Search Results

Abstract Diesel engines include systems for cooling, lubrication, and fuel injection and contain a variety of components. A malfunction in any of the engine systems or the presence of any faulty element influences engine performance and deteriorates its components. This research is concerned with the untimely appearance of vital cracks in the liners of a turbocharged heavy-duty Diesel engine. To find the root causes for premature failure, rigorous examinations through visual observations, material characterization, and metallographic investigations are performed. These include Scanning Electron Microscope (SEM) and Energy-Dispersive Spectroscopy (EDS), fracture mechanics analysis, and performance examination, which are also followed by Finite Element Moldings. To find the proper remedy to resolve the problem, drawing a precise and reliable picture of the engine’s operating conditions is required.

Abstract The frequency of video-capturing collision events from surveillance systems are increasing in reconstruction analyses. The video that has been provided to the investigator may not always include a clear perspective of the relevant area of interest. For example, surveillance video of an incident may have captured a pre- or post-incident perspective that, while failing to capture the precise moment when the pedestrian was struck by a vehicle, still contains valuable information that can be used to assist in reconstructing the incident. When surveillance video is received, a quick and efficient technique to place the subject object or objects into a three-dimensional environment with a known rate of error would add value to the investigation.

Abstract The causes of failure due to cracking in the resistance spot welding of the advanced high-strength steels dual-phase 590 (DP590) were investigated using scanning electron microscopy (SEM), optical microscopy, and the tensile-shear test. The results showed that by increasing the current amount, the formation of the melting zone occurred in the heat-affected zone, leading to the cracking in this area, reducing the tensile strength and decreasing the mechanical properties; the initiation and growth of cracking and failure in this region also happened. In the heat-affected zone, by increasing the current amount with the softening phenomenon, the recrystallized coarse grains also occurred, eventually resulting in the loss of mechanical properties. The results of the tensile-shear test also indicated that by increasing the current up to 12 kA, the strength was raised, but the ductility was reduced.

Abstract This study implements Flame Image Velocimetry (FIV), a diagnostic technique based on post-processing of high-speed soot luminosity images, to show the in-flame flow field development impacted by after-injection in a single-cylinder, small-bore optical diesel engine. Two after-injection cases with different dwell times between the main injection and after-injection, namely, close-coupled and long-dwell, as well as a main-injection-only case are compared regarding flow fields, flow vector magnitude, and turbulence intensity distribution. For each case, high-speed soot luminosity movies from 100 individual combustion cycles are recorded at a high frame rate of 45 kHz for FIV processing. The Reynolds decomposition using a spatial filtering method is applied to the obtained flow vectors so that bulk flow structures and turbulence intensity distributions can be discussed.

Abstract Although tread block deformation analysis is important, the deformation measurement is difficult because fast-rotating tires maintain a continuous contact with the road surface. Furthermore, capturing small displacements near the edge of tread blocks using a high-speed camera is difficult because of the particularly limited resolution. Additionally, the tread blocks being significantly deformed at the edge and susceptible to wear powder, the state change of the feature points, is highly probable. To overcome these problems, a system that obtains high-resolution images and measures the deformation of a fast-rotating body (tire) is proposed herein. The developed system captures the deformation behavior through intermittent imaging. To further measure the strain distribution, fine tracking markers are drawn on the tread block using a laser processing machine. The displacement of the marker is calculated using the particle mask correlation method.

Abstract An ongoing challenge with Gasoline engines is achieving rapid activation of the three-way catalyst during cold starts in order to minimize pollutant emissions. Retarded combustion is an effective way in achieving rapid light-up of the three-way catalyst and can be facilitated by stratified charge using late fuel injection. This, however, provides insufficient time for fuel entrainment with air, resulting in locally fuel-rich diffusion combustion. Employing a split injection strategy can help tackle these issues. The effects of a split injection strategy, using a high-pressure Solenoid injector, on the in-cylinder charge formation are investigated in the current study. The studies are performed inside an optical Gasoline engine using high-speed particle image velocimetry (PIV) in the central tumble and Omega tumble planes, by means of a high-speed laser and camera operating at a repetition rate of 10 kHz.

Abstract This research involves the study of the different properties of aluminum alloy (AA) 2024 in the presence of carbon nanotubes (CNTs) and Silicon (Si) nanoparticles. Structural morphology, elemental composition, mechanical properties (density, tensile strength, elongation, and hardness), and tribological properties (wear rate and coefficient of friction) of AA 2024 in the presence of CNTs, Si, and its combinations at various proportions were evaluated using a Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Analyzer (EDX), Universal Testing Machine (UTM), Model HMV-2T Vickers hardness test machine, and pin-on-disk friction-and-wear test rig. The Hybrid Metal Matrix Composite (HMMC) material is prepared by a two-stage stir casting method. It was found that the density of the AA 2024 + 4%CNT + 2%Si is 2.22 g/cm3, ultimate tensile strength is 308 N/mm2, elongation is 15.5%, and Vickers hardness is 187.5 Vickers Hardness Number (VHN).

Abstract Reliable detection of obstacles around an autonomous vehicle is essential to avoid potential collision and ensure safe driving. However, a vast majority of existing systems are mainly focused on detecting large obstacles such as vehicles, pedestrians, and so on. Detection of small obstacles such as road debris, which pose a serious potential threat are often overlooked. In this article, a novel stereo vision-based road debris detection algorithm is proposed that detects debris on the road surfaces and estimates their height accurately. Moreover, a collision warning system that could warn the driver of an imminent crash by using 3D information of detected debris has been studied.

Abstract High fuel injection pressure systems for Gasoline Direct Injection (GDI) engines have become widely used in passenger car engines to reduce emissions of particulates and pollutant gases. Current commercial systems operate at pressures of up to 450 bar, but several studies have examined the use of injection pressures above 600 bar, and some have even used pressures around 1500 bar. These works revealed that high injection pressures have numerous benefits including reduced particulate emissions, but there is still a need for more data on the possible benefits of injection pressures above 1000 bar. This article presents spray and engine data from a comprehensive study using several measurement techniques in a spray chamber and optical and metal engines. Shadowgraph imaging and Phase Doppler Interferometry (PDI) were used in a constant volume chamber to interpret spray behavior. Particle Image Velocimetry (PIV) was used to capture near-nozzle air entrainment.

Abstract Today, as the spread of vehicles equipped with autonomous driving functions increases, accidents caused by autonomous vehicles are also increasing. Therefore, issues regarding safety and reliability of autonomous vehicles are emerging. Various studies have been conducted to secure the safety and reliability of autonomous vehicles, and the application of the International Organization for Standardization (ISO) 26262 standard for safety and reliability improvement and the importance of verifying the safety of autonomous vehicles are increasing. Recently, Mobileye proposed an RSS model called Responsibility Sensitive Safety, which is a mathematical model that presents the standardization of safety guarantees of the minimum requirements that all autonomous vehicles must meet. In this article, the RSS model that ensures safety and reliability was derived to be suitable for variable focus function cameras that can cover the cognitive regions of radar and lidar with a single camera.

Abstract In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing, and training a neural network, following which the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on the go based on the predicted steering angle and other parameters such as the actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed the computational efficiency of the pipeline and evaluated the robustness of the trained models through exhaustive experimentation during the deployment phase.

Abstract A major reduction of greenhouse gas emissions, as well as other toxic emissions, is required to reduce the environmental impact of transportation systems. Renewable fuels, in combination with new internal combustion processes, such as reactivity controlled compression ignition (RCCI), are promising measures to enable this reduction. By combining two fuels with different reactivity, RCCI offers high efficiency and low emissions through homogeneous low-temperature combustion. However, a two-fuel RCCI approach leads to an increased number of adjustable operation parameters, such as injection timing. Optimizing these operation parameters to ensure homogenous combustion is challenging. To that end, optical methods provide temporally and spatially resolved information on mixture formation and combustion to analyze the homogeneity of the process. However, established methods, such as OH* imaging, cannot differentiate between multiple fuels.

Abstract With the sustainable development of the social economy and the continuous maturity of science and technology, urban rail transit has developed rapidly. It solved the problems of urban road load and people’s travel and brought about the problem of rail transit passenger congestion. The image detection algorithm for rail transit congestion is established based on the convolutional neural networks (CNN) structure to realize intelligent video image monitoring. The CNN structure is optimized through the backpropagation (BP) algorithm so that the model can detect and analyze the riding environment through the monitoring camera and extract the relevant motion characteristics of passengers from the image. Furthermore, the crowding situation of the riding environment is analyzed to warn the rail transit operators. In practical application, the detection accuracy of the algorithm reached 91.73%, and the image processing speed met the second-level processing.

Abstract In view of the fact that the current positioning methods of railway fasteners are easily affected by illumination intensity, bright spots, and shadows, a positioning method with relative grayscale invariance is proposed. The median filter is used to remove the noise in order to reduce the adverse effects on the subsequent processing results, and the baffle seat edge features are enhanced by improved Census transform. The mean-shift clustering algorithm is used to classify the edges to weaken the interference by short lines. Finally, the Hough transform is used to quickly extract the linear feature of the baffle seat edge and achieve the exact position of the fastener with the prior knowledge. Experimental results show that the proposed method can accurately locate and have good adaptability under different illumination conditions, and the position accuracy is increased by 4.3% and 8%, respectively, in sunny and rainy days.

Abstract Vaporizing foil actuator welding (VFAW) created nominally solid-state spot welds between high-strength DP980 steel and 6022 T4 aluminum. The effects of varying the impact velocity and angle between the Al flyer and target steel sheets on the structure and properties of the joints were evaluated using photonic Doppler velocimetry (PDV), scanning electron microscopy (SEM), fractography, and energy-dispersive spectroscopy (EDS) analysis. The incident angle and velocity of the flyer plate were quantified using PDV, and their relations to the structure and properties of the joint were assessed with microscopy and strength testing. Impact velocity and average impact angle increase with the increasing standoff. Lower impact angles and higher impact velocities promoted interfacial failure due to increased melting, higher intermetallic thickness, and lower wave amplitude and wavelength.

Abstract Weather conditions such as rain, fog, snow, and dust can adversely impact sensing and perception, limit operational envelopes, and compromise the safety and reliability of advanced driver-assistance systems and autonomous vehicles. Physical testing of an autonomous system in a weather laboratory and on-road is costly and slow and exposes the system to only a limited set of weather conditions. To overcome the limitations of physical testing, a physics-based simulation workflow was developed by coupling computational fluid dynamics (CFD) with optical simulations of camera and lidar sensors. The computational data of various weather conditions can be rapidly generated by CFD and used to assess the impact of weather conditions on the sensors and perception algorithms.

Abstract The latest-generation spark-ignition (SI) engines implement high-tumble flow design to achieve unprecedented high brake thermal efficiency of over 40%, which will continue to play an important role in both conventional and future electrified vehicles. To maximize the potential of high-tumble SI engines, there is a clear need for in-cylinder flow and flame analysis conducted timely in a realistic environment. For the first time, this study meets this need by performing innovative endoscopic imaging of flow fields and flame inside the cylinder of a selected production engine using a particle image velocimetry (PIV) laser and high-speed camera system operated at 35 kHz. Through this time-resolved, two-dimensional measurement of the realistic in-cylinder phenomenon, many new findings have been achieved.

Abstract The millimeter-wave radar has good weather robustness, but currently lacks performance in object classification. With the advent of imaging radar, three-dimensional (3D) point clouds of objects can be obtained. Based on 3D radar point clouds, an support vector machine (SVM algorithm using 3D features is proposed to solve poor radar classification performance. First, a new 29-feature vector is proposed from many perspectives, such as shape features, statistical features, and other features. Then the SVM classifier with four different kernel functions and other machine learning methods are used to achieve multi-objective classification. Finally, experiments are carried out on three types of datasets collected by ourselves, and the results show that the algorithm achieves a 95.1% classification accuracy, which is 15.7% higher than the traditional 2D radar point cloud.

Abstract Super duplex stainless steel (SDSS) is a type of stainless steel made of chromium (Cr), nickel (Ni), and iron (Fe). In the present work, a 1.6 mm wide thin sheet of SDSS is joined using gas tungsten arc welding (GTAW). The ideal parameter for a bead-on-plate trial is found, and 0.216 kJ/mm of heat input is used for welding. As an outcome of the welding heating cycle and subsequent cooling, a microstructural study revealed coarse microstructure in the heat-affected zone and weld zone. The corrosion rate for welded joints is 9.3% higher than the base metal rate. Following the corrosion test, scanning electron microscope (SEM) analysis revealed that the welded joint’s oxide development generated a larger corrosive attack on the weld surface than the base metal surface. The percentages of chromium (12.5%) and molybdenum (24%) in the welded joints are less than those in the base metal of SDSS, as per energy dispersive X-ray (EDX) analysis.

Abstract This article primarily focuses on studying and analyzing the effect of machining parameters, viz., pulse on time (TON), pulse off time (TOFF), and pulse current (Ip) on machining performance in terms of surface roughness (Ra) and material removal rate (MRR) during electrical discharge machining (EDM) of alloy tool steel (SKD11 steel). The traditional trial-and-error methods used to derive empirical relationship and optimize the process is time consuming and results in reduced productivity, high rejection, and cost. The response surface methodology (RSM) approach of design of experiment technique was applied for designing the experiments. The influences of EDM parameters on Ra and MRR were investigated using different graphs. The mathematical model equations for Ra and MRR were generated. The optimum parametric combinations for smaller Ra (highest surface finish) and highest MRR were found, and the optimized values of Ra and MRR were obtained.