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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?”

In-flight icing significantly influences the design of large passenger aircraft. Relevant aspects include sizing of the main aerodynamic surfaces, provision of anti-icing systems, and setting of operational restrictions. Empennages of large passenger aircraft are particularly affected due to the small leading edge radius, and the requirement to generate considerable lift for round out and flare, following an extended period of descent often in icing conditions. This paper describes a CFD-based investigation of the effects of sweep on the aerodynamic performance of a novel forward-swept horizontal stabilizer concept in icing conditions. The concept features an unconventional forward sweep, combined with a high lift leading edge extension (LEX) located within a fuselage induced droplet shadow zone, providing passive protection from icing.

This paper introduces the Lagrangian particle tracking technology readily available in Ansys Fluent in the in-flight icing simulation workflow, which normally uses the Eulerian approach for droplet flows. The Lagrangian solver is incorporated in the Fluent Icing workspace which is to become the next-gen in-flight icing simulation tool provided by Ansys. Lagrangian tracking will eventually be used for SLD and ice crystal rebound and re-impingement calculations in the Ansys workflow. Here we introduce some preliminary results with the current state of its implementation as of Fluent Icing release 2023R2. Example cases include several selections from the 1st Ice prediction workshop with experimental comparisons as well as results obtained earlier with the Eulerian droplet solution strategy. Collection efficiency comparisons on clean geometries show good agreement between Eulerian and Lagrangian methods when the particle seeds are in the millions range.

This paper studies the level of confidence and applicability of CFD simulations using steady-state Reynolds-Averaged Navier-Stokes (RANS) in predicting aerodynamic performance losses on swept-wings due to contamination with ice accreted in-flight. The wing geometry selected for the study is the 65%-scale Common Research Model (CRM65) main wing, for which NASA Glenn Research Center’s Icing Research Tunnel has generated experimental ice shapes for the inboard, mid-span, and outboard sections. The reproductions at various levels of fidelity from detailed 3D scans of these ice shapes have been used in recent aerodynamic testing at the Office National d’Etudes et Recherches Aérospatiales (ONERA) and Wichita State University (WSU) wind tunnels. The ONERA tests were at higher Reynolds number range in the order of 10 million, while the WSU tests were in the order of 1 million.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

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.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

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.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

Reducing the weight of the M1A2 tank by lightweighting hull, suspension, and track results in 5.1%, 1.3%, and 0.6% tank mass reductions, respectively. The impact of retrofitting with lightweight components is evaluated through primary energy demand (PED), cost, and fuel consumption (FC). Life cycle stages included are preproduction (design, prototype, and testing), material production, part fabrication, and operation. Metrics for lightweight components are expressed as ratios comparing lightweighted and unmodified tanks. Army-defined drive cycles were employed and an FC vs. mass elasticity of 0.55 was used. Depending on the distance traveled, cost to retrofit and operate a tank with a lightweighted hull is 3.5 to 19 times the cost for just operating an unmodified tank over the same distance. PED values for the lightweight hull are 1.1 to 2 times the unmodified tank. Cost and PED ratios decrease with increasing distance.

In-vehicle infotainment (IVI) platforms are getting increasingly connected. Besides OEM apps and services, the next generation of IVI platforms are expected to offer integration of third-party apps. Under this anticipated business model, vehicular sensor and event data can be collected and shared with selected third-party apps. To accommodate this trend, Google has been pushing towards standardization among proprietary IVI operating systems with their Android Automotive platform which runs natively on the vehicle’s IVI platform. Unlike Android Auto’s limited functionality of display-projecting certain smartphone apps to the IVI screen, Android Automotive will have access to the in-vehicle network (IVN), and will be able to read and share various vehicular sensor data with third-party apps. This increased connectivity opens new business opportunities for both the car manufacturer as well as third-party businesses, but also introduces a new attack surface on the vehicle.

Due to the increasingly stringent environmental regulations all around the world confronted by exhaust emission and energy consumption, improving fuel economy has been the top priority for most automotive manufacturers. In this context, the basic process for vehicle shape development has evolved into optimizing the design to achieve better aerodynamic characteristics, especially drag reduction. Of all the optimization approaches, the gradient-based adjoint method has currently received extensive attention for its high efficiency in calculating the objective sensitivity with respect to geometry parameters, which is the first and foremost step for subsequent shape modification. In this work, the main goal is to explore the adjoint method through optimizing the vehicle shape for a lower drag based on a production SUV. Firstly, the influence of different mesh schemes was discussed on sensitivity prediction of aerodynamic drag.

This SAE EDGE Research Report explores the many legal issues raised by the advent of automated vehicles. While promised to bring major changes to our lives, there are significant legal challenges that have to be overcome before they can see widespread use. A century’s worth of law and regulation were written with only human drivers in mind, meaning they have to be amended before machines can take the wheel. Everything from key federal safety regulations down to local parking laws will have to shift in the face of AVs. This report undertakes an examination of the AV laws of Nevada, California, Michigan, and Arizona, along with two failed federal AV bills, to better understand how lawmakers have approached the technology. States have traditionally regulated a great deal of what happens on the road, but does that still make sense in a world with AVs? Would the nascent AV industry be able to survive in a world with fifty potential sets of rules?

A CFD simulation methodology for the inclusion of the post-impact trajectories of splashing/bouncing Supercooled Large Droplets (SLDs) and film detachment is introduced and validated. Several scenarios are tested to demonstrate how different parameters affect the simulations. Including re-injecting droplet flows due to splashing/bouncing and film detachment has a significant effect on the accuracy of the validations shown in the article. Validation results demonstrate very good agreement with the experimental data. This approach is then applied to a full-scale twin-engine turboprop to compute water impingement on the wings and the empennage.

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.