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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.

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).

Simulations used to estimate carbon dioxide (CO2) emissions and fuel consumption of medium- and heavy-duty vehicles over prescribed drive cycles often employ engine fuel maps consisting of engine measurements at numerous steady-state operating conditions. However, simulating the engine in this way has limitations as engine controls become more complex, particularly when attempting to use steady-state measurements to represent transient operation. This paper explores an alternative approach to vehicle simulation that uses a “cycle average” engine map rather than a steady state engine fuel map. The map contains engine CO2 values measured on an engine dynamometer on cycles derived from vehicle drive cycles for a range of generic vehicles. A similar cycle average mapping approach is developed for a powertrain (engine and transmission) in order to show the specific CO2 improvements due to powertrain optimization that would not be recognized in other approaches.

Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward-looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements.

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.

Over the last two decades, global emission regulations have become more stringent and have required the use of more advanced fuel injection systems. This includes the use of tighter tolerances, more rapid injections and internal components actuated by weaker injection forces. Unfortunately, these design features make the entire system more susceptible to fuel contaminants. Over the last six years, the composition of these contaminants has evolved from hard insoluble debris, such as dust and rocks, to soluble chemical contaminants. Recent research by the diesel engine manufacturers, fuel injection equipment suppliers and the fuel and fuel additive industry has discovered a major source of the soluble chemical contaminant that leads to injector deposits to be derived from cost effective and commonly used additives used to protect against pipeline corrosion.

This study is presenting a comparative spray study of modern large bore medium speed diesel engine common rail injectors. One subject of paper is to focus on nozzles with same nominal flow rate, but different machining. The other subject is penetration velocity measurements, which have a new approach when trying to understand the early phase of transient spray. A new method to use velocimetry for spray tip penetration measurements is here introduced. The length where spray penetration velocity is changed is found. This length seems to have clear connection to volume fraction of droplets at gas. These measurements also give a tool to divide the development of spray into acceleration region and deceleration region, which is one approach to spray penetration. The measurements were performed with backlight imaging in pressurized injection test rig at non-evaporative conditions. Gas density and injection pressure were matched to normal diesel engine operational conditions.

Non-evaporating diesel sprays have been simulated utilizing the ETAB and the WAVE atomization and breakup models and have been compared with experimental data. The experimental penetrations and widths were determined from back-lit spray images and the droplet sizes have been measured by means of a Malvern particle sizer. The model evaluation criteria include the spray penetration, the spray width and the local droplet size. The comparisons have been performed for variations of the injection pressure, the gas density and the fuel viscosity. The fuel nozzle exit velocities used in the simulations have been computed with a special code that considers the effect of in-nozzle cavitation. The simulations showed good overall agreement with experimental data. However, the capabilities of the models to predict the droplet size for different fuels could be improved.

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.

Wideband Acoustical Holography (WBH), which is a monopole-based, equivalent source procedure (J. Hald, “Wideband Acoustical Holography,” INTER-NOISE 2014), has proven to offer accurate noise source visualization results in experiments with a simple noise source: e.g., a loudspeaker (T. Shi, Y. Liu, J.S. Bolton, “The Use of Wideband Holography for Noise Source Visualization”, NOISE-CON 2016). From a previous study, it was found that the advantage of this procedure is the ability to optimize the solution in the case of an under-determined system: i.e., when the number of measurements is much smaller than the number of parameters that must be estimated in the model. In the present work, a diesel engine noise source was measured by using one set of measurements from a thirty-five channel combo-array placed in front of the engine.

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.

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.

Connected vehicles have the potential to transform the way we commute and travel in a multitude of ways. Vehicles will cooperate and coordinate with each other to solve problems appropriate for the environment in which they are operating. In this paper, we focus on the development of test equipment that includes the infrastructure and vehicles to measure and record all of the information necessary to quantify the performance of cooperative driving algorithms in realistic scenarios. The system allows tests to include real vehicles on the track and virtual vehicles in a digital twin. Real and virtual vehicles interact through the road-side units and test facility network, allowing each test vehicle to receive messages from virtual vehicles as well as the infrastructure. Messages transmitted from the test vehicles are received in the digital twin, allowing the real vehicle to interact with virtual vehicles.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

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.

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.

This paper presents a methodology that optimizes the periods of engine operation on a selected route for a Plug-in Hybrid Electric Vehicle (PHEV) or Hybrid Electric Vehicle (HEV) using Connected Vehicle data to minimize energy consumption. The study was conducted using a Reduced-Order Powertrain model of second-generation Chevrolet Volt. The method utilizes the Backward Induction Dynamic Programming algorithm to come up with an optimal control mode matrix of engine operation along the selected route for various battery states of charge. The objective of this method is to make use of Vehicle Connectivity to minimize the energy utilization of an HEV by using the speed and elevation profile of a selected route transmitted to the vehicle via V2X communication systems.

Based on RADAR and LiDAR measurements of deer with RADAR and LiDAR in the Spring and Fall of 2014 [1], we report the best fit statistical models. The statistical models are each based on time-constrained measurement windows, termed test-points. Details of the collection method were presented at the SAE World Congress in 2015. Evaluation of the fitness of various statistical models to the measured data show that the LiDAR intensity of reflections from deer are best estimated by the extreme value distribution, while the RCS is best estimated by the log-normal distribution. The value of the normalized intensity of the LiDAR ranges from 0.3 to 1.0, with an expected value near 0.7. The radar cross-section (RCS) varies from -40 to +10 dBsm, with an expected value near -14 dBsm.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.