• 文献标题:   Utilising genetic algorithm to optimise pyrolysis kinetics for fire modelling and characterisation of chitosan/graphene oxide polyurethane composites
  • 文献类型:   Article
  • 作  者:   YUEN ACY, CHEN TBY, WANG C, WEI W, KABIR I, VARGAS JB, CHAN QN, KOOK S, YEOH GH
  • 作者关键词:   large eddy simulation, layerbylayer, flame retardant, pyrolysi, combustion, cone calorimeter
  • 出版物名称:   COMPOSITES PART BENGINEERING
  • ISSN:   1359-8368 EI 1879-1069
  • 通讯作者地址:   Univ New South Wales
  • 被引频次:   5
  • DOI:   10.1016/j.compositesb.2019.107619
  • 出版年:   2020

▎ 摘  要

A fire assessment model has been developed to provide a better understanding of the flame propagation, toxic gases and smoke generations of polymer composites. In this study, the effectiveness of the Chitosan/Graphene Oxide layer-by-layer fire retardant coating on flexible polyurethane foam was investigated experimentally and numerically via Cone Calorimetry. To generate quality pyrolysis kinetics to enhance the accuracy of the model, a systematic framework to extract TGA data is proposed involving the Kissinger-Akahira-Sunose method followed by Genetic Algorithm, with less than 5% of RMS error against experimental data. The proposed fire model is capable of predicting and visualising fire development and emitting gas volatiles.