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Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e., the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry.

There has been a great effort expended in identifying causes of Hydro-Carbon (HC) and Particulate Matter (PM) emissions resulting from poor spray preparation, leading to characterization of fueling behavior near nozzle. It has been observed that large droplet size is a primary contributor to HC and PM emission. Imaging technologies have been developed to understand the break-up and consistency of fuel spray. However, there appears to be a lack of studies of the spray characteristics at the End of Injection (EOI), near nozzle, in particular, the effect that tip deposits have on the EOI characteristics. Injector tip deposits are of interest due to their effect on not only fuel spray characteristics, but also their unintended effect on engine out emissions. Using a novel imaging technique to extract near nozzle fuel characteristics at EOI, the impact of tip deposits on Gasoline Direct Injection (GDI) fuel injectors at the EOI is being examined in this work.

The liquid fuel spray impingement onto surfaces occurs in both spark ignited and compression ignited engines. It causes a fundamental issue affecting the preparation of air-fuel mixture prior to the combustion, further, affecting engine performance and emissions. To better understand the underlying mechanism of spray interaction with a solid surface, the physics of a single droplet impact on a heated surface was experimentally investigated. The experimental work was conducted at four surface temperatures where a single diesel droplet was injected from a precision syringe pump with a specific droplet diameter and impact velocity. A high-speed camera was used to visualize the droplet impingement process. Images from the selected test condition (We = 52 to 925, Re = 789 to 3330 based on initial droplet impingement parameters) were analyzed to qualify the impinging outcomes and quantify the post-impingement characteristics.

Increasing the strength of materials is effective in reducing weight and boosting structural part performance, but there are cases in where the residual strain generated during the process of manufacturing of high-strength materials results in a decline of durability. It is therefore important to understand how the residual strain in a manufactured component changes due to processing conditions. In the case of a connecting rod, because the strain load on the connecting rod rib sections is high, it is necessary to clearly understand the distribution of strain in the ribs. However, because residual strain is generally measured by using X-ray diffractometers or strain gauges, measurements are limited to the surface layer of the parts. Neutron beams, however, have a higher penetration depth than X-rays, allowing for strain measurement in the bulk material.

Tumble motion plays a significant role in modern spark-ignition engines in that it promotes mixing of air/fuel for homogeneous combustion and increases the flame propagation speed for higher thermal efficiency and lower combustion variability. Cycle-by-cycle variations in the flow near the spark plug introduce variability to the initial flame kernel development, stretching, and convection, and this variability is carried over to the entire combustion process. The design of current direct-injection spark-ignition engines aims to have a tumble flow in the vicinity of the spark plug at the time of ignition. This work investigates how the flow condition changes in the vicinity of the spark plug throughout the late compression stroke via high-speed imaging of a long ignition discharge arc channel and its stretching, and via flow field measurement by particle imaging velocimetry.

Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component’s behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles.

Extensive studies have addressed diesel sprays under non-vaporizing, vaporizing and combusting conditions respectively, but further insights into the mechanism by which combustion alters the macroscopic characteristics including the spray penetration and the shape of the spray under diesel engine conditions are needed. Contradictory observations are reported in the literature regarding the combusting diesel spray penetration compared to the inert conditions, and it is an objective of this study to provide further insights and analyses on the combusting spray characteristics by expanding the range of operating parameters. Parameters varied in the studies are charge gas conditions including oxygen levels of 0 %, 15%, 19%, charge densities of 22.8 & 34.8 kg/m3, and charge temperatures of 800, 900 & 1050 K for injection pressures of 1200, 1500, and 1800 bar with a single-hole injector with a nozzle diameter of 100 μm.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

High pressure diesel sprays were visualized under vaporizing and combusting conditions in a constant-volume combustion vessel. Near-simultaneous visualization of vapor and liquid phase fuel distribution were acquired using a hybrid shadowgraph/Mie-scattering imaging setup. This imaging technique used two pulsed LED's operating in an alternative manner to provide proper light sources for both shadowgraph and Mie scattering. In addition, combustion cases under the same ambient conditions were visualized through high-speed combustion luminosity measurement. Two single-hole diesel injectors with same nozzle diameters (100μm) but different k-factors (k0 and k1.5) were tested in this study. Detailed analysis based on spray penetration rate curves, rate of injection measurements, combustion indicators and 1D model comparison have been performed.

This paper reports an experimental and numerical investigation on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel spray under engine-like conditions. The high pressure diesel spray was investigated in an optically-accessible constant volume combustion vessel for studying the influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio). Measurements were carried out by a high-speed imaging system capable of acquiring Mie-scattering and schlieren in a nearly simultaneous fashion mode using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies were performed at three injection pressures (70, 120, and 180 MPa), 23.9 kg/m3 ambient gas density, and 900 K gas temperature in the vessel.

Increasing fuel injection pressure has enabled reduction of diesel emissions while retaining the advantage of the high thermal efficiency of diesel engines. With production diesel injectors operating in the range from 300 to 2400 bar, there is interest in injection pressures of 3000 bar and higher for further emissions reduction and fuel efficiency improvements. Fundamental understanding of diesel spray characteristics including very early injection and non-vaporizing spray penetration is essential to improve model development and facilitate the integration of advanced injection systems with elevated injection pressure into future diesel engines. Studies were conducted in an optically accessible constant volume combustion vessel under non-vaporizing conditions. Two advanced high pressure multi-hole injectors were used with different hole diameters, number of holes, and flow rates, with only one plume of each injector being imaged to enable high frame rate imaging.

The paper describes an experimental activity on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel at engine-like conditions. The influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio) has been studied by spraying fuel in an optically-accessible constant-volume combustion vessel. A high-speed imaging system, capable of acquiring Mie-scattering and Schlieren images in a near simultaneous fashion mode along the same line of sight, has been developed at the Michigan Technological University using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies have been performed at three injection pressures (70, 120 and 180 MPa), 23.9 kg/m3 ambient gas density and 900 K gas temperature in the vessel.

In-cylinder blending of gasoline and diesel to achieve Reactivity Controlled Compression Ignition (RCCI) has been shown to reduce NOX and particulate matter (PM) emissions while maintaining or improving brake thermal efficiency as compared to conventional diesel combustion (CDC). The RCCI concept has an advantage over many advanced combustion strategies in that the fuel reactivity can be tailored to the engine speed and load allowing stable low-temperature combustion to be extended over more of the light-duty drive cycle load range. Varying the premixed gasoline fraction changes the fuel reactivity stratification in the cylinder providing further control of combustion phasing and pressure rise rate than the use of EGR alone. This added control over the combustion process has been shown to allow rapid engine operating point exploration without direct modeling guidance.

The projected frontal area of a vehicle has a significant impact on aerodynamic drag, and thus is an important parameter, for vehicle development, benchmarking, and modeling. However, determining vehicle frontal area can be tedious, time consuming, expensive, or inaccurate. Existing methods include analysis of engineering drawings, vehicle projections, 3D scanners, planimeter measurements from photographs, and estimations using vehicle dimensions. Currently accepted approximation methods can be somewhat unreliable. This study focuses on introducing a method to find vehicle frontal area using digital images and subtraction functions via MATLABs' Image Processing Toolbox. In addition to an overview of the method, this paper describes several variables that were examined to optimize and improve the process such as camera position, surface glare, and vehicle shadow effects.

Three high pressure multi-hole GDI injectors, one manufactured by Continental, two manufactured by Bosch, are experimentally characterized under various injection strategies in terms of instantaneous mass flow rate and fuel dispersion. Spray visualization within an optically accessible pressure vessel allows the measurement of the single jet cone angle and penetration length. A portable numerical model for the issuing spray dynamics is developed within the AVL Fire code, exploiting a log-normal distribution for the initial droplets diameter, whose expected value and variance are properly defined as a function of the main physical parameters. Tuning of the entering constants is realized by means of an automatic optimization procedure. An example of application of the spray model within a 3D simulation of the in-cylinder process of a GDI engine is presented. Effects of splitting injection into two successive events are discussed.

Continuous efforts to develop a lightweight alloy suitable for the most demanding applications in automotive industry resulted in a number of advanced aluminum (Al) and magnesium alloys and manufacturing routes. One example of this is the application of 319 Al alloy for production of 3.6L V6 gasoline engine blocks. Aluminum is sand cast around Fe-liner cylinder inserts, prior to undergoing the T7 heat treatment process. One of the critical factors determining the quality of the final product is the type, level, and profile of residual stresses along the Fe liners (or extent of liner distortion) that are always present in a cast component. In this study, neutron diffraction was used to characterize residual stresses along the Al and the Fe liners in the web region of the cast engine block. The strains were measured both in Al and Fe in hoop, radial, and axial orientations. The stresses were subsequently determined using generalized Hooke's law.