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Piston temperature plays a major role in determining details of fuel spray vaporization, fuel film deposition and the resulting combustion in direct-injection engines. Due to different heat transfer properties that occur in optical and all-metal engines, it becomes an inevitable requirement to verify the piston temperatures in both engine configurations before carrying out optical engine studies. A novel Spot Infrared-based Temperature (SIR-T) technique was developed to measure the piston window temperature in an optical engine. Chromium spots of 200 nm thickness were vacuum-arc deposited at different locations on a sapphire window. An infrared (IR) camera was used to record the intensity of radiation emitted by the deposited spots. From a set of calibration experiments, a relation was established between the IR camera measurements of these spots and the surface temperature measured by a thermocouple.

A direct-injection and spark-ignition single-cylinder engine with optical access to the cylinder was used for the combustion visualization study. Gasoline and ethanol-gasoline blended fuels were used in this investigation. Experiments were conducted to investigate the effects of fuel injection pressure, injection timing and the number of injections on the in-cylinder combustion process. Two types of direct fuel injectors were used; (i) high-pressure production injector with fuel pressures of 5 and 10 MPa, and (ii) low-pressure production-intent injector with fuel pressure of 3 MPa. Experiments were performed at 1500 rpm engine speed with partial load. In-cylinder pressure signals were recorded for the combustion analyses and synchronized with the high-speed combustion imaging recording. Visualization results show that the flame growth is faster with the increment of fuel injection pressure.

Combustion studies were completed using an International VT275-based, optical DI Diesel engine fueled with Diesel fuel, a Canola-derived FAMES biodiesel, as well as with a blend of the Canola-derived biodiesel and a cetane-reducing, oxygenated fuel, Di-Butyl Succinate. Three engine operating conditions were tested to examine the combustion of the fuels across a range of loads and combustion schemes. Pressure data and instantaneous images were recorded using a high-speed visible imaging, infrared imaging, and high-speed OH imaging techniques. The recorded images were post processed to analyze different metrics, such as projected areas of in-cylinder soot, OH, and combustion volumes. A substantially reduced in-cylinder area of soot formation is observed for the Canola-DBS blended fuel with a slight reduction from the pure FAMES biodiesel compared to pump Diesel fuel.

Since the 1960's, automotive seats have been designed and evaluated with tools and procedures described in the SAE Recommended Practice J826. The SAE J826 design template and testing manikin each have a torso with a flat lower back shape and with a single joint at the H-point. The JOHN models provide a more anatomically detailed representation of human shape and movement. The articulations of the JOHN torso (pelvic, lumbar, and thoracic) segments are coupled so that their relative positions are determined by a single parameter related to spinal curvature. This paper describes the development and use of the JOHN biomechanical models for seating design.

Driver and passenger comfort, as related to automotive seats, is a growing issue in the automotive industry. As this trend continues, automotive seat designers and developers are generating a greater need for more anthropometrically accurate tools to aid them in their work. One tool being developed is the JOHN software program that utilizes three-dimensional solid objects to represent humans in seated postures. Contours have been developed to represent the outside skin surfaces of three different body types in a variety of postures in the sagittal plane. These body types include: the small female, the average male, and the large male.

LiDAR sensors are common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of diverse training data, partly due to sensors’ high cost and rapid development cycles. Public datasets (e.g. KITTI) offer poor coverage of edge cases, whereas these samples are essential for safer self-driving. We address the unmet need for abundant, high-quality LiDAR data with the development of a synthetic LiDAR point cloud generation tool and validate this tool’s performance using the KITTI-trained PIXOR object detection model. The tool uses a single camera raycasting process and filtering techniques to generate segmented and annotated class specific datasets.

This paper presents results from experiments performed in an axisymmetric water analog model of a four-stroke IC engine using the optical velocimetry technique LIPA (Laser Induced Photochemical Anemometry). The investigation can be described as a fundamental scientific inquiry into the fluid dynamics encountered during engine operation, with the long term goal of increasing performance. An application of LIPA to a fluid dynamics problem delivers two-dimensional fields of velocity vectors which are projections of the full three-dimensional vectors in single measurement steps. From an evaluation of a velocity field vorticity information can be obtained readily. Velocity fields and vorticity distributions are, in this study, the basis for the evaluation of seven parametric quantities. Some of these may become tools that give engineers ‘rule of thumb’ indications of the mixing that is occurring.