Browse Topic: Safety testing and procedures
Letter from the Guest Editors
Youth bicycle helmet manufacturers often add shell protrusions to make helmets more attractive or fun for children, yet the safety implications of these features are not well understood. While prior work suggests protrusions may affect head impact kinematics, no study has evaluated their effectiveness in location-matched impacts between helmets with and without protrusions. Twelve youth bicycle helmet models, with protrusions intact and removed, underwent matched oblique impacts at three locations and 4.2 m/s to capture resulting peak linear acceleration (PLA) and peak rotational acceleration (PRA). A linear mixed model was used to quantify the effect of protrusion presence on PLA, PRA, and impact duration. The youth bicycle helmet models produced a wide range of kinematics. Protrusion presence had a strong effect on PLA, PRA, and impact duration. PLA averaged 103.0 ± 22.8 g for helmets with protrusions compared to 143.9 ± 21.4 g for helmets without protrusions, while PRA averaged 4432 ± 1570 rad/s2 for helmets with protrusions and 5177 ± 2110 rad/s2 for helmets without protrusions. These findings suggest that helmet protrusions can reduce peak head accelerations in youth bicycle helmet impacts.
Model-Based Systems Engineering (MBSE) has become a mandated practice for Department of Defense acquisition programs, yet measured benefits remain elusive. The 2024 Defense Science Board found that less than one percent of published literature actually quantified MBSE outcomes, and flagship ground vehicle programs such as the XM30 Infantry Fighting Vehicle have experienced schedule delays attributed directly to insufficient proficiency with model-based approaches. This paper presents the Digital Safety Twin concept: an AI-powered safety intelligence architecture that addresses three of the most labor-intensive and error-prone MBSE workflows. First, the architecture uses hybrid natural language processing and large language model (NLP/LLM) pipelines to auto-formalize unstructured natural language documents into formally structured, traceable requirements. Second, it auto-generates and continuously maintains traceability relationships across requirements, design elements, hazard analyses, and verification artifacts. Third, it provides continuous safety case completeness and confidence assessment through automated Goal Structuring Notation (GSN) synthesis connected to live evidence sources. The approach is grounded in Systems-Theoretic Process Analysis (STPA), the OMG Risk Analysis and Assessment Modeling Language (RAAML), MIL-STD-882E system safety practice, and the UL 4600 safety case framework. We present the methodology, its alignment to the DoD Digital Engineering Strategy, and its applicability to ground vehicle autonomy programs including next-generation infantry fighting vehicles and robotic combat vehicles. We also discuss the limitations, risks, and cultural barriers that must be addressed for AI-augmented safety engineering to achieve acceptance in mission-critical defense applications.
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
Wind turbines equipped with large blades significantly enhance power generation efficiency. For large turbines reaching 150 meters in height, concrete towers offer an effective means of cost savings. Nevertheless, the stability of such structures must be carefully considered, given that wind perturbations are amplified with increased height. In this study, we aim to develop a numerical approach for conducting fluid-structure interaction (FSI) analysis on a 150-meter wind turbine concrete tower. A two-way FSI analysis method has been developed using the immersed boundary method, effectively addressing the coupling effects between the structural and fluid models without the need for body-fitted meshing. Our numerical results demonstrate that the proposed method achieves stable convergence and accurately captures dynamic structural responses under high-speed wind conditions. This method will contribute to the numerical design and safety validation of wind turbine infrastructure in engineering projects.
The gathered journalists cover their ears as a loud hydraulic ram begins to load and a warning alarm sounds. Less than a minute later… FOOSH… BAM! A 9.9-pound model of a human head is launched into the front-end structure of a test vehicle, deforming the hood and sending the head bouncing to the ground. The violence of the moment, which is part of pedestrian safety research, is a stark reminder of the seriousness of all the testing that happens at Toyota's North American Research and Development Headquarters in York Township, Michigan. The $187-million (in 2007 dollars) facility includes 180,000 sq.ft. of research and testing space.
Extreme winter weather often leads to ice accretion on transmission lines. Manual removal is inefficient, costly, and poses safety risks. To address this issue, this paper presents the design of a de-icing robot to replace manual operations for transmission line de-icing. The main content focuses on the detailed structural design of the robot, including the mobile platform, de-icing mechanism, and adaptive adjustment module. Finite element simulations are conducted on key components to verify the structural rationality and the correctness of material selection. The proposed de-icing robot enhances the safety of the de-icing process, improves operational efficiency, and provides a valuable reference for transmission line de-icing methods, demonstrating significant practical value.
With the complexity of chemical warfare threats and the diversification of battlefield environments, traditional toxic agent detection methods are facing bottlenecks such as response delays, coverage blind spots, and personnel safety risks. This research focuses on the application of unmanned aerial vehicle (UAV) carried toxic agent sensor systems, aiming to analyze the methods of mounting and deploying the sensors on the UAVs, and to construct a rapid response, high-precision, and highly resistant toxic agent monitoring system. Its significance lies in two aspects: 1. Tactical value: It breaks through the time and space limitations of manual reconnaissance, realizes real-time dynamic perception and early warning of toxic agent contamination, and provides key decision-making support for battlefield command; 2. Application expansion: The research results can be transferred to counter-terrorism, nuclear, biological, and chemical emergency response fields, providing theoretical support and engineering paradigms for the development of unmanned and intelligent chemical defense equipment.
Impact testing utilizing instrumented hammers and accelerometers is a widely adopted technique in dynamic testing. The mass loading effect of the accelerometer alters the dynamic response of the test structure, leading to deviations between the measured frequency response functions (FRFs) and their true values. Furthermore, the effects on the FRFs are contingent upon the positioning of the accelerometer, thereby causing the measured FRFs between two points to fail to meet the principle of reciprocity. This paper investigates the compensation method for the mass of a single accelerometer in impact testing. Compensation formulas for both origin–FRF and cross–FRF are derived using the frequency domain substructure decoupling method. Numerical simulations on a cantilever beam and experimental tests with milling tools validate the proposed methodology. The compensation formulas for FRFs presented in this paper are expected to enhance the measurement accuracy of FRFs in modal testing of small structures, particularly relevant for lightweight components in aerospace, aircraft, and transportation systems, where precise dynamic characterization is critical.
In response to the current airworthiness regulations’ inability to cover the stall flight test requirements under icing conditions of civil aircraft with high-angle-of-attack restriction function and the lack of relevant flight test technologies in China, a study was conducted on the differences in airworthiness provisions for stall characteristics under icing conditions of such aircraft. Key technologies, including simulated ice accretion stall flight test methods, ice installation strategies, and data analysis techniques, are proposed and successfully applied to a specific civil aircraft. The results demonstrate that the methodologies proposed in this paper can effectively support simulated ice accretion stall tests, providing valuable insights for other similar aircraft.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
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