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A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming. However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

Accurately predicting the evolution of soot mass and soot particle numbers under engine conditions is critical to advanced engine design. A detailed soot-chemistry model that can capture soot under gasoline and diesel conditions without tuning is necessary for such predictions. Building confidence in the predictive usage of the chemistry in engine simulations requires validating the soot kinetics over a wide range of operating conditions and fuels, using data from different experimental techniques, and using sources from laboratory flames to engines. This validation study focuses on a soot-chemistry model that considers multiple nucleation, growth, and oxidation reaction pathways. It involves 14 gas-phase precursors and considers the effect of different soot-particle surface sites.

In order to achieve the purpose of saving energy and reducing emission, the improvement of aerodynamic performance plays an increasingly crucial role for car manufacturers. Previous studies have confirmed the validity of gradient-based adjoint algorithm for its high efficiency in shape optimization. In this paper, two important aspects of adjoint approach were explored. One is vehicle aerodynamic optimization with multiple objectives, and the other is using time-averaged flow results as the primal solution, both are issues of high interest in recent applications. First, adjoint shape optimization with steady-state and time-averaged flow simulations were respectively calculated and comparatively discussed based on a production SUV. The shape modifications of the two cases indicated that the impact of primal solution on design change could not be neglected, due to the different intrinsic codes of steady and transient turbulence models.

Strain-rate sensitivity has been neglected in the simulation of the traditional stamping process because the strain rate typically does not significantly impact the forming behavior of sheet metals in such a quasi-static process, and traditional crank or link mechanical presses lack the flexibility of slide motion. However, the recent application of servo drive presses in stamping manifests improvement in formability and reduction of springback, besides increased productivity and energy savings. An accurate simulation of servo stamping entails constitutive models with strain-rate sensitivity. This study evaluated a few strain rate-sensitive models including the power-law model, the linear power-law model, the Johnson-Cook model, and the Cowper-Symonds model through the exercise of fitting these models to the experimental data of a deep draw quality (DDQ) steel.

Aircraft components are commonly produced with 7000 series aluminum alloys (AA) due to its weight, strength, and fatigue properties. Auto Industry is also choosing more and more aluminum component for weight reduction. Current additive manufacturing (AM) methods fall short of successfully producing 7000 series AA due to the reflective nature of the material along with elements with low vaporization temperature. Moreover, lacking in ideal thermal control, print inherently defective products with such issues as poor surface finish alloying element loss and porosity. All these defects contribute to reduction of mechanical strength. By monitoring plasma with spectroscopic sensors, multiple information such as line intensity, standard deviation, plasma temperature or electron density, and by using different signal processing algorithm, AM defects have been detected and classified.

Two main approaches are available when studying droplet dynamics for in-flight icing simulations: the Lagrangian approach, in which each droplet trajectory is integrated until it impacts the vehicle under study or when it leaves it behind without impact, and the Eulerian approach, where the droplet dynamics is solved as a continuum. In both cases, the same momentum equations are solved. Each approach has its advantages. In 2D, the Lagrangian approach is easy to code and it is very efficient, particularly when used in combination with a panel method flow solver. However, it is a far less practical approach for 3D simulations, particularly on complex geometries, as it is not an easy task to accurately determine the droplet seeding region without a great number of droplet trajectories, dramatically increasing the computing cost. Converting the impact locations into a water collection distribution is also a complex task, since droplet trajectories in 3D can follow convoluted paths.

Privacy has been a rising concern. The European Union has established a privacy standard called General Data Protection Regulation (GDPR) in May 2018. Furthermore, the Facebook-Cambridge Analytica data incident made headlines in March 2018. Data collection from vehicles by OEM platforms is increasingly popular and may offer OEMs new business models but it comes with the risk of privacy leakages. Vehicular sensor data shared with third-parties can lead to misuse of the requested data for other purposes than stated/intended. There exists a relevant regulation document introduced by the Alliance of Automobile Manufacturers (“Auto Alliance”), which classifies the vehicular sensors used for data collection as covered and non-sensitive parameters.

There is a strong evidence that the overrepresentation of teen drivers in motor vehicle crashes is mainly due to their poor hazard perception skills, i.e., they are unskilled at appropriately detecting and responding to roadway hazards. This study evaluates two cuing systems designed to help teens better understand their driving environment. Both systems use directional color-coding to represent different levels of proximity between one’s vehicle and outside agents. The first system provides an overview of the location of adjacent objects in a head-up display in front of the driver and relies on drivers’ focal vision (focal cuing system). The second system presents similar information, but in the drivers’ peripheral vision, by using ambient lights (peripheral cuing system). Both systems were retrofitted into a test vehicle (2014 Toyota Camry). A within-subject experiment was conducted at the University of Michigan Mcity test-track facility.

A new closed-form structural stress solution for a spot weld in a square thin plate under central bending conditions is derived based on the thin plate theory. The spot weld is treated as a rigid inclusion and the plate is treated as a thin plate. The boundary conditions follow those of the published solution for a rigid inclusion in a square plate under counter bending conditions. The new closed-form solution indicates that structural stress solution near the rigid inclusion on the surface of the plate along the symmetry plane is larger than those for a rigid inclusion in an infinite plate and a finite circular plate with pinned and clamped outer boundaries under central bending conditions. When the radius distance becomes large and approaches to the outer boundary, the new analytical stress solution approaches to the reference stress whereas the other analytical solutions do not.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.

Crash safety researchers have an increased concern regarding the decreased thoracic deflection and the contributing injury causation factors among the elderly population. Sternum fractures are categorized as moderate severity injuries, but can have long term effects depending on the fragility and frailty of the occupant. Current research has provided detail on rib morphology, but very little information on sternum morphology, sternum fracture locations, and mechanisms of injury. The objective of this study is two-fold (1) quantify sternum morphology and (2) document sternum fracture locations using computed tomography (CT) scans and crash data. Thoracic CT scans from the University of Michigan Hospital database were used to measure thoracic depth, manubriosternal joint, sternum thickness and bone density. The sternum fracture locations and descriptions were extracted from 63 International Center for Automotive Medicine (ICAM) crash cases, of which 22 cases had corresponding CT scans.

Additive Manufacturing, also known as AM or 3D printing, is a class of manufacturing processes that create objects by shaping material layer by layer. Having demonstrated the ability to produce miraculously complex geometries, it is broadly claimed that AM will have endless applications as the technology improves. However, underneath the hype surrounding this technology is a world of nuance and constraints as well as highly strategic applications. Additive Manufacturing for Designers: A Primer, written by Dr. Amy Elliott from Oak Ridge National Laboratory and Dr. Cynthia K. Waters from North Carolina A&T State University discusses the topics needed for a holistic understanding of the many micro and macro components of the world of 3D printing. Additive Manufacturing for Designers: A Primer takes the reader on a journey beginning with important aspects of AM part design and process dependence, including resolution and tolerance issues of interest to any manufacturer.

Failure mode and fatigue behavior of flow drill screw (FDS) joints in lap-shear specimens of aluminum 6082-T6 sheets made with different processing conditions are investigated based on the experimental results and a structural stress fatigue life estimation model. Lap-shear specimens with FDS joints without clearance hole and lap-shear specimens with stripped FDS joints with clearance hole were made and then tested under quasi-static and cyclic loading conditions. Optical micrographs show the failure modes of the FDS joints without clearance hole (with gap) and the stripped FDS joints with clearance hole under quasi-static and cyclic loading conditions. The fatigue failure mode of the FDS joints without clearance hole (with gap) in lap-shear specimens is similar to those with clearance hole. The fatigue lives of lap-shear specimens with FDS joints without clearance hole are lower than those with clearance hole for given load ranges under cyclic loading conditions.

Warpage is the distortion induced by inhomogeneous shrinkage during injection molding of plastic parts. Uncontrolled warpage will result in dimensional instability and bring a lot of challenges to the mold design and part assembly. Current commercial simulation software for injection molding cannot provide consistently accurate warpage prediction, especially for semi-crystalline thermoplastics. In this study, the root cause of inconsistency in warpage prediction has been investigated by using injection molded polypropylene plaques with a wide range of process conditions. The warpage of injection molded plaques are measured and compared to the numerical predictions from Moldex3D. The study shows that with considering cooling rate effect on crystallization kinetics and using of the improved material model for residual stress calculations, good agreements are obtained between experiment and simulation results.

This presentation is an assessment of the turbulence-stress scale-similarity in an IC engine, which is used for modeling subgrid dissipation in LES. Residual stresses and Leonard stresses were computed after applying progressively smaller spatial filters to measured and simulated velocity distributions. The velocity was measured in the TCC-II engine using planar and stereo PIV taken in three different planes and with three different spatial resolutions, thus yielding two and three velocity components, respectively. Comparisons are made between the stresses computed from the measured velocity and stress computed from the LES resolved-scale velocity from an LES simulation. The results present the degree of similarity between the residual stresses and the Leonard stresses at adjacent scales. The specified filters are systematically reduced in size to the resolution limits of the measurements and simulation.

In this study, we extend and improve on the methods introduced by Brudnak et al. [1] by adding a second objective to the reduction of test time. This second objective under consideration is to diminish or reduce the number of cuts or deletions to the time histories during an editing process. As discussed in [1], segment-based methods consider each segment for retention or deletion based on its own localized severity, not considering the segments around it. As a result, retained segments can be widely scattered in the time domain depending on signal characteristics and therefore a large number of cuts can be induced unintentionally. Regardless of the joining method, such cuts and joins require artificial signal processing and should therefore be minimized. In this paper we present techniques to minimize these cuts while at the same time maintaining our original goals of time reduction and severity retention.

Increasing the strength of materials is effective in reducing weight and boosting structural part performance, but there are cases in where the residual strain generated during the process of manufacturing of high-strength materials results in a decline of durability. It is therefore important to understand how the residual strain in a manufactured component changes due to processing conditions. In the case of a connecting rod, because the strain load on the connecting rod rib sections is high, it is necessary to clearly understand the distribution of strain in the ribs. However, because residual strain is generally measured by using X-ray diffractometers or strain gauges, measurements are limited to the surface layer of the parts. Neutron beams, however, have a higher penetration depth than X-rays, allowing for strain measurement in the bulk material.