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As per WHO 2018 report, pedestrian fatalities account for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance highly depends on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface.

Aluminum alloy wheels are being widely used in the automotive industry since the last decade due to its superior styling and performance. Alloy wheel rim is one of the critical components and plays an important role in a frontal crash scenario. The wheel rim failure prediction in safety simulation is essential to ensure robust safety performance. Determining failure characteristics of an alloy wheel poses many difficulties considering its brittle nature, porosity and inhomogeneity in material properties across different regions of wheel rim due to mold design, cooling rate and other process parameters of the low-pressure die casting process. This paper describes the modelling and simulation methodology developed to predict accurate wheel behavior. The methodology addresses two distinct areas of challenges such as alloy wheel rim failure prediction and associated tire blow out.

Pedestrians are a vulnerable road user group, comprising 22% of global road traffic deaths [1]. In Japan, pedestrian fatalities accounted for 28% of total road fatalities and approximately 16% in Australia. These figures compare with 13% for the USA and 40-50% for India and Thailand [2]. Various pedestrian safety requirements are mandated in different markets in recent years worldwide. For pedestrian head-form, vehicle front-end styling and the free package space below bonnet plays a vital role in deciding the pedestrian head-form safety performance. Currently during initial phase of vehicle development, the free package space requirement is decided based on benchmark data. However, the benchmarking data does not give any insight into the physics involved and is subjective in nature as it varies from vehicle to vehicle. This paper gives an analytical approach for defining the free package space requirements for meeting the targeted pedestrian head form performance requirements.

Steering column and steering wheel are critical safety components in vehicle interior environment. Steering system needs to be designed to absorb occupant impact energy in the event of crash thereby reducing the risk of injury to the occupant. This is more critical for non-airbag vehicle versions. To evaluate the steering system performance, Body block impact test is defined in IS11939 standard [1]. Nowadays for product development, CAE is being extensively used to reduce development cycle time and minimize number of prototypes required for physical validation. In order to design the steering system to meet the Body Block performance requirements, a detailed FE model of Body Block impactor is required. The static stiffness and moment of inertia of body block are defined in SAE J244a [2]. The reference data available in SAE J244a is not sufficient to develop a Body Block model that would represent the physical impactor.

The vehicle crash signature (here on referred as crash pulse) significantly affects occupant restraints system performance in frontal crash events. Restraints system optimization is usually undertaken in later phase of product development. This leads to sub-optimal configurations and performance, as no opportunity exists to tune vehicle structure and occupant package layouts. In concept phase of development, crash pulse characterization helps to map occupant package environment with available structure crush space and stiffness. The crash pulse slope, peaks, average values at discrete time intervals, can be tuned considering library of restraints parameters. This would help to derive an optimal occupant kinematics and occupant-restraints interaction in crash event. A case study has been explained in this paper to highlight the methodology.

Computer Aided Engineering (CAE) plays an important role in the product development. Now a days major decisions like concept selection and design sign off are taken based on CAE. All the Original Equipment Manufacturers (OEMs) are putting consistent efforts to improve accuracy of the CAE results. In recent years confidence on CAE prediction has been increased mainly because of good correlation of CAE predictions with the test results. Defining proper correlation criteria and using a systematic approach helps significantly in building the overall confidence level for predictions given by CAE simulations. Representation of manufacturing effects on material properties and material failure in the simulation is still a big challenge for achieving a good CAE correlation. This paper describes side impact test vs CAE correlation. The important parameters affecting the CAE correlation were discussed.