Search Results

Abstract Bilateral knee impacts were conducted on Hybrid III and THOR 5th percentile female anthropomorphic test devices (ATDs), and the results were compared to previously reported female PMHS data. Each ATD was impacted at velocities of 2.5, 3.5, and 4.9 m/s. Knee–thigh–hip (KTH) loading data, obtained either via direct measurement or through exercising a one-dimensional lumped parameter model (LPM), was analyzed for differences in loading characteristics including the maximum force, time to maximum force, loading rate, and loading duration. In general, the Hybrid III had the highest loading rate and maximum force, and the lowest loading duration and time to peak force for each point along KTH. Conversely, the PMHS generally had the lowest loading rate and maximum force, and the highest loading duration and time to peak force for each point along KTH.

Abstract Ground vibration testing (GVT) is an important phase of the development, or the structural modification of an aircraft program. The modes of vibration and their associated parameters extracted from the GVT are used to modify the structural model of the aircraft to make more reliable dynamics predictions to satisfy certification authorities. Due to the high cost and the extensive preparations for such tests, a new method of vibration testing called taxi vibration testing (TVT) rooted in operational modal analysis (OMA) was recently proposed and investigated by the German Institute for Aerospace Research (DLR) as alternative to conventional GVT. In this investigation, a computational framework based on fully coupled flexible multibody dynamics for TVT is presented to further investigate the applicability of the TVT to flexible airframes. The time domain decomposition (TDD) method for OMA was used to postprocess the response of the airframe during a TVT.

Abstract Since the complexity of modern vehicles is increasing continuously, car manufacturers are forced to improve the efficiency of their development process to remain profitable. A frequently mentioned measure is the consequent integration of virtual methods. In this regard, objective evaluation criteria are essential for the virtual design of driving dynamics. Therefore, this article aims to identify robust objective evaluation criteria for the nonlinear combined longitudinal and lateral dynamics of a vehicle. The article focuses on the acceleration in a turn maneuver since available objective criteria do not consider all relevant characteristics of vehicle dynamics. For the identification of the objective criteria, a generic method is developed and applied. First, an open-loop test procedure and a set of potential robust objective criteria are defined.

Abstract Crash pulse prediction is one of the crucial factors in developing integrated (a unified active and passive) safety systems in the vehicle. As discussed by many researchers, the crash pulse can have considerable effects on seat occupant response. So, thereby predicting the crash pulse can help to decrease the severity of the injury in many cases. In this research article, we propose a machine-learning-based model to predict the crash pulse during head-on collisions. The model uses a regression algorithm and gradient descent optimization method for accurate predictions. After introducing the topic, the related works from different researchers highlights the need to predict the crash pulse. The following section will discuss the method adopted for generating the data and selecting the training and testing data. The training data is used to learn the algorithm, and the testing data is used for validating the prediction model.

Abstract A rear underrun protection device (RUPD) plays a fundamental role in reducing the risk of running a small car beneath the rear or the side of a heavy truck because of the difference in structure heights in the event of a vehicle collision. Even in cars with five-star safety ratings, crashing into a truck with poorly designed RUPD results in a passenger compartment intrusion (PCI) more than the maximum allowable limit as per the United States (US) American National Highway Traffic Safety Administration (NHTSA) standards Federal Motor Vehicle Safety Standard (FMVSS). In this article, mild steel was used to fabricate the new designs of RUPD. The design was analyzed using finite element (FE) analysis LS-DYNA software. Simulations of a Toyota Yaris 2010 and Ford Taurus 2001 were performed at a constant speed of 63 km/h at the time of impact. The ability to prevent severe injuries in a collision with the rear side of the truck was estimated to optimize the underrun design.

Abstract In autonomous driving vehicles with an automation level greater than three, the autonomous system is responsible for safe driving, instead of the human driver. Hence, the driving safety of autonomous driving vehicles must be ensured before they are used on the road. Because it is not realistic to evaluate all test conditions in real traffic, computer simulation methods can be used. Since driving safety performance can be evaluated by simulating different driving scenarios and calculating the criticality metrics that represent dangerous collision risks, it is necessary to study and define the criticality metrics for the type of driving scenarios. This study focused on the risk of collisions in the confluence area because it was known that the accident rate in the confluence area is much higher than on the main roadway.

Abstract Aircraft lift bag is the equipment used for the recovery of an aircraft and is considered as a lifting equipment. Boeing 737 is a domestic aircraft considered for designing this bag. The aircraft lift bag is made of composite material, and the most widely used materials are nylon and neoprene. A composite material is used to make the bag lightweight and easy to handle. For calculation of properties and the engineering constant of the respective composite materials, micromechanics approach is used, in which the method of Representative Volume Element (RVE) is taken into consideration. The loading and boundary conditions are the exact replica of the working conditions. The operation of this bag is completely pneumatic. The stresses induced in the bag are analyzed in finite element software and are compared with the calculated theoretical values. CATIA is used to model the bag, and ABAQUS is used for the finite element calculations.

Abstract Driver drowsiness is the cause of many fatal accidents all over the world. Many research works have been conducted on detecting driver drowsiness for more than half a century, but statistical data show that such accidents have not decreased significantly. Most researchers have focused on using certain sensors and extracting their relevant features. However, there has been no research work on developing an algorithm to detect driver drowsiness independently from the input type. In this paper, a hybrid fuzzy-reinforcement learning drowsiness detection algorithm is presented. This algorithm is flexible to work with any number and any kind of data related to driver alertness. It estimates the level of alertness based on an arbitrary number of inputs. The algorithm extracts driving patterns specific to each driver and determines driver’s level of drowsiness using a continuous numerical variable rather than a discrete variable.

Abstract The preeminent obligation of the automotive engineers, while designing a car, is to assure the driver’s well-being during any kind of impact by suppressing intrusions into the cockpit or minacious deceleration levels. Technologists and designers are advancing various modern active and passive safety systems to augment vehicle occupants’ safety. To mitigate the research and development expenditure in time and money, it is recommended to utilize computational crash simulations for the early evaluation of safety behavior under vehicle impact tests. Therefore, in this research study, an attempt is made to simulate crashworthiness and design the impact attenuator utilized in Formula SAE vehicles to absorb the kinetic energy of a car during a frontal collision. Closed-cell aluminum foam is selected as its material because of its less density than solid metals and ability to undergo large deformations at almost constant load.

Abstract The Coordinating Research Council (CRC) is actively involved in developing and applying advanced analytical techniques to the chemical characterization of transportation fuels. This article complements a 2017 CRC project to quantify and compare the effects of a commercially available renewable diesel fuel (hydrotreated vegetable oil [HVO]) and an ultralow sulfur diesel (ULSD) fuel on engine-out gaseous and particulate matter (PM) emissions from a light-duty vehicle. Results showed that the combustion of HVO fuel had an advantage over ULSD in terms of lowering engine-out emissions (THC, CO, NOx, etc.). Furthermore, this advantage is strongly related to the fuel composition. This article summarizes the results of advanced and comprehensive analytical tests on the same ULSD and HVO fuels and attempts to connect some of the engine-out emissions results to fuel composition and specific chemical structures.

Abstract This study proposes a method for the rapid detection and location of cavity defects in ballastless track structures of high-speed railways in service. First, the propagation law of air-coupled ultrasonic Lamb waves in the ballastless track structure is studied. Theoretical calculation results show that the ultrasonic Lamb wave group velocity of the A2 mode in the track plate is 4000 m/s. Then, the excitation and reception methods of the air-coupled ultrasound are studied. Theoretical and experimental results show that the A2 mode Lamb wave can be generated by the 3.8° oblique incidence of the ballastless track structure. Finally, an experimental system for air-coupled ultrasonic testing is constructed. A pair of air-coupled ultrasonic probes is used to provide excitation and reception Lamb wave signals at an inclined angle of 3.8°, 20 mm away from the surface of the track plate, and 40 mm/step along the scanning direction.

Abstract Sideswipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this article, a fusion approach is introduced that utilizes data from two differing modality sensors (a front-view camera and an onboard diagnostics (OBD) sensor) for the purpose of detecting driver’s behavior of lane changing. For lane change detection, both feature-level fusion and decision-level fusion are examined by using a collaborative representation classifier (CRC). Computationally efficient detection features are extracted from distances to the detected lane boundaries and vehicle dynamics signals. In the feature-level fusion, features generated from two differing modality sensors are merged before classification, while in the decision-level fusion, the Dempster-Shafer (D-S) theory is used to combine the classification outcomes from two classifiers, each corresponding to one sensor.

Abstract Objectives: The project goal was to create an initial set of standardized tests to explore whether they enable the ongoing evaluation of automated driving features as they evolve over time. These tests focused on situations that were representative of several daily driving scenarios as encountered by lower-level automated features, often called Advanced Driver Assistance Systems (ADAS), while looking forward to higher levels of automation as new systems are deployed. Methods: The research project initially gathered information through a review of existing literature about ADAS and current test procedures. Thereafter, a focus group of industry experts was convened for additional insights and feedback. With this background, the research team developed a series of tests designed to evaluate a variety of automated driving features in currently available implementations and anticipated future variants.

Abstract Traumatic brain injury is a leading cause of global death and disability. Clinically relevant large animal models are a vital tool for understanding the biomechanics of injury, providing validation data for computation models, and advancing clinical translation of laboratory findings. It is well-established that large angular accelerations of the head can cause TBI, but the effect of head impact on the extent and severity of brain pathology remains unclear. Clinically, most TBIs occur with direct head impact, as opposed to inertial injuries where the head is accelerated without direct impact. There are currently no active large animal models of impact TBI. Sheep may provide a valuable model for studying TBI biomechanics, with relatively large brains that are similar in structure to that of humans. The aim of this project is to develop an ovine model of impact TBI to study the relationships between impact mechanics and brain pathology.

Abstract A fully predictive one-dimensional model of a Common Rail injection apparatus for diesel passenger cars is presented and discussed. The apparatus includes high-pressure pump, high-pressure pipes, injectors, rail and a fuel-metering valve that is used to control the rail pressure level. A methodology for separately assessing the accuracy of the single submodels of the components is developed and proposed. The complete model of the injection system is finally validated by means of a comparison with experimental high-pressure and injected flow-rate time histories. The predictive model is applied to examine the fluid dynamics of the injection system during either steady-state or transient operations. The influence of the pump delivered flow-rate on the rail-pressure time history and on the injection performance is analysed for different energizing times and nominal rail pressure values.

Abstract Some anthropomorphic test devices (ATDs) currently being developed are equipped with abdominal pressure twin sensors (APTS) for the assessment of abdominal injuries and as an indicator of the occurrence of the submarining of an occupant during a crash event. The APTS is comprised of a fluid-filled polyurethane elastomeric bladder which is sealed by an aluminum cap with an implanted pressure transducer. It is integrated into ATD abdomens, and fluid pressure is increased due to the abdomen/bladder compression due to interactions with the seatbelt or other structures. In this article, a nonlinear dynamic finite element (FE) model is constructed of an APTS using LS-PrePost and converted to the LS-Dyna solver input format. The polyurethane bladder and the internal fluid are represented with viscoelastic and isotropic hypoelastic material models, respectively. The aluminum cap was considered a rigid part since it is significantly stiffer than the bladder and the fluid.

Abstract Safety systems have historically been evaluated with anthropomorphic test devices for research, development, or regulatory concerns. Human body models are another avenue for use in the investigation of occupant safety. In this study, transfer equations are developed to quantify the response of a human model (Global Human Body Models Consortium average male simplified model) and dummy model (Hybrid-III) in equivalent environments. Environments were selected based on certification test setups used for the Hybrid III ATD as well as a basic frontal sled environment. The tests include a head drop, neck flexion/extension, and chest and knee impacts. Furthermore, models were positioned within a simplified occupant interior for sled tests. In all, 30 matched pair simulations were run, 60 in total.

Abstract Nowadays, demanding carbon dioxide (CO2) targets push for the electrification of the powertrain since the internal combustion engine, as the sole way to propel the vehicle, cannot reach those targets. In this context, Mild-Hybrid Vehicles (MHV) with a low-voltage 48 V electric network proved to be a cost-effective solution for the reduction of CO2 emissions. However, the impact of the electrification on the powertrain thermal management, on the aftertreatment light-off, and thus on tailpipe (TP) emissions must be properly assessed. For this reason, a virtual testing approach for thorough powertrain and vehicle virtual validation is required to reduce testing and calibration efforts. The aim of this research work is to bridge the gap between high-fidelity models commonly used for the development of components and a system-level approach for the evaluation of full vehicle technologies and architectures.

Abstract Human thoracic injury under frontal collisions is an inevitable problem in vehicle safety research. Compared with the Multiple Rigid-Body Models (MRBMs) and Finite Element Human Body Models (FEHBMs), Mathematical Equivalent Models (MEMs) can not only provide important data but also improve the research efficiency. The current thoracic MEMs usually adapted the mechanical isolation method to isolate the thorax from the human body; therefore, the effects of the head, neck, and lower body internal organs on the mechanical responses of the thorax are not considered. In this article, a new thoracic MEM, named as Improved Consistent Lobdell Model (ICLM), is developed based on the concentrated mass-spring-damping system to consider the energy absorbed by the deformation of the internal soft tissue and the motion hysteresis of the head, neck, and lower body.

Abstract Vehicle cabin air quality depends on various parameters such as number of passengers, fan speed, and vehicle speed. In addition to controlling the temperature inside the vehicle, HVAC control system has evolved to improve cabin air quality as well. However, there is no standard test method to ensure reliable and repeatable comparison among different cars. The current study defined Cabin Air Quality Index (CAQI) and proposed a test method to determine CAQI. CAQIparticles showed dependence on the choice of metrics among particle number (PN), particle surface area (PS), and particle mass (PM). CAQIparticles is less than 1 while CAQICO2 is larger than 1. The proposed test method is promising but needs further improvement for smaller coefficient of variations (COVs).