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Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries.

Nowadays, rolling resistance sits at the core of tyre development goals because of its considerable effect on the car’s fuel economy. In contrast to the experimental method, the finite element (FE) method offers an inexpensive and efficient estimation technique. However, the FE technique is yet to be a fully developed product particularly for rolling-resistance estimation. An assessment is conducted to study the role of material viscoelasticity representation in FE, in linear and non-linear forms, through the use of Prony series and parallel rheological framework (PRF) models, respectively, on the tyre’s rolling-resistance calculation and its accuracy. A unique approach was introduced to estimate the rolling resistance according to the tyre’s hysteresis energy coefficient.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

Occupant stature and body shape may have significant effects on injury risks in motor vehicle crashes, but the current finite element (FE) human body models (HBMs) only represent occupants with a few sizes and shapes. Our recent studies have demonstrated that, by using a mesh morphing method, parametric FE HBMs can be rapidly developed for representing a diverse population. However, the biofidelity of those models across a wide range of human attributes has not been established. Therefore, the objectives of this study are 1) to evaluate the accuracy of HBMs considering subject-specific geometry information, and 2) to apply the parametric HBMs in a sensitivity analysis for identifying the specific parameters affecting body responses in side impact conditions. Four side-impact tests with two male post-mortem human subjects (PMHSs) were selected to evaluate the accuracy of the geometry and impact responses of the morphed HBMs.

Despite the increasing use of carbon fibre reinforced plastic (CFRP) composites, titanium and aluminium alloys still constitute a significant proportion of modern civil aircraft structures, which are primarily assembled via mechanical joining techniques. Drilling of fastening holes is therefore a critical operation, which has to meet stringent geometric tolerance and integrity criteria. The paper details the development of a three-dimensional (3D) finite element (FE) model for drilling aerospace grade aluminium (AA7010-T7451 and AA2024-T351) and titanium (Ti-6Al-4V) alloys. The FE simulation employed a Coupled Eulerian Lagrangian (CEL) technique. The cutting tool was modelled according to a Lagrangian formulation in which the mesh follows the material displacement while the workpiece was represented by a non-translating and material deformation independent Eulerian mesh.

The objective of this study is to develop a method that uses a combination of field data analysis, naturalistic driving data analysis, and computational simulations to explore the potential injury reduction capabilities of integrating passive and active safety systems in frontal impact conditions. For the purposes of this study, the active safety system is actually a driver assist (DA) feature that has the potential to reduce delta-V prior to a crash, in frontal or other crash scenarios. A field data analysis was first conducted to estimate the delta-V distribution change based on an assumption of 20% crash avoidance resulting from a pre-crash braking DA feature. Analysis of changes in driver head location during 470 hard braking events in a naturalistic driving study found that drivers’ head positions were mostly in the center position before the braking onset, while the percentage of time drivers leaning forward or backward increased significantly after the braking onset.

The aging population is a growing concern as the increased fragility and frailty of the elderly results in an elevated incidence of injury as well as an increased risk of mortality and morbidity. To assess elderly injury risk, age-specific computational models can be developed to directly calculate biomechanical metrics for injury. The first objective was to develop an older occupant Global Human Body Models Consortium (GHBMC) average male model (M50) representative of a 65 year old (YO) and to perform regional validation tests to investigate predicted fractures and injury severity with age. Development of the GHBMC M50 65 YO model involved implementing geometric, cortical thickness, and material property changes with age. Regional validation tests included a chest impact, a lateral impact, a shoulder impact, a thoracoabdominal impact, an abdominal bar impact, a pelvic impact, and a lateral sled test.

This paper presents a statistical characterisation of the effects of variations in vehicle parameters on vehicle road load data using a quarter vehicle as a case study. A model of a quarter vehicle test rig constructed from a commercial SUV is created in a multi-body dynamics (MBD) simulation environment to reproduce the real-life behaviour of the SUV. The model is thereafter validated by correlating the response data collected from both the model and laboratory test rig to the same road input. In order to ensure that only the effects of the variation of the vehicle parameters are captured, a time domain drive signal for a kerb strike road event on the physical vehicle is generated from the proving ground data collected during durability testing of the vehicle.

The development of intelligent tire technology from concept to application covers multi-disciplinary fields. During the course of development, the computational method can play a significant role in understanding tire behavior, assisting in the design of the intelligent tire prototype system and in developing tire parameters estimation algorithm, etc. In this paper, a finite element tire model was adopted for developing a strain-based intelligent tire system. The finite element tire model was created considering the tire's composite structure and nonlinear properties of its constituent materials, and the FE model was also validated by physical tests. The FE model is used to study tire strain characteristics by steady state simulation for straight line rolling, traction and braking, as well as cornering. Tire loading conditions were estimated by feature extraction and data fitting.

There is an increasing recognition of injector deposit (ID) formation in fuel injection equipment as direct injection spark ignition (DISI) engine technologies advance to meet increasingly stringent emission legislation and fuel economy requirements. While it is known that the phenomena of ID in DISI engines can be influenced by changes in fuel composition, including increasing usage of aliphatic alcohols and additive chemistries to enhance fuel performance, there is however still a great deal of uncertainty regarding the physical and chemical structure of these deposits, and the mechanisms of deposit formation. In this study, a mechanical cracking sample preparation technique was developed to assess the deposits across DISI injectors fuelled with gasoline and blends of 85% ethanol (E85).

Recent developments in diesel engines lead to increased fuel efficiency and reduced exhaust gas temperature. Therefore more energy efficient aftertreatment systems are required to comply with tight emission regulations. In this study, a computational fluid dynamics package was used to investigate the thermal behaviour of a diesel aftertreatment system. A parametric study was carried out to identify the most influential pipework material and insulation characteristics in terms of thermal performance. In the case of the aftertreatment pipework and canning material effect, an array of different potential materials was selected and their effects on the emission conversion efficiency of a Diesel Oxidation Catalyst (DOC) were numerically investigated over a driving cycle. Results indicate that although the pipework material's volumetric heat capacity was decreased by a factor of four, the total emission reduction was only considerable during the cold start.

Dieseline combustion as a concept combines the advantages of gasoline and diesel by offline or online blending the two fuels. Dieseline has become an attractive new compression ignition combustion concept in recent years and furthermore an approach to a full-boiling-range fuel. High speed imaging with near-parallel backlit light was used to investigate the spray characteristics of dieseline and pure fuels with a common rail diesel injection system in a constant volume vessel. The results were acquired at different blend ratios, and at different temperatures and back pressures at an injection pressure of 100MPa. The penetrations and the evaporation states were compared with those of gasoline and diesel. The spray profile was analyzed in both area and shape with statistical methods. The effect of gasoline percentage on the evaporation in the fuel spray was evaluated.

Sloped medians provide a run-off area for errant vehicles so that they can be safely stopped off-road with or without barriers placed in the sloped median. However, in order to optimize the design of sloped medians and the containment barriers, it is essential to accurately model the behavior of vehicles on such sloped terrain surfaces. In this study, models of a vehicle fleet comprising a small sedan and a pickup truck and sloped terrain surface are developed in CarSim™ to simulate errant vehicle behavior on sloped median. Full-scale crash tests were conducted using the vehicle fleet driven across a 9.754 meters wide median with a 6:1 slope at speeds ranging from 30 to 70 km/h. Measured data such as the lateral accelerations of the vehicle as well as chassis rotations (roll and pitch) were synchronized with the vehicle motion obtained from the video data.

Aluminum metal matrix composites reinforced with particulates have attracted much attention in the automotive industry, due to their improved wear resistance in comparison to aluminum alloys, in recent years. The wear behavior is the critical factor influencing the product life and performance in engineering components. Carbon nanotubes (CNT) are one of the most promising candidates of reinforcements used to improve mechanical strength such as wear in metal matrix composites (MMCs). However, in industrial applications, wear tests are relatively expensive and prolonged. As a result, for several years, research has been increasingly concentrated on development of wear prediction models. In this study, prediction of wear behavior of aluminum (Al) matrix (MMCs) reinforced with different amounts (0, 0.5, 1 and 2 wt%) of CNTs was investigated. A nonlinear autoregressive exogenous (NARX) model structure was chosen for the modeling.

Lateral impact tests were performed using seven male post-mortem human subjects (PMHS) to characterize the force-deflection response of contacted body regions, including the lower abdomen. All tests were performed using a dual-sled, side-impact test facility. A segmented impactor was mounted on a sled that was pneumatically accelerated into a second, initially stationary sled on which a subject was seated facing perpendicular to the direction of impact. Positions of impactor segments were adjusted for each subject so that forces applied to different anatomic regions, including thorax, abdomen, greater trochanter, iliac wing, and thigh, could be independently measured on each PMHS. The impactor contact surfaces were located in the same vertical plane, except that the abdomen plate was offset 5.1 cm towards the subject.

Disc brake noise is recognized as a major problem of the automotive industry. Various experimental and numerical techniques have been developed to model the noisy brake and investigate possible solutions. Developing a virtual model of the disc brake which can accurately reproduce the behavior of the brake unit under different conditions is a considerable step forward towards reaching this goal. Among various aspects of the analytical model of a disc brake, application of the correct value of damping based on the material properties and functional frequency range of each component is a significant factor in ensuring correct prediction of the brake system behavior. Complex Eigenvalue Analysis is well established as a tool for predicting brake instabilities which can potentially lead to brake noise. However, it is known to over-predict instabilities i.e. predict instabilities which do not occur in the real brake system.

In the automotive industry, engine downsizing has been widely accepted as an enabler to improving the fuel economy and reducing the CO₂ emissions. The Atkinson cycle is one of the key technologies. In this paper, the Atkinson cycle with different expansion ratios are compared and analyzed. The investigation is compared with the benchmark whose expansion and compression ratio are identical. The aim is to understand the inherent characteristics of the over-expansion and its effect on the engine performance and emissions. The simulation results show that, the Atkinson cycle produces higher efficiency due to over-expansion. The Atkinson cycle has higher internal EGR compared with the benchmark at equivalent conditions, which contributes to lower the NOx and CO emissions.

Computational Fluid Dynamics (CFD) is used to simulate chemical reactions and transport phenomena occurring in a single channel of a honeycomb-type automotive catalytic converter under lean burn combustion. Microkinetic analysis is adopted to develop a detailed elementary reaction mechanism for propane oxidation on a silver catalyst. Activation energies are calculated based on the theory of the Unity Bond Index-Quadratic Exponential Potential (UBI-QEP) method. The order-of-magnitude of the pre-exponential factors is obtained from Transition State Theory (TST). Sensitivity analysis is applied to identify the important elementary steps and refine the pre-exponential factors of these reactions. These pre-exponential factors depend on inlet temperatures and propane concentration; therefore optimised pre-exponential factors are written in polynomial forms. The results of numerical simulations are validated by comparison with experimental data.

The use of accurate tire material properties is a major requirement for conducting a successful tire analysis using finite element method (FEM). Obtaining these material properties however poses a major challenge for tire modelers and researchers due to the complex nature of tire material and associated proprietary protections of constituent material properties by tire manufactures. In view of this limitation, a simple and effective procedure for generating tire materials data used in tire finite element analysis (FEA) is presented in this paper. All the tire test specimens were extracted from a tire product based on special considerations such as specimen dimension and shape, test standard, precondition of specimen and test condition for cords. The required material properties of tire rubber component, including hyperelasticity and viscoelasticity were obtained using simple uni-axial tension test.