In modern warfare, body armor has greatly reduced soldier casualties, yet back-face deformation from high-velocity bullets can still cause severe blunt trauma. Current studies mainly rely on numerical simulations, where accurately defining soft-tissue mechanical properties and constitutive models remains a key challenge. This study fitted a rate-dependent bilinear constitutive model for soft tissue using Hopkinson bar test data and implemented it in ABAQUS through a VUMAT subroutine. A protected human blunt-impact model was then established by combining a validated body-armor model with the Toyota THUMS human model. The model was used to simulate the impact of a 5.8 mm rifle bullet on an armored human thorax. Results show that the proposed constitutive model effectively captures the dynamic mechanical behavior of the heart and liver. Under impact, peak pressures reached 11.2 MPa in the ribs, 0.1 MPa in the lungs, 0.06 MPa in the heart, and 0.23 MPa in the liver, indicating the need for improved torso protection.
This article evaluates the energy absorption of holed steel tubes with a circular cross-section under axial compression. The process was modelled using an FE simulation using ABAQUS software. The numerical results were confirmed by comparison with the experimental results. Afterward, the effect of input parameters on the initial crushing force (ICF) and specific energy absorption (SEA). The experiments were designed using Taguchi's L9 orthogonal array and were numerically performed. The results showed that the diameter of the holes is the most important parameter for the outputs. The angle of 30 degrees, the diameter of 7 mm, and the number of 6 rows of holes will result in the best answer for the ICF. Finally, using the MCDM technique, it was found that a holed tube with an angle of 45 degrees, a diameter of 5 mm, and 6 rows of holes is the best arrangement while simultaneously considering the criteria.
Analysis of heavy vehicle accidents is critical for traffic safety, as such crashes often result in serious injuries, fatalities, material damage, traffic disruptions, and economic losses. This study proposes an integrated approach combining geographic information systems (GIS), multi-criteria decision-making (MCDM), and statistical analysis to examine heavy vehicle accidents in T & uuml;rkiye between 2015 and 2019. The methodology includes: (i) examining accident records and identifying 21 independent variables and 93 sub-variables, (ii) calculating their priority values using the fuzzy Logarithm Methodology of Additive Weights, (iii) conducting GIS-based spatial analyses, and (iv) applying binary logistic regression to evaluate accident outcomes as injury or fatality. The results indicate that road, environmental, accident-related, and driver-related factors significantly affect crash severity. Fuzzy LMAW results show that driver violations have the greatest negative impact. Spatial analyses reveal that severe crashes are mainly concentrated in western, north-western, south-western, and central T & uuml;rkiye.
The influence of vibration on metal and composite materials makes the latter less susceptible to vibration. Utilising composite materials instead of metal panels as the faces of sandwich structure is better than using metal, a hybrid, which makes the sandwich structures perform better. Composite and hybrid materials have little impact on the environment compared to metal. This work involved an experimental and numerical study examining the effects of replacing the faces of metal plates with composite materials, as well as hybrid materials, on vibration in sandwich structures and investigating the impact of adding layers of rubber to sandwich structures. The sandwich structure consists of a core made of PLA with different faces, a plate of aluminium, composite materials (fibreglass and carbon fibre), hybrid materials (nanosilica and nanoalumina), and two layers of rubber. The experimental work covers the manufacturing of the composites, core, and rubber samples and the tests, as well as tensile, three-point bending, and vibration tests.
Accurately predicting occupant collision response curves can reduce reliance on costly physical testing and simulations, playing a key role in advancing vehicle collision safety. Deep learning has gained significant traction and become a cornerstone of modern vehicle safety technologies owing to its fast prediction capabilities. However, existing models frequently overlook the mechanical properties of restraint systems and key spatial parameters, which are challenging to quantify numerically yet critically influence injury outcomes. To compensate for this, we propose a multimodal method that converts these spatial parameters into robust image features, mitigating subjective and measurement errors. Our resulting model (MOIPM) predicts key injury metrics with >0.9 accuracy in merely 3.684 s, a 384.6-fold speed increase over traditional simulations. Crucially, MOIPM outperforms baseline models that lack spatial parameter inputs across all evaluated metrics, demonstrating the value of incorporating image-based spatial representations. This approach establishes a new technical pathway for intelligent vehicle safety.
The use of numerical simulation is essential in vehicle development and particularly in pedestrian safety. Based on simulation results, component modifications are derived and validated through physical testing. The introduction of the advanced Pedestrian Legform Impactor (aPLI) by EuroNCAP presents new challenges, as it affects more front-end components and design parameters than its predecessor, the FlexPLI. Furthermore, development timelines are shortened, particularly in early phases. Consequently, a methodology has been developed to evaluate a multitude of design parameters for the aPLI impact in late development stages to compensate for the reduced development time. Thereby, we minimise differences between the simulation model and the corresponding test by implementing sensitivity-based Finite Element Model Updating (FEMU). Further, we generate a simplified vehicle front-end (spring) model that allows a comprehensive investigation of design parameters. In particular, the accuracy of the spring model for aPLI injury predictions has been confirmed in two relevant use cases.
The safety of aircrew members is critically dependent on the performance of their helmets under high-impact conditions. As helmets must be lightweight yet capable of withstanding substantial G-forces during manoeuvres, the choice of material significantly affects impact properties. This study explores the impact performance and damage tolerance of aircrew helmet shells using hybrid laminates incorporating various high-performance fibres, including Kevlar (TM), carbon, Dyneema and Zylon. By systematically investigating both inter-layer and intra-layer hybridisation techniques, this research seeks to optimise the composite stacking sequence and material composition for enhanced aircrew helmet safety. The findings indicate that hybrid laminates combining Dyneema-Kevlar (TM) (DK) and Zylon-Kevlar (TM) (ZK) (DK/ZK laminate, Sample 10) offer superior impact resistance, lightweight characteristics, and energy absorption properties, specifically achieving the highest total energy absorption of 39.6 J and a high peak force of 3692.951 N at a low weight of 35 g.
Serious traffic accidents from passenger and freight transport often cause serious casualties and property damage. However, the specific mechanisms distinguishing single-vehicle (SV) and multi-vehicle (MV) crashes remain insufficiently explored. This study investigated the primary variables associated with SV and MV serious accidents and examined the factors contributing to different crash types. The scope of this research encompasses 164 accident reports involving passenger and freight transport in China from 2010 to 2021. The Apriori algorithm was utilized to mine strong association rules for SV and MV accidents. The results indicate that in SV accidents, there is a high likelihood of fall-off accidents, which are strongly linked to self-refitting practices and adverse weather conditions. In the case of MV accidents, frontal collisions are frequently observed, with their occurrence correlated with the shedding of vehicle wheels during rainy and snowy conditions. These findings offer safety precautions for traffic departments, transportation companies and drivers.
This study evaluates the feasibility of an Aluminium-Silicon Carbide (Al-SiC) metal matrix composite tubular space-frame chassis for Formula SAE applications, focusing on weight reduction, crashworthiness, stiffness, and cost efficiency compared to conventional materials like chromoly steel. A full-scale FSAE-compliant chassis was designed in SolidWorks and analyzed using finite element methods in ANSYS. Crash performance was assessed under front, side, and rear impacts using equivalent static loads derived from the impulse-momentum approach, examining von Mises stress, deformation, and factor of safety. Results show the Al-SiC chassis safely withstands all impact scenarios, with stresses below yield strength and acceptable deformation. Key results include 123.69 MPa (FOS 2.3) for front impact, 153.83 MPa (FOS 1.8) for side impact, and 266.98 MPa (FOS 1.3) for rear impact. The design achieves similar to 35% weight reduction over steel and similar to 20% over titanium, demonstrating superior stiffness-to-weight performance and full-chassis applicability.
In this study two methods were chosen for the production of composites: vacuum infusion and hand lay-up. The effect of varying the graphene addition method on the mechanical properties of the composite material was investigated. The first method involved adding graphene to the epoxy resin and infusing it into the fibre. The second method involved adding graphene dissolved in an acetone to the fibre. Quasi-static compression tests were applied to the samples, and the resulting energy absorption values (EA), specific energy absorption values (SEA), and peak forces (PF) were compared. The greatest EA was obtained with 200.42 joules with graphene-free epoxy resin matrix glass fibre produced by vacuum-infusion. The lowest EA was obtained with 132.32 joules with the sample produced by hand lay-up. In terms of PF values, the highest result was observed with 6.45 kN in the sample which graphene added in acetone solution.
