mixture of 9.50% CO2 + 19.01% H2O + 71.49% N2 by mole. EGR ratios of 0%, 5%, 10%, and 15% were tested. Experimental results show that LFS values are lowered by 20-23%, 38-43% and 53-54% due to 5%, 10% and 15% EGR, respectively. Additionally, it was observed that Lb values slightly increase at high equivalence and EGR ratios, where CH4 flames are more stable and more stretched.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

With increasingly stringent emissions regulations and concurrent requirements for enhanced engine thermal efficiency, a comprehensive characterization of the automotive gasoline fuel spray has become essential. The acquisition of accurate and repeatable spray data is even more critical when a combustion strategy such as gasoline direct injection is to be utilized. Without industry-wide standardization of testing procedures, large variablilities have been experienced in attempts to verify the claimed spray performance values for the Sauter mean diameter, Dv90, tip penetration and cone angle of many types of fuel sprays. A new SAE Recommended Practice document, J2715, has been developed by the SAE Gasoline Fuel Injection Standards Committee (GFISC) and is now available for the measurement and characterization of the fuel sprays from both gasoline direct injection and port fuel injection injectors.

Low-cost lean NOx aftertreatment is one of the main challenges facing high-efficiency gasoline and diesel engines operating with lean mixtures. While there are many candidate technologies, they all offer tradeoffs. We have investigated a multi-component Dual SCR aftertreatment system that is capable of obtaining NOx reduction efficiencies of greater than 90% under lean conditions, without the use of precious metals or urea injection into the exhaust. The Dual SCR approach here uses an Ag HC-SCR catalyst followed by an NH3-SCR catalyst. In bench reactor studies from 150 °C to 500 °C, we have found, for modest C/N ratios, that NOx reacts over the first catalyst to predominantly form nitrogen. In addition, it also forms ammonia in sufficient quantities to react on the second NH3-SCR catalyst to improve system performance. The operational window and the formation of NH3 are improved in the presence of small quantities of hydrogen (0.1–1.0%).

This study focuses on the combustion visualization of spark ignition combustion in an optical single cylinder engine using natural gas and propane at several air to fuel ratios and speed-load operating points. Propane and natural gas fuels were compared as they are the most promising gaseous alternative fuels for reciprocating powertrains, with both fuels beginning to find wide market penetration on the fleet level across many regions of the world. Additionally, when compared to gasoline, these gaseous fuels are affordable, have high knock resistance and relatively low carbon content and they do not suffer from the complex re-fueling and storage problems associated with hydrogen.

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

Air-to-fuel (A/F) ratio is the mass ratio of the air-to-fuel mixture trapped inside a cylinder before combustion begins, and it affects engine emissions, fuel economy, and other performances. Using an A/F ratio and dual-fuel ratio control oriented engine model, a multi-input-multi-output (MIMO) sliding mode control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems. The control target is to regulate the A/F ratio at a desired level (e.g., at stoichiometric) and fuel ratio (ratio of PFI fueling vs. total fueling) to any desired level between zero and one. A MIMO sliding mode controller was designed with guaranteed stability to drive the system A/F and fuel ratios to the desired target under various air flow disturbances.

A single-cylinder engine was used to study the potential of a high-efficiency combustion concept called gasoline direct-injection compression-ignition (GDCI). Low temperature combustion was achieved using multiple injections, intake boost, and moderate EGR to reduce engine-out NOx and PM emissions engine for stringent emissions standards. This combustion strategy benefits from the relatively long ignition delay and high volatility of regular unleaded gasoline fuel. Tests were conducted at 6 bar IMEP - 1500 rpm using various injection strategies with low-to-moderate injection pressure. Results showed that triple injection GDCI achieved about 8 percent greater indicated thermal efficiency and about 14 percent lower specific CO2 emissions relative to diesel baseline tests on the same engine. Heat release rates and combustion noise could be controlled with a multiple-late injection strategy for controlled fuel-air stratification. Estimated heat losses were significantly reduced.

This paper includes a numerical and experimental study of fluid flow in automotive catalytic converters. The numerical work involves using computational fluid dynamics (CFD) to perform three-dimensional calculations of turbulent flow in an inlet pipe, inlet cone, catalyst substrate (porous medium), outlet cone, and outlet pipe. The experimental work includes using hot-wire anemometry to measure the velocity profile at the outlet of the catalyst substrate, and pressure drop measurements across the system. Very often, the designer may have to resort to offset inlet and outlet cones, or angled inlet pipes due to space limitations. Hence, it is very difficult to achieve a good flow distribution at the inlet cross section of the catalyst substrate. Therefore, it is important to study the effect of the geometry of the catalytic converter on flow uniformity in the substrate.

Human intraspecific variation is a complex problem, but may be better understood by using computational models in tandem with knowledge about the genetic bases of phenotypic traits. These results can be used in a multitude of settings. To move closer to this goal, biologically-realistic mappings between genotype and phenotype are constructed using genetic algorithm and neural network-like models. These models allow for gene-gene and gene-environment interactions to be characterized in the resulting phenotype. Two types of model are introduced: a simple, two-layer model, and a more complex model. The final section will focus on trends of growth and development in relation to relationship to modeling anthropometric traits and other morphological phenomena.

Noise radiated by engine front cover is estimated using statistical energy analysis approach. Correlations are made with the results obtained using FEM and BEM approaches. Parametric studies are also carried out to assess the regions of applicability of the SEA approach for this class of structures.

Lower extremity (knee) injury prediction resulting from impact trauma is currently based on a bone fracture criterion derived from experiments on predominantly aged cadavers. Subsequent experimental and theoretical studies indicate that more aged, pathological specimens require higher, not lower, loads to initiate bone fracture. This suggests that a bone fracture criterion based solely on aged specimens may not be representative of the current driving population. In the current study, we sought to determine if cadaver age, physical size, sex, baseline joint pathology, or patellar geometry correlated with fracture load. An analysis was made of data from previous impact experiments conducted on fifteen isolated cadaver knees using a consistent impact protocol. The protocol consisted of sequentially increasing the impact energy with a rigid interface until gross fracture. Gross bone fractures occurred at loads of 6.9±2.0 kN (range 3.2 to 10.6 kN) using this protocol.

Computer simulation was used as a complement to crash and injury field data analysis and physical sled and barrier tests to investigate and predict the spinal injuries of a rear impact in an IndyCar. The model was expected to relate the spinal loads to the observed injuries, thereby predicting the probability and location of spinal fractures. The final goal is to help reduce the fracture risk by optimizing the seat and restraint system design and the driver's position using computer modeling and sled testing. MADYMO Full FE Human Body Model (HBM) was selected for use because of its full spinal structural details and its compatibility with the vehicle and restraint system models. However, the IndyCar application imposed unique challenges to the HBM. First, the driver position in a race car is very different from that in a typical passenger car.