Search Results

Abstract Computational and experimental studies have been undertaken to investigate injurious head-first impacts (HFI), which can occur during automotive rollovers. Recent studies assume a torso surrogate mass (TSM) boundary condition, wherein the first or first two thoracic vertebrae are potted and constrained to only move in the vertical loading direction. The TSM boundary condition has not been compared with a full body (FB) model computationally or experimentally for HFI. In this study, the Global Human Body Models Consortium 50th percentile male detailed human body model (M50-O, Version 6.0) was applied to compare the kinematic, kinetic, and injury response of an HFI with a TSM boundary condition (M50-TSM), and a full body boundary condition (M50-FB). Impacts (to M50-TSM and M50-FB) were simulated between the head and a rigid plate using a commercial FE code (LS-DYNA).

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 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 Compressive impacts on the cervical spine can result in bony fractures. Bone fragments displaced into the spinal canal produce spinal canal occlusion, increasing the potential for spinal cord injury (SCI). Human body models (HBMs) provide an opportunity to investigate SCI but currently need to be improved in their ability to model compression fractures and the resulting material flow. Previous work to improve fracture prediction included the development of an anisotropic material model for the bone (hard tissues) of the vertebrae assessed in a functional spinal unit (FSU) model. In the FSU model, bony failure was modeled with strain-based element erosion, with a limitation that material that could occlude the spinal canal during compression was removed when an element was eroded.

Abstract Pre-crash vehicle maneuvers are known to affect occupant posture and kinematics, which consequently may influence injury risks during a collision. In this study, the influence of pre-crash vehicle maneuvers on the injury risks of front-seated occupants during a frontal crash was numerically evaluated. A generic buck vehicle model was developed based on a publicly available FE model, which included the vehicle interior and the front passenger airbag (PAB). The pre-crash phase was simulated using specific rigid-body human models with active joints (GHBMCsi-pre models) developed based on exterior shapes of the simplified deformable human model (GHBMCsi) representing a 50th male subject. Two pre-crash maneuvers representing (1) a generic 1g braking and (2) turning-and-braking scenarios were simulated.

Abstract Pyrotechnic seat belt pretensioners typically remove 8–15 cm of belt slack and help couple an occupant to the seat. Our study investigated pretensioner deployment on forward-leaning, live volunteers. The forward-leaning position was chosen because research indicates that passengers frequently depart from a standard sitting position. Characteristics of the 3D kinematics of forward-leaning volunteers following pretensioner deployment determines if body size is correlated with subject response. Nine adult subjects (three female), ages 18–43 years old, across a wide range of body sizes (50–120 kg) were tested. The age was limited to young, active adults as pyrotechnic pretensioners can deliver a notable force to the trunk. Subjects assumed a forward-leaning position, with 26 cm between C7 and the headrest, in a laboratory setting that replicated the passenger seat of a vehicle.

Abstract Due to the incoming phase out of fossil fuels from the market in order to reduce the carbon footprint of the automotive sector, hydrogen-fueled engines are candidate mid-term solution. Thanks to its properties, hydrogen promotes flames that poorly suffer from the quenching effects toward the engine walls. Thus, emphasis must be posed on the heat-up of the oil layer that wets the cylinder liner in hydrogen-fueled engines. It is known that motor oils are complex mixtures of a number of mainly heavy hydrocarbons (HCs); however, their composition is not known a priori. Simulation tools that can support the early development steps of those engines must be provided with oil composition and properties at operation-like conditions. The authors propose a statistical inference-based optimization approach for identifying oil surrogate multicomponent mixtures. The algorithm is implemented in Python and relies on the Bayesian optimization technique.

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 In recent years, demands of flat wipers have rapidly increased in the vehicle industry due to their simpler structure compared to the conventional wipers. Procedures for evaluating the appropriate metallic flexor geometry, which is one of the major components of the flat wiper, were proposed in the authors’ previous study. However, the computational cost of the aforementioned procedures seems to be unaffordable to the industry. The discrete Winkler model regarding the flexor as the Euler–Bernoulli beam is established as the mathematical model in this study to simulate a flexor compressed against a surface at various wiping angles. The deflection of the beam is solved using a finite difference method, and the calculated contact pressure distributions agree fairly with those based on the corresponding finite element model. Flexor designs are paired with various windshield surfaces to accumulate a sufficiently large simulation database based on the mathematical model.

Abstract The use of data-driven algorithms for the integration or substitution of current production sensors is becoming a consolidated trend in research and development in the automotive field. Due to the large number of variables and scenarios to consider; however, it is of paramount importance to define a consistent methodology accounting for uncertainty evaluations and preprocessing steps, that are often overlooked in naïve implementations. Among the potential applications, the use of virtual sensors for the analysis of solid emissions in transient cycles is particularly appealing for industrial applications, considering the new legislations scenario and the fact that, to our best knowledge, no robust models have been previously developed.

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 The progressive development toward highly automated driving poses major challenges for the release and validation process in the automotive industry, because the immense number of test kilometers that have to be covered with the vehicle cannot be tackled to any extent with established test methods, which are highly focused on the real vehicle. For this reason, new methodologies are required. Simulation-based testing and, in particular, virtual driving tests will play an important role in this context. A basic prerequisite for achieving a significant reduction in the test effort with the real vehicle through these simulations are realistic test scenarios. For this reason, this article presents a novel approach for generating relevant traffic situations based on a traffic flow simulation in SUMO and a vehicle dynamics simulation in CarMaker. The procedure is shown schematically for an emergency braking function.

Abstract Motion planning for autonomous vehicles remains challenging, especially in environments with multiple vehicles and high speeds. Autonomous racing offers an opportunity to develop algorithms that can deal with such situations and adds the requirement of following race rules. We propose a hybrid local planning approach capable of generating rule-compliant trajectories at the dynamic limits for multi-vehicle oval racing. The planning method is based on a spatiotemporal graph, which is searched in a two-step process to exploit the dynamic limits on the one hand and achieve a long planning horizon on the other. We introduce a soft-checking procedure that can handle cases where no collision-free, feasible, or rule-compliant solutions are found to restore an admissible state as quickly as possible. We also present a state machine explicitly designed for fully autonomous operation on a racetrack, acting on a higher level of the planning algorithm.

Abstract To investigate the effect of a tire blowout (TBO) on the dynamics of the vehicle comprehensively, a three-dimensional full-vehicle multibody mathematical model is developed and integrated with the nonlinear Dugoff’s tire model. In order to ensure the validity of the developed model, a series of standard maneuvers is carried out and the resulting response is verified using the high-fidelity MSC Adams package. Consequently, the in-plane, as well as out-of-plane dynamics of the vehicle, is extensively examined through a sequence of TBO scenarios with various blown tires and during both rectilinear and curvilinear motion. Moreover, the different possible inputs from the driver, the road bank angle, and the antiroll bar have been accounted for. The results show that the dynamic behavior of the vehicle is tremendously affected both in-plane and out-of-plane and its directional stability is degraded.

Abstract Pre-chamber turbulent jet ignition (TJI) is a method of generating distributed ignition sites through multiple high-speed turbulent jets in order to achieve an enhanced burn rate in the engine cylinder when compared to conventional spark plug ignition. To study the gas-dynamic interactions between the two chambers in a gasoline engine, a three-dimensional numerical model was developed using the commercial CFD code CONVERGE. The geometry and parameters of the engine used were based on a modified turbocharged GM four-cylinder 2.0 L GDI gasoline engine. Pre-chambers with nozzle diameters of 0.75 mm and 1.5 mm were used to investigate the effect of pre-chamber geometry on pre-chamber charging, combustion, and jet formation. The local developments of gas temperature and velocity were captured by adaptive mesh refinement, while the turbulence was resolved with the k-epsilon model of the Reynolds averaged Navier–Stokes (RANS) equations.

Abstract A previously developed piston damage and exhaust gas temperature models are coupled to manage the combustion process and thereby increasing the overall energy conversion efficiency. The proposed model-based control algorithm is developed and validated in a software-in-the-loop simulation environment, and then the controller is deployed in a rapid control prototyping device and tested online at the test bench. In the first part of the article, the exhaust gas temperature model is reversed and converted into a control function, which is then implemented in a piston damage-based spark advance controller. In this way, more aggressive calibrations are actuated to target a certain piston damage speed and exhaust gas temperature at the turbine inlet. A more anticipated spark advance results in a lower exhaust gas temperature, and such decrease is converted into lowering the fuel enrichment with respect to the production calibrations.

Abstract Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm.

Abstract The key issues of automatic emergency braking (AEB) control algorithm are when and how to brake. This article proposes an AEB control algorithm that integrates risk perception (RP) and emergency braking characteristics of professional drivers for rear-end collision avoidance. Using the formulated RP by time to collision (TTC) and time headway (THW), the brake trigger time can be determined. Based on the professional driver fitting (PDF) characteristic, the brake pattern can be developed. Through MATLAB/Simulink simulation platform, the European New Car Assessment Programme (Euro-NCAP) test scenarios are used to verify the proposed control algorithm. The simulation results show that compared with the TTC control algorithm, PDF control algorithm, and the integrated PDF and TTC control algorithm, the proposed integrated PDF and RP control algorithm has the best performance, which can not only ensure safety and brake comfort, but also improve the road resource utilization rate.

Abstract Lubrication has been a major area of interest in engineering. Especially in vehicle transmissions, lubrication plays a very crucial role because gears and bearings are constantly subjected to heavy loads. Proper lubrication is essential for maintaining system performance and ensuring endurance life. Insufficient lubrication can lead to excessive wear, increased friction, and eventually, failures in the transmission components. However, excess lubrication can result in power losses due to the resistance offered by the excessive lubricant. Therefore, achieving effective lubrication using optimized lubrication system design is vital for ensuring the longevity and efficiency of the transmission system. Majorly, two types of lubrication methods are used in transmissions: splash lubrication and forced lubrication. This article focuses on forced lubrication, where the lubrication system actively delivers the required flow of lubricant to specific locations within the transmission.

Abstract Electric vehicles (EVs) suffer from long charging time and inconvenient charging due to limited charging stations, which are the main causes of drivers’ range anxiety. Real-time and accurate driving range prediction can help drivers plan journeys, alleviate range anxiety, and promote EV development. However, predicting the EV driving range is challenging due to different weather, road conditions, driver habits, and limited available data. To address this issue, this article proposes a novel digital twin-based driving range prediction method. First, a one-year real-world EV dataset in Beijing is utilized. Detailed feature selection is conducted for the dataset, and six key features are extracted: battery SOC, consumed battery SOC, battery total voltage, battery maximum cell voltage, battery minimum cell voltage, and mileage already driven. Then, a random forest method is used to train the EV driving range prediction model using the features described earlier.