Design and Simulation of a Crash Energy Absorption System Integrated with Magneto-Rheological Absorber

Archakam, Pavan Kumar; Muthuswamy, Sreekumar

doi:10.1007/s42417-021-00318-6

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…It also suggests potential countermeasures to resolve the problem, such as developing a control system or using materials such as magnetorheological elastomer (MRE) to absorb the impact on the front bumper. Other related studies that have explored these solutions include Archakam and Muthuswamy [11] and Rahmat et al [12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Modeling of Crumple Zone in Vehicle Crash Simulation Using Modified Kamal Model Optimized with Gravitational Search Algorithm

Zubir,

Hudha,

Kadir

et al. 2023

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The effectiveness of a vehicle crash system depends on how well it can simulate the behavior of a real vehicle in a crash scenario and accurately identifies the correct working limits of the model parameters, including mass, spring, and damper. Therefore, this study explores the modelling vehicle front crumple zone to represent the behaviors of real crash scenario. The modelling process using Kamal approach is used to develop a precise vehicle crash model for analyzing the impact of a collision on both the vehicle and its passengers. In this study, a complex mass-spring-damper system representing the front crumple zone of an actual car is re-designed to modify the existing vehicle crash model. The gravitational search algorithm (GSA) is implemented in the simulation model's code to obtain optimized values of damping coefficient (c) and spring constant (k). The simulation results show that the deformation response of crumple zone and the deceleration response of vehicle body match the experimental results, indicating the model's accuracy. Additionally, this study investigates the effects of varying the GSA parameters' number of agents (N), the beta parameter (β), and the gravitational constant (G) to improve the model's accuracy by minimizing the root mean square error (RMSE) between model response and crash test data. The optimal GSA parameter chosen in this study were N = 50, β = 0.3, and G = 20 with the lowest RMSE of 22.3874, 22.26664, and 23.86638 respectively.

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Section: Introductionmentioning

confidence: 99%