Search Results

Abstract A kinematic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towing vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point.

Abstract To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, “U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards” identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis.

Abstract In Advanced Driver Assistant System (ADAS), the automotive radar is used to detect targets or obstacles around the vehicle. The procedure of Constant False Alarm Rate (CFAR) plays an important role in adaptive targets detection in noise or clutter environment. But in practical applications, the noise or clutter power is absolutely unknown and varies over the change of range, time and angle. The well-known cell averaging (CA) CFAR detector has a good detection performance in homogeneous environment but suffers from masking effect in multi-target environment. The ordered statistic (OS) CFAR is more robust in multi-target environment but needs a high computation power. Therefore, in this paper, a new two-dimension CFAR procedure based on a combination of Generalized Order Statistic (GOS) and CA CFAR named GOS-CA CFAR is proposed. Besides, the Linear Frequency Modulation Continuous Wave (LFMCW) radar simulation system is built to produce a series of rapid chirp signals.

Mechanical and thermal properties of the rubber compounds of a tire play an important role in the overall performance of the tire when it is in contact with the terrain. Although there are many studies conducted on the properties of the rubber compounds of the tire to improve some of the tire characteristics, such as the wear of the tread, there are a limited number of studies that focused on the performance of the tire when it is in contact with ice. This study is a part of a more comprehensive project looking into the tire-ice performance and modeling. In this study, to understand the effect of different rubber compounds on the tire performance, three identical tires from the same company have been chosen. The tires’ only difference is the material properties of the rubber. Two approaches have been implemented in this study.

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 With the growing number of cross-sea flights, the occurrence of maritime-related accidents, which have a high fatality rate, has become increasingly critical. This study is aimed at highlighting the causes of maritime-related accidents and identifying the risk factors that led to fatal crashes in the period 2009-2019. A total of 207 maritime-related accidents, the final reports of which are available in the online database of the National Transportation Safety Board, were considered. The accident cause distribution was obtained from the final reports. A two-step approach, involving uni-variable and multi-variable analysis logistic regression, was implemented to select the significant risk factors from 27 parameters. Results showed that the four main causes of maritime-related accidents were personnel issues (69.6%), aircraft-related aspects (60.4%), environmental issues (36.7%), and organizational issues (3.9%).

Abstract Recently, high-resolution sensors capable of multiple detections (MDs) per object are available for perception applications in autonomous or semi-autonomous vehicles. Conventional multi-target tracking (MTT) approaches start with the point-target assumption and thus cannot be applied directly to the MDs of high-resolution sensors. A popular solution widely used in literature starts with a measurement partitioning approach, followed by repurposing conventional tracking algorithms to accommodate the resulting partitions. However, the computational requirement increases combinatorially, especially under multi-sensor applications that also independently return multiple radar reflections as in the automotive radar sensors used in this work. Thus, a hybrid approach that combines a clustering technique (such as DBSCAN) to alleviate the computational complexity and an MD tracking scheme that admits multiplicity of the target detections is employed.

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 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 The spray/wall interaction plays a significant role on the mixture formation, combustion, and exhaust emissions. In the present study, the numerical code General Transport Equation Analysis (GTEA) is used to investigate the effect of fuel primary spray on the spray/wall interaction process. Taylor Analogy Breakup (TAB) model, Kelvin-Helmholtz-Rayleigh-Taylor (KH-RT) model, and Hybrid breakup (Hybrid) model are used to simulate the fuel spray process. By comparing the radius and height of the impinged spray, the performance of these breakup models is evaluated. Then, Bai and Gosman (BG) and Zhang and Jia (ZJ) spray/wall interaction models are implemented into GTEA code to describe the complicated spray/wall interaction process, and these interaction models are validated by the radius and height of the impinged spray and the size and velocity of the secondary droplets.

Abstract In recent years, active sound design (ASD) has become one of the most important research topics in the field of active sound control technology. For electric vehicles (EVs), road noise and wind noise become the dominant contributors to the interior noise level due to the elimination of internal combustion engines (ICEs). In this case, different vehicle brands tend to resemble each other in the perspective of the interior sound quality, leading to the loss of the distinctive interior sound characteristics and brand image. In order to restore the brand DNA characteristics, ASD is a viable and implementable choice to break the dilemma the next-generation EVs would confront. Sound amplitude control strategy plays a key role in drivers’ subjective perception during dynamically operating an EV equipped with an ASD system.

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.

Abstract This article is a review of automobile sensor technologies that have the potential to enhance fuel economy. Based on an in-depth review of the literature and demonstration projects, the following sensor technologies were selected for evaluation: vehicular radar systems (VRS), camera systems (CS), and vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) systems. V2V and V2I systems were found to have the highest merit in improving fuel economy over a wide range of integration strategies, with fuel economy improvements ranging from 5 to 20% with V2V and 10 to 25% for V2I. However, V2V and V2I systems require significant adoption for practical application which is not expected in this decade. Numerous academic studies and contemporary vehicular safety systems attest VRS as more technologically mature and robust relative to other sensors. However, VRS offers less fuel economy enhancement (~14%).

Abstract Autonomous Vehicle (AV) technology has the potential to fundamentally transform the automotive industry, reorient transportation infrastructure, and significantly impact the energy sector. Rapid progress is being made in the core artificial intelligence engines that form the basis of AV technology. However, without a quantum leap in testing and verification, the full capabilities of AV technology will not be realized. Critical issues include finding and testing complex functional scenarios, verifying that sensor and object recognition systems accurately detect the external environment independent of weather conditions, and building a regulatory regime that enables accumulative learning. The significant contribution of this article is to outline a novel methodology for solving these issues by using the Florida Poly AV Verification Framework (FLPolyVF).

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract In order for autonomous vehicles to be widely adopted, they must be able to operate in all conditions possible in the regions they are operating. In northern climates, this means they must be able to operate on low friction due to the presence of ice or snow. An autonomous vehicle performing an obstacle avoidance maneuver in the form of a double-lane change maneuver must account for low friction when present to ensure a safe and comfortable ride for passengers. This work presents a graphical optimization method for determining a minimum maneuver distance based on surface friction coefficient, which is constrained by cross-track error and lateral acceleration.

Abstract Presently, 270 V direct current (DC) systems replace older 28 V DC voltage systems in both the civil and military aviation industry due to the requirement for more electrical power needs on board. Therefore, the existing avionics require retrofitting. The conversion from 270 V to 28 V appears to be quite promising for both old and new systems. This study aims to design an interleaved synchronous modular buck converter topology as a candidate for these requirements. Calculations for the converter design are conducted considering aviation standards. Switching with pulse-width modulation (PWM) is used to control the power converter. A double-loop feedback control system based on voltage and current feedback is designed. Therefore, the buck converter circuit with 1145 W power output is proposed, which supplies a 28 V and 41 A DC output from a 270 V DC input. The concept is verified using simulations and hardware-in-the-loop (HIL) experimental results.

Abstract Global warming has motivated the aeronautical industry to develop new technologies that will reduce polluting emissions. A direct way to achieve this goal is to reduce fuel consumption. Reference trajectory optimization contributes to this goal by guiding aircraft to zones where meteorological conditions are favorable to execute their required missions and thereby to reduce flight costs. In this article, the reference trajectory was optimized in terms of geographical position, altitude, and speed, by taking into account a Required Time of Arrival (RTA) constraint and weather conditions. The algorithm assumes that there is no traffic and that the aircraft can fly anywhere in the search space. The search space was modeled in the form of a unidirectional weighted graph, fuel burn was computed using a numerical model, and the weather forecast was taken into account.

The paper was originally published with the incorrect author reference in the citation. The correct citation should appear as follows: Murrieta-Mendoza, A., Botez, R., Ruiz, H., and Kessaci, S., “Particle Swarm Optimization with Required Time of Arrival Constraint for Aircraft Trajectory,” SAE Int. J. Aerosp. 13(2):269-291, 2020, doi:10.4271/01-13-02-0020.

Abstract To design better power converters with enhancement-mode Gallium Nitride high-electron-mobility transistor (eGaN HEMT) for emerging applications such as Electric Vehicles (EV), it is essential to model their switching transients and loss accurately. Analytical modeling has proved to be an effective approach to study the transistor’s dynamic behaviors and analyze the switching energy loss during the turn-on and turn-off transients. Furthermore, it helps to understand the essential factors that influence the switching transients and loss calculation. The accuracy of the analytical model mainly depends on the equivalent circuits and the parasitic parameters inside the transistor packaging and external circuits under different switching stages. It is always challenging to extract the parasitic parameters accurately due to its natural character of nonlinearity and complex correlation during the switching transients.