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Abstract - The Warrior Injury Assessment Manikin (WIAMan) was developed to assess injury in Live Fire Test and Evaluation (LFTE) and laboratory development tests of vehicles and vehicle technologies subjected to underbody blast (UBB) loading. While UBB events impart primarily vertical loading, the occupant location in the vehicle relative to the blast can result in some inherent non-vertical, or off-axis loading. In this study, the WIAMan Technology Demonstrator (TD) was subjected to 18 tests with a 350g, 5-ms time duration drop tower pulse using an original equipment manufacturer (OEM) energy attenuating seat in four conditions: purely vertical, 15° forward tilt, 15° rearward tilt, and 15° lateral tilt to simulate the partly off-axis loading of an UBB event. The WIAMan TD showed no signs of damage upon inspection. Time history data indicates the magnitude, curve shape, and timing of the response data were sensitive to the off-axis loading in the lower extremity, pelvis, and spine.

We describe how we apply the SAE AS 5506 Architecture and Analysis Design Language (AADL) [4] to reason about contextual and architectural concerns for cyber security. A system’s cyber security certification requires verification that the system’s cyber security mechanisms are correct, non-bypassable, and tamper-resistant. We can verify correctness by examining the mechanism itself, but verifying the other qualities requires us to examine the context in which that mechanism resides. Understanding that context and validating the system’s evolving design against that context is an objective for the Architecture Centric Virtual Integration Process (ACVIP), an AADL-based approach to model and detect system design defects before they become too costly to fix. We describe our work to apply AADL to assess non-bypassability and tamper-resistance. The results of our research - tool plugins for cyber security architectural validation - support system developers today in their ACVIP activities.

Game-changing opportunities abound for the application of vehicle health management (VHM) across multiple transportation-related sectors, but key unresolved issues continue to impede progress. VHM technology is based upon the broader field of advanced analytics. Much of traditional analytics efforts to date have been largely descriptive in nature and offer somewhat limited value for large-scale enterprises. Analytics technology becomes increasingly valuable when it offers predictive results or, even better, prescriptive results, which can be used to identify specific courses of action. It is this focus on action which takes analytics to a higher level of impact, and which imbues it with the potential to materially impact the success of the enterprise. Artificial intelligence (AI), specifically machine learning technology, shows future promise in the VHM space, but it is not currently adequate by itself for high-accuracy analytics.

The “holy grail” for prognostics and health management (PHM) professionals in the aviation sector is to have integrated vehicle health management (IVHM) systems incorporated into standard aircraft maintenance policies. Such a change from current aerospace industry practices would lend credibility to this field by validating its claims of reducing repair and maintenance costs and, hence, the overall cost of ownership of the asset. Ultimately, more widespread use of advanced PHM techniques will have a positive impact on safety and, for some cases, might even allow aircraft designers to reduce the weight of components because the uncertainty associated with estimating their predicted useful life can be reduced. We will discuss how standard maintenance procedures are developed, who the various stakeholders are, and – based on this understanding - outline how new PHM systems can gain the required approval to be included in these standard practices.

In recent years, the increasing complexity of modern aerospace systems has driven the rapid adoption of robust Model-Based Systems Engineering (MBSE). MBSE is a development methodology centered around computational models, which are instrumental in supporting the design and analysis of intricate systems. In this context, the Architecture Analysis and Design Language (AADL) and Systems Modeling Language (SysML) are two prominent modeling languages for specifying and analyzing the structure and behavior of a cyber-physical system. Both languages have their own specific use cases and tool environments and are typically employed to model different aspects of system design. Although multiple software tools are available for transforming models from one language to another, their effectiveness is limited by fundamental differences in the semantics of each language.

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

This paper describes an approach to integrating high-fidelity vehicle dynamics with a high-fidelity gaming engine, specifically with respect to terrain. The work is motivated by the experimental need to have both high-fidelity visual content with high-fidelity vehicle dynamics to drive a motion base simulator. To utilize a single source of terrain information, the problem requires the just-in-time sharing of terrain content between the gaming engine and the dynamics model. The solution is implemented as a client-server with the gaming engine acting as a stateless server and the dynamics acting as the client. The client is designed to actively maintain a locally cashed terrain grid around the vehicle and actively refresh it by polling the server in an on-demand mode of operation. The paper discusses the overall architecture, the protocol, the server, and the client designs. A practical implementation is described and shown to effectively function in real-time.

This paper investigates the fuel saving potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the Lithium-ion battery pack in the hybridized configuration. On the other hand, the powertrain supervisory control optimization finds the most efficient way to split power demands between the battery pack and the engine. Most of the previous literatures implement them separately. In contrast, combining the sizing and energy management problem into a single optimization problem produces the global optimal solution. This study proposes a novel unified framework to couple Genetic Algorithm (GA) with Pontryagin’s Minimum Principle (PMP) to determine the battery pack sizing and the power split control sequence simultaneously.

The second-life use of batteries from electric vehicles (EV) represents an excellent and cost-effective option for energy storage applications, including the control of fluctuations in energy supply and demand or in combination with solar photovoltaic and wind turbine. Indeed, these batteries are normally replaced from EV use before the end of their service life, when they still have 70-80% of the original capacity. Depending on the cell chemistry and the specific design, such batteries can still be employed in less stressful applications than the automotive one, including commercial, residential, and industrial applications. With the aim to promote the transition to a circular closed-loop economy for spent traction batteries, this study consists in a systematic literature review of available options for reusing EV batteries as a storage system in a factory environment, highlighting benefits and critical aspects.

Success in metal additive manufacturing (AM) relies on the optimization of a large set of process parameters to achieve materials whose properties and performance meet design and safety requirements. Despite continuous improvements in the process over the years, the quality of AM parts remains a major concern for manufacturers. Today, researchers are starting to move from discrete geometry-dependent build parameters to continuously variable or dynamically changing parameters that are geometry- and scan-path aware. This approach has become known as “feedforward control.” Process Control for Defect Mitigation in Laser Powder Bed Fusion Additive Manufacturing discusses the origins of feedforward control, its early implementations in AM, the current state of the art, and a path forward to its broader adoption. Click here to access the full SAE EDGETM Research Report portfolio.

In alignment with the U.S. Army's Climate Strategy and the broader trend in automotive technology, there is a strategic shift towards electrification and hybridization of the vehicle fleet. While a major goal of this effort is to mitigate the carbon footprint of the U.S. Army's vehicle operations, this transition also presents an opportunity to harness advancements in automotive electrification. Among the key vehicles in focus are tactical wheeled vehicles, which provide military forces with versatile and rugged transportation solutions for various combat scenarios, ensuring mobility, protection, and adaptability on the battlefield. This study investigates the potential of electrified tactical wheeled vehicles by conducting a survey involving a diverse group of vehicle operators across various ranks within the U.S. Army.

The advancement of both sensory and unmanned technology, combined with increased utilization of autonomous platforms in complex teaming scenarios, has created a need for practical design space exploration tools to aid in the synthesis of effective System-of-Systems (SoS). The presented work describes a modular, flexible, and extensible framework, referred to herein as the Technologies and Teaming Evaluation (TATE) framework, for straightforward identification of high-quality SoS, which may include both manned and autonomous elements, through quantitative evaluation of system-level and SoS-level attributes against a set of user-defined reference tasks.

Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality.

Mobile heavy article tilter and positioner (HATP) is special purpose vehicle designed to level, articulate and positioning of very heavy load within the accuracy of arc minutes and in a stipulated time in fully auto mode. HATP system uses sophisticated electronic controller system to carry out required task in auto mode. This electronic controller system comprises of various types of electronic hardware, software, sensors and actuators. As this system is dealing with heavy load, any failure in any of subsystem of HATP can result into catastrophe. Therefore active and passive safety measure at various levels must be incorporated into system which firstly prevents the failure and reduce the effect of failure. The safety system for HATP system has been divided in three major levels: 1. Access level safety 2. Operational safety 3. Preventive safety. All three levels of safety is incorporated at appropriate subsystem based on Risk Priority Number (RPN) and failure mode effect analysis.

As the complexity of systems expands with increasing emphasis for digital transformation, the aerospace industry is generating big data to meet customer requirements. The ability to that data to solve challenging problems is limited by many factors, including the capabilities of current classical computing systems. Impact of Quantum Computing in Aerospace discusses how quantum computing systems offer (possibly quadratic to exponentially) greater computational power over classical computers. The power of quantum computing is tremendous and has many potential impacts on the aerospace industry; however, there are also many unsettled topics surrounding the future of the technology. Click here to access the full SAE EDGETM Research Report portfolio.

Project Oculus is an in-flight deployable mechanical arm/pod system that will accommodate 500 pounds of sensor payload, developed for a C-130 military aircraft. The system is designed for use in counter narco-terrorism and surveillance applications by the Department of Defense and the National Guard [1]. A prototype of the system has been built and is in the testing/analysis phase. The purpose of this study was to analyze the actual stresses and strains in the critical areas found using previous Finite Element (FE) simulations and to ensure that acceptable safety requirements have been met. The system components tested will be redesigned, tested, and reconstructed in the case of unacceptable safety factors or if more reliable methods can be implemented. The system was built to be deployed and retracted in flight, to avoid causing any problems in take off and landing.

Tradespace exploration (TSE) describes the activity occurring early in the design process through which stakeholders explore a broad solution space in search of more-optimal alternatives. In doing so, these stakeholders attempt to maximize the utility inherent in the chosen solution while understanding the tradeoffs and compromises that may be required to find an acceptable solution. In the field of vehicle design, tradespaces are often comprised of vast amounts of alternatives which increases the complexity of the decision-making process. Additionally, the number of stakeholders has grown, as decision-makers seek to include more variety in both perspectives and expertise. As such, decision-making stakeholders can often find themselves working at odds and attempting to maximize vastly different objectives in the process. One way to rectify these contrasting viewpoints can be to intentionally introduce a group framing prior to the start of decision making.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

After manufacture, every military vehicle experiences a unique history of dynamic loads, depending on loads carried, missions completed, etc. Damage accumulates in vehicle structures and components accordingly, leading eventually to failures that can be difficult to anticipate, and to unpredictable consequences for mission objectives. The advent of simulation-based fatigue life prediction tools opens a path to Digital Twin based solutions for tracking damage, and for gaining control over vehicle reliability. An incremental damage updating feature has now been implemented in the Endurica CL fatigue solver with the aim of supporting such applications for elastomer components. The incremental updating feature is demonstrated via the example of a simple transmission mount component. The damage state of the mount is computed as it progresses towards failure under a series of typical loading histories.