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Showing 2 results for Mohseni Kabir

A. Khalkhali, V. Agha Hosseinali Shirazi, M. Mohseni Kabir,
Volume 3, Issue 2 (6-2013)

One of the most important structural components of engine compartment assembly in a car body is the Srail. S-rails has significant role in absorbing energy during crash events and therefore it is designed for efficient behavior in such conditions. Driving the peak crushing force of the S-rails is one of the important objectives in the design process of such structures. Peak crushing force is exactly the force applied to the downstream components and then will be transferred to the cabin of vehicle. In this paper, closed form solution is performed to drive the peak crushing force of the S-rails. Results of such analytical model finally are compared with the results of finite element simulation. Good agreement between such results shows the accuracy of the proposed analytical model.

M. Mohseni Kabir, M. Izanloo, Ab. Khalkhali,
Volume 7, Issue 2 (6-2017)

Automotive design engineers face the challenging problem of developing products in highly competitive markets. In this regard, using conceptual models in the first step of automotive development seems so necessary. In this paper, to make a body in white conceptual model, an engineering approach is developed for the replacement of beam-like structures, joints, and panels in a vehicle model. The proposed replacement methodology is based on the reduced beam, joint, and panel modeling approach, which involves a geometric analysis of beam member cross-sections and a static analysis of joints. In order to validate the proposed approach, an industrial case-study is presented. Two static load cases are defined to compare the original and the concept model by evaluating the stiffness of the full vehicle under torsion and bending in accordance with the standards used by automotive original equipment manufacturer (OEM) companies. The results show high accuracy of the concept models in comparison with the original model in bending and torsional stiffness prediction.

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