Search Results

Abstract The ASTM D130 was first issued in 1922 as a tentative standard for the detection of corrosive sulfur in gasoline. A clean copper strip was immersed in a sample of gasoline for three hours at 50°C with any corrosion or discoloration taken to indicate the presence of corrosive sulfur. Since that time, the method has undergone many revisions and has been applied to many petroleum products. Today, the ASTM D130 standard is the leading method used to determine the corrosiveness of various fuels, lubricants, and other hydrocarbon-based solutions to copper. The end-of-test strips are ranked using the ASTM Copper Strip Corrosion Standard Adjunct, a colored reproduction of copper strips characteristic of various degrees of sulfur-induced tarnish and corrosion, first introduced in 1954. This pragmatic approach to assessing potential corrosion concerns with copper hardware has served various industries well for a century.

Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).

Abstract Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits.

Abstract Composite ceramic brake discs are made of ceramic material reinforced with carbon fibers and offer exceptional advantages that translate directly into higher vehicle performance. In the case of an electric vehicle, it could increase the range of the vehicle, and in the case of conventional internal combustion engine vehicles, it means lower fuel consumption (and consequently lower CO2 emissions). These discs are typically characterized by complex internal geometries, further complicated by the presence of drilling holes on both friction surfaces. To estimate the aerothermal performance of these discs, and for the thermal management of the vehicle, a reliable model for predicting the air flowing across the disc channels is needed. In this study, a real carbon-ceramic brake disc with drilling holes was investigated in a dedicated test rig simulating the wheel corner flow conditions experimentally using the particle image velocimetry technique and numerically.

Abstract Autonomous Racing is gaining increasing publicity as an attractive showcase of state-of-the-art technologies and the enhanced algorithms used for autonomous driving. The Indy Autonomous Challenge (IAC) tackled autonomous high-speed wheel-to-wheel racing at the famous Indianapolis Motor Speedway (IMS) in October 2021. To solve this problem, advanced autonomous driving algorithms were developed by each team. In this article, we display a multi-sensor localization approach developed for usage in the IAC. To decouple the lateral and longitudinal position of the ego vehicle, a trackbound coordinate system is used that can be transformed to conventional Cartesian coordinates. The longitudinal motion of the car is tracked via a modified version of the OpenVSLAM that outputs the progress of the already driven distance.

Abstract With the introduction of advanced lightweight materials with complex microstructures and behaviors, more focus is put on the accurate determination of their forming limits, and that can only be possible through experiments as the conventional theoretical models for the forming limit curve (FLC) prediction fail to perform. Despite that, CAE engineers, designers, and toolmakers still rely heavily on theoretical models due to the steep costs associated with formability testing, including mechanical setup, a large number of tests, and the cost of a stereo digital image correlation (DIC) system. The international standard ISO 12004-2:2021 recommends using a stereo DIC system for formability testing since two-dimensional (2D) DIC systems are considered incapable of producing reliable strains due to errors associated with out-of-plane motion and deformation.

Abstract Automotive interior coatings for flexible and rigid substrates represent an important segment within automotive coating space. These coatings are used to protect plastic substrates from mechanical and chemical damage, in addition to providing colour and design aesthetics. These coatings are expected to resist aggressive chemicals, fluids, and stains while maintaining their long-term physical appearance and mechanical integrity. Designing such coatings, therefore, poses significant challenges to the formulators in effectively balancing these properties. Among many factors affecting coating properties, the cross-link density (XLD) and solubility parameter (δ) of coatings are the most predominant factors.

Abstract A modular, stationary IC engine blow rig for differential and integral flow field measurements using particle image velocimetry (PIV) has been developed. Unlike conventional PIV blow rigs, the given design is capable of operating under near-reality charge air conditions, that is, highly pressurized, hot intake air supply at high flow rates. Its conceptual flexibility as well as peripheral infrastructure allow for comprehensive and wide-ranging flow field analysis. Because of a modular architecture, it is neither confined to a specific cylinder head design nor limited solely to the application of PIV for differential flow field analysis. It also already accounts for direct inlet flow determination through an additional PIV access point upstream of the cylinder head. The inlet and outlet ducts have been designed with regular shapes and smooth walls, such that a digital twin-type CFD model of the blow rig is conveniently feasible.

Abstract Effective circumstance perception technology is the prerequisite for the successful application of autonomous driving, especially the detection technology of traffic objects that affects other tasks such as driving decisions and motion execution in autonomous vehicles. However, recent studies show that a single sensor cannot perceive the surrounding environment stably and effectively in complex circumstances. In the article, we propose a multi-scale feature fusion framework that exploits a dual backbone network to extract camera and radar feature maps and performs feature fusion on three different feature scales using a new fusion module. In addition, we introduce a new generation mechanism of radar projection images and relabel the nuScenes dataset since there is no other suitable autonomous driving dataset for model training and testing.

Abstract Three-dimensional patterns representing crosshatched plateau-honed cylinder bores based on two-dimensional Fast Fourier Transform (FFT) of measured surfaces were generated and used to calculate pressure flow, shear-driven flow, and shear stress factors. Later, the flow and shear stress factors obtained by numerical simulations for various surface patterns were used to calculate lubricant film thickness and friction force between piston ring and cylinder bore contact in typical diesel engine conditions using a mixed lubrication model. The effects of various crosshatch honing angles, such as 30°, 45°, and 60°, and texture heights on engine friction losses, wear, and oil consumption were discussed in detail. It is observed from numerical results that lower lubricant film thickness values are generated with higher honing angles, particularly in mixed lubrication regime where lubricant film thickness is close to the roughness level, mainly due to lower resistance to pressure flow.

Abstract The following work presents a new concept for optical accessibility of a single-cylinder medium-speed large-bore marine engine from the concept development to the implementation and feasibility investigation in a test bench observing the flame chemiluminescence of dual-fuel (DF) combustion. The design’s feasibility is verified using conjugated heat transfer (CHT) and finite element method (FEM) simulation during the whole design process presented herein. Assumptions made for the simulation, e.g., of the mount between the optical component and the steel engine parts, are evaluated in pretesting setups presented and described as follows. The optical access is made to withstand steady-state full-load operating conditions and is proofed so. The optical access is designed for an engine with a bore of 350 mm and a stroke of 440 mm.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

Abstract Considering the biodiesel composition, blend percentage, and temperature as input variables in the models to predict biodiesel-diesel blends’ properties is imperative. However, there are no models available in the literature to predict the properties of biodiesel-diesel blends that consider all these variables. The accuracy of spray and combustion models for diesel engines depends on the accuracy at which the fuel properties are estimated. Thus, straightforward approaches to accurately predict the properties of biodiesel-diesel blends are required. A novel reference property-based approach is proposed in the present work to predict the biodiesel-diesel blends’ properties to address this research gap. Models available in the literature correlating the properties of interest to fuel temperature were modified by including a reference property measured at 293 K.

Abstract In this study, a parametric thoracic spine (T-spine) model was developed to account for morphological variations among the adult population. A total of 84 CT scans were collected, and the subjects were evenly distributed among age groups and both sexes. CT segmentation, landmarking, and mesh morphing were performed to map a template mesh onto the T-spine vertebrae for each sampled subject. Generalized procrustes analysis (GPA), principal component analysis (PCA), and linear regression analysis were then performed to investigate the morphological variations and develop prediction models. A total of 13 statistical models, including 12 T-spine vertebrae and a spinal curvature model, were combined to predict a full T-spine 3D geometry with any combination of age, sex, stature, and body mass index (BMI). A leave-one-out root mean square error (RMSE) analysis was conducted for each node of the mesh predicted by the statistical model for every T-spine vertebra.

Abstract Many consider hydrogen to be the automobile fuel of the future. Indeed, it has numerous characteristics that makes it very attractive. Hydrogen has a much higher energy density than gasoline, can be produced from water, and its only emission is water. However, there are numerous challenges associated with hydrogen. In particular, the production of hydrogen is a key issue. Currently, most hydrogen is developed from methane, resulting in hydrogen having a carbon footprint. New investments into electrolysis from renewable energy sources is showing promise as an alternative for generating hydrogen. Further, the distribution of hydrogen poses many problems, requiring substantial infrastructure to support a hydrogen economy. Additionally, hydrogen storage is a key issue since most conventional storage mechanisms are overly bulky. If these three issues can be addressed, hydrogen is posed for being a key fuel as the world tries to move away from fossil fuels.

Abstract This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation.

Abstract Recently, lean manufacturing (LM) practices are being combined with tools and techniques that belong to other areas of knowledge such as risk management (RM). Value stream mapping (VSM) is a well-known tool in showing the value, the value stream, and the flow, which represents the three lean principles. VSM and RM, when used in tandem with one another, are more advantageous in covering VSM issues such as the variability of production processes. In this article, a conceptual model that integrates the two is shown and explained. The model helps to generate scenarios of current state map (CSM) and future state map (FSM) in a dynamic way by identifying current and potential risks. These risks might happen in the future, bringing with it negative ramifications including not reaching the main objectives within the defined time. The model has been tested in a coffee production company belonging to health and food sector.

Abstract In this article, a methodology is presented to assess the influence of time-averaged deformations on a production car of the 2018 A-class due to wind load. Exemplary, the deformations of the front and rear bumper are investigated. The aerodynamic development of vehicles at Mercedes-Benz is divided into several phases. When comparing, force coefficients differences can be observed between these distinct hardware stages as well as when comparing steady-state simulations to wind tunnel measurements. In early phases when prototype vehicles are not yet available, so-called aero foam models are used. These are well-defined full-sized vehicle models as the outer skin is milled from Polyurethane. Important aerodynamic characteristics such as an engine compartment with a cooling module, deflecting axles with rotatable wheels, and underbody covers are represented.

Abstract The fuel injection pressures used in gasoline direct injection (GDI) engines have increased in recent years to improve fuel efficiency and reduce emissions. Current GDI engines use injection pressures of up to 350 bar, and there is evidence that even higher fuel injection pressures could yield further improvements in atomization. Higher injection pressures could also improve mixture formation by increasing the spray velocity; however, the research with higher injection pressures over 1000 bar is limited due to a limit of mechanical components. This manuscript summarizes experimental investigations into the effect of injection pressure, injection mass, and nozzle shape on spray-induced air motion with ultrahigh injection pressure over 1000 bar.