Browse Topic: Impact tests
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.
With new energy vehicles developing rapidly, battery safety, as an important part of the impact on the range of new energy vehicles and vehicle safety, has become the focus of attention. The battery pack protection plate is a core component to protect the battery, its performance needs not only impact resistance, but also lightweight, honeycomb sandwich structure with its excellent energy absorption characteristics and weight reduction performance by the battery pack protection plate performance research. At present, the core-to-face sheet interaction in conventional sandwich structures subjected to impact loads has not been fully elucidated, and the quantitative characterization of damage is insufficient, so this paper aims to optimize the lightweight impact-resistant structure by exploring the synergistic energy dissipation mechanism between the high-strength core material and the steel plate. The study combines theory and simulation, adopting ideal rigid-plastic film theory to establish a critical response model to predict the structural failure threshold, equivalent single-layer theory to simplify the analysis of plywood, and a stiffness matrix model to quantify the structural mechanical contribution of each layer. A two-material synergistic design framework is proposed by fully considering the material properties and adopting the corresponding intrinsic structure and failure criteria for different materials. Analysis reveals that geometric confinement is a key characteristic of the honeycomb sandwich panel’s response and a strain gradient driving mechanism at low impact resistance, and a new energy distribution paradigm is found through the analysis of the energy absorption ratio. The theoretical and simulation results are in great agreement with each other, which just has a difference of 0.7% in the peak force, 1.4% in the critical displacement error, and less than 2% in the impulse integration error. The proposed dual-material co-design framework provides a solution for electric vehicle battery protection systems that balances lightweight and impact resistance.
The bird strike performance of the flight critical components of a rotorcraft is to be proved. The study investigates the bird strike performance of the cowling structure through experiments and simulations by considering a Building Block Approach. Based on this approach, bird impact tests on a rigid plate and composite panels are performed to validate Smoothed Particle Hydrodynamics method (SPH) bird model and composite material model in LS-DYNA. The composite material properties are obtained from the coupon level test results. After the composite material model is calibrated and validated, the bird strike performance of the cowling structure at critical locations is assessed. A good correlation between the experimental and numerical results was obtained at coupon, sub-component and component levels. The developed composite material modeling technique and validated bird models may be used in showing bird resistances of other airframe components of similar structure of the rotorcraft.
Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were conducted to clarify the underlying mechanisms. When the obstacle height falls below the window frame level, rollover effects become more significant. The longer roll moment arm allows part of the impact energy to be dissipated through vehicle roll motion, leading to a reduction in battery intrusion. However, as the obstacle height is further reduced into the threshold beam and battery side beam region, the supporting structural members are bypassed. The equivalent contact stiffness drops sharply, resulting in significant battery intrusion. The findings demonstrate that obstacle height governs EV battery safety through a competition between rollover energy dissipation and reduced contact stiffness. This work provides new insights for extending existing side impact tests to low-height obstacles and offers guidance for vehicle safety design.
Occupant Safety systems are usually developed using anthropomorphic test devices (ATDs), such as the Hybrid III, THOR-50M, ES-2, and WorldSID. However, in compliance with NCAP and regulatory guidelines, these ATDs are designed for specific crash scenarios, typically frontal and side impacts involving upright occupants. As vehicles evolve (e.g., autonomous layouts, diverse occupant populations), ATDs are proving increasingly inadequate for capturing real-world injury mechanisms. This has led to the adoption of computational Human Body Models (HBMs), such as the Global Human Body Models Consortium (GHBMC) and Total Human Model for Safety (THUMS), which offer superior anatomical fidelity, variable anthropometry, active muscle behaviour modelling, and improved postural flexibility. HBMs can predict internal injuries that ATDs cannot, making them valuable tools for future vehicle safety development. This study uses a sled CAE simulation environment to analyze the kinematics of the HBMs model in a frontal crash scenario. The methodology includes the initial correlation of Hybrid III CAE simulation results with physical sled test data, followed by a comparative analysis with GHBMC M50-O v6-2 based simulations. A significant difference was observed in pelvic forward displacement between the Hybrid III and GHBMC M50-O v6-2. The difference in interaction originates from the difference in the construction of the pelvis between the Hybrid III and GHBMC. In the GHBMC, reduced displacement occurs because the pelvis locks in the seat. This interaction is absent in ATDs, resulting in increased torso rotation and a potential rise in upper extremity injury risk for HBMs. The study examines the various reasons for pelvic locking and increased upper body rotation. These evaluations aim to raise the negative consequences of pelvic locking on upper extremity injuries. The probable solutions that can reduce pelvis locking while preserving occupant stability is also discussed. The study highlights the significance of HBMs in understanding occupant interactions and supports their use in the development of next-generation restraint systems.
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full-frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and positional parameters of the components, along with their force-displacement curves, to estimate the vehicle's crash pulse. In this work, we have implemented PINNs to generate an optimized force scheme using the historical CAE test data. Using this approach promises to cut short the time and cost that goes into the conventional process of target setting.
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical evaluations demonstrate the system's proficiency in classifying occupant states, including normal, distressed, and unconscious conditions, achieving high prediction accuracy with low false positive rates. Notably, the system attains sub-10-second detection latency and facilitates end-to-end response actions within a 5-minute window. Experimental deployment in a Mercedes vehicle demonstrated high accuracy in occupancy detection (97%), vital sign monitoring (94%), and full ERS (Emergency Response System) activation within five minutes, meeting Euro NCAP 2025+ Child Presence Detection (CPD) requirements. Furthermore, the cloud infrastructure supports the accumulation of health data, contributing to personalized driver profiles and informed decision-making for future interventions. This research underscores the potential of UWB radar technology in augmenting automotive safety through real-time health monitoring, paving the way for smarter and more secure vehicular environments.
Severe rear-impact collisions can cause significant intrusion into the occupant compartment when the structural integrity of the rear survival space is insufficient. Intrusion patterns are influenced by impact configuration—underride, in-line, or override—with underride collisions channeling forces below the beltline through the rear wheels as a primary load path. This force concentration rapidly propels the rear seat-pan forward, contacting the rearward-rotating front seatback. The resulting bottoming-out phenomenon produces a forward impulse that amplifies loading on the front occupant’s upper torso, increasing the risk of thoracic injury even when the head is properly supported by the head restraint. This study analyzes a real-world rear-impact collision that resulted in fatal thoracic injuries to the driver, attributed to the interaction between the driver’s seatback and the forward-moving rear seat pan. A vehicle-to-vehicle crash test was conducted to replicate similar intrusion characteristics and assess the relative kinematics between the seatback and rear seat structure. Results demonstrate that seatback bottoming out under intrusion conditions significantly elevates thoracic loading. These findings highlight the need for improved rear structural design strategies to manage load paths in underride scenarios and to minimize front seatback rearward collapse and associated occupant loading.
Rear-facing infant seats that are positioned behind front outboard vehicle seats are at risk of being compromised by the rearward yielding of occupied front seat seatbacks during rear-impact collisions. This movement can cause the plastic shell of the infant seat to collapse and deform, increasing the risk of head injuries to the infant. Current designs of rear-facing infant seats typically do not consider the loading effects from the front seatback during rear-impact situations, which results in weak and collapsible shell structures. Moreover, regulatory compliance tests, such as FMVSS 213, do not include assessments of rear-facing infant seats under realistic rear-impact conditions. as the bench used for the regulatory test lacks realistic vehicle interior components. This study emphasizes the need for revised testing methodologies that employ sled tests with realistic seatback intrusion conditions to facilitate the development of improved infant seat designs. Research shows that rear-facing infant seats designed for real-world loading conditions can improve safety and injury outcomes for infants in severe rear-impact collisions in the presence of front-occupied seats.
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