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 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 This work presents a generalized methodology for the optimal thermal management of different powertrain devices. The methodology is based on the adoption of an electrically driven pump and on the development of a specifically designed controller algorithm. This is achieved following a Model Predictive Control approach and requires a generalized lumped-parameters model of the thermal exchange between the device walls and the coolant. The methodology is validated at a test rig, with reference to a four-cylinder spark-ignition engine. Results show that the proposed approach allows a reduction in fuel consumption of about 2-3% during the engine warm-up, a decrease in fuel consumption of about 1-2% during fully warmed operation, and an estimated fuel consumption reduction of about 2.5-3% in an NEDC. Finally, the investigation highlights that the proposed approach reduces the risk of after-boiling when the engine is rapidly switched off after a prolonged high-load operation.

Abstract It is widely understood that the thermal state of a light-duty vehicle at the beginning of a trip influences the vehicle performance throughout the drive cycle. Cold starts, or initial states with component temperatures near ambient conditions, are strongly correlated with reduced vehicle performance and energy efficiency and increased emissions. Despite this understanding, there is little literature available that characterizes initial thermal states beyond empirical studies and simplified analyses of dwell times. We introduce a framework that considers vehicle activity patterns, including the previous drive event, duration of the previous dwell event, and relevant ambient conditions occurring during these events. Moreover, the framework allows for technologies to influence the prominence of cold starts and warm starts.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval. Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

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 The 2010 Environmental Protection Agency (EPA) Emission Standard for heavy-duty engines required 0.2 g/bhp-hr over certification cycles (cold and hot Federal Test Procedure [FTP]), and the California Air Resources Board (CARB) standards require 0.02 g/bhp-hr for the same cycles leading to a 90% reduction of overall oxides of nitrogen (NOx) emissions. Similar reductions may be considered by the EPA through its Cleaner Trucks Initiative program. In this article, aftertreatment system components consisting of a diesel oxidation catalyst (DOC); a selective catalytic reduction (SCR) catalyst on a diesel particulate filter (DPF), or SCR-F; a second DOC (DOC2); and a SCR along with two urea injectors have been analyzed, which could be part of an aftertreatment system that can achieve the 0.02 g/bhp-hr standard.

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 Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced air mobility (AAM) applications ranging from cargo delivery to structure inspection to passenger transport, across multiple sectors. In addition to guiding the UAS, autonomy will ensure that they stay safe in a large number of off-nominal situations without requiring the operator to intervene. While the addition of autonomy enables the safety case for the overall operation, there is a question as to how we can assure that the autonomy itself will work as intended. Specifically, we need assurable technical approaches, operational considerations, and a framework to develop, test, maintain, and improve these capabilities. We make the case that many of the key autonomy functions can be realized in the near term with readily assurable, even certifiable, design approaches and assurance methods, combined with risk mitigations and strategically defined concepts of operations.

Abstract Heavy-duty (HD) diesel engines are the primary propulsion systems in use within the transportation sector and are subjected to stringent oxides of nitrogen (NOx) and particulate matter (PM) emission regulations. The objective of this study is to develop a robust calibration technique to optimize HD diesel engine for performance and emissions to meet current and future emissions standards during certification and real-world operations. In recent years, California - Air Resources Board (C-ARB) has initiated many studies to assess the technology road maps to achieve Ultra-Low NOx emissions for HD diesel applications [1]. Subsequently, there is also a major push for the complex real-world driving emissions as the confirmatory and certification testing procedure in Europe and Asia through the UN-ECE and ISO standards.

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 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 The braking phenomenon is an aspect of vehicle stopping performance where with kinetic energy due to the speed of the vehicle is transformed into thermal energy produced by the brake disc and its pads. The heat must then be dissipated into the surrounding structure and into the airflow around the brake system. The thermal friction field during the braking phase between the disc and the brake pads can lead to excessive temperatures. In our work, we presented numerical modeling using ANSYS software adapted in the finite element method (FEM), to follow the evolution of the global temperatures for the two types of brake discs, full and ventilated disc during braking scenario. Also, numerical simulation of the transient thermal analysis and the static structural analysis were performed here sequentially, with coupled thermo-structural method.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

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 In this work, the refinement of a phenomenological turbulence model developed in recent years by the authors is presented in detail. As known, reliable information about the underlying turbulence intensity is a mandatory prerequisite to predict the burning rate in phenomenological combustion models. The model is embedded under the form of “user routine” in the GT-Power™ software. The main advance of the proposed approach is the potential to describe the effects on the in-cylinder turbulence of some geometrical parameters, such as the intake runner orientation, the compression ratio, the bore-to-stroke ratio, and the valve number. The model is based on three balance equations, referring to the mean flow kinetic energy, the tumble vortex momentum, and the turbulent kinetic energy (3-eq. concept). An extended formulation is also proposed, which includes a fourth equation for the dissipation rate, allowing to forecast also the integral length scale (4-eq. concept).

Abstract A wind-tunnel study was undertaken to investigate the drag reduction potential of two-truck platooning, in the context of understanding some of the factors that may influence the potential fuel savings and greenhouse-gas reductions. Testing was undertaken in the National Research Council Canada 2 m × 3 m Wind Tunnel with two 1/15-scale models of modern aerodynamic tractors paired with dry-van trailers configured with and without combinations of side-skirts and boat-tails. Separation distances of 0.14, 0.28, 0.49, 0.70 and 1.04 vehicle lengths were tested (3 m, 6 m, 10.5 m, 15 m, and 22.5 m full scale). Additionally, within-lane lateral offsets up to 0.31 vehicle widths (0.8 m full scale) were evaluated, along with a full-lane offset of 1.42 vehicle widths (3.7 m full scale). This study has made use of a wind-averaged-drag coefficient as the primary metric for evaluating the effect of vehicle platooning.