Parameter identification for a low-density-foam material model using numerical optimisation procedures

Abstract

Purpose – In the event of a crash involving a car, its seats, together with their backrests and head supports, ensure the safety of the passengers. The filling material used for such a car seat is normally made of polyurethane foam. To simulate the behaviour of the seat assembly during a crash, the material characteristics of the seat-filling foam should be appropriately modelled. The purpose of this paper is to present a method, with which the proper parameter values of the selected material model for the seat-filling foam can be easily determined. Design/methodology/approach – In the study, an experiment with the specimen from seat-filling foam was carried out. The results from this experiment were the basis for the determination of the parameter values of the low-density-foam material model, which is often used in crash-test simulations. Two different numerical optimisation algorithms – a genetic algorithm and a gradient-descent algorithm – were coupled with LS-DYNA explicit simulations to identify the material parameters. Findings – The paper provides comparison of two optimisation algorithms and discusses the engineering applicability of the results. Originality/value – This paper presents an approach for the identification of the missing parameter values of the highly non-linear material model, if these cannot be easily determined directly from experimental data.