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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.

Driving comfort and automotive product quality are strongly associated with the vibration that is transmitted to the occupants of a vehicle at the points of contact to the human body, including the seat, steering wheel, and pedals. Of these three contact locations, the seats have the most general importance, as all occupants of a vehicle experience seat vibration. Particularly relevant to driving comfort is the way in which vehicle occupants perceive seat vibration, which may be different than expected considering sensor measured vibration levels. Much of the interest in seat vibration has been focused on internal combustion engine powertrain vibration, especially idle vibration. However, electrification of vehicles changes the focus from low frequency idle vibration to higher frequency vibration sources.

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.

Environmental perception is considered an essential module for autonomous driving and Advanced Driver Assistance System (ADAS). Recently, deep Convolutional Neural Networks (CNNs) have become the State-of-the-Art with many different architectures in various object detection problems. However, performances of existing CNNs have been dropping when detecting small objects at a large distance. To deploy any environmental perception system in real world applications, it is important that the system achieves high accuracy regardless of the size of the object, distance, and weather conditions. In this paper, a robust sensor fused object detection system is proposed by utilizing the advantages of both vision and automotive radar sensors. The proposed system consists of three major components: 1) the Coordinate Conversion module, 2) Multi level-Sensor Fusion Detection (MSFD) system, and 3) Temporal Correlation filtering module.

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.

As advanced vehicle controls and autonomy become mainstream in the automotive industry, the need to employ traditional mathematical models and control strategies arises for the purpose of simulating autonomous vehicle handling maneuvers. This study focuses on lane change maneuvers for autonomous vehicles driving at low speeds. The lane change methodology uses PID (Proportional-Integral-Derivative) controller to command the steering wheel angle, based on the yaw motion and lateral displacement of the vehicle. The controller was developed and tested on a bicycle model of an electric vehicle (a Chevrolet Bolt 2017), with the implementation done in MATLAB/Simulink. This simple mathematical model was chosen in order to limit computational demands, while still being capable of simulating a smooth lane change maneuver under the direction of the car’s mission planning module at modest levels of lateral acceleration.

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.

The fatal automobile accidents can be attributed to fatigued and distracted driving by drivers. Driver Monitoring Systems alert the distracted drivers by raising alarms. Most of the image based driver drowsiness detection systems face the challenge of failure proof performance in real time applications. Failure in face detection and other important part (eyes, nose and mouth) detections in real time cause the system to skip detections of blinking and yawning in few frames. In this paper, a real time robust and failure proof driver drowsiness detection system is proposed. The proposed system deploys a set of detection systems to detect face, blinking and yawning sequentially. A robust Multi-Task Convolutional Neural Network (MTCNN) with the capability of face alignment is used for face detection. This system attained 97% recall in the real time driving dataset collected. The detected face is passed on to ensemble of regression trees to detect the 68 facial landmarks.

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.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

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.

The objective of this work was to design an automated bed clearing mechanism for the Anet brand A8 3D printer, which uses Fused Deposition Modeling (FDM) process. This work has been carried out as a capstone course. Many OEMs are focusing on using functional 3D printed parts to replace metal parts that otherwise require complex assemblies or to reduce weight. The concept behind the work presented in this paper was to allow every user to be able to print multiple parts without human interaction. This saves time to load and unload one part at a time. The idea was to develop a universal bed clearing mechanism that can be used for most brands of 3D printers. Upon researching into the many different styles and designs of printers, it became clear that the designs are different and complex to create a universal product. It was decided to aim for the most common style of 3D printers for which easy modeling and testing should be possible.