Numerical simulation of decomposition of Polymer Nano-composites: Investigation of the Influence of the Char Structure

In recent years, nano-particles such as nano-clays, carbon nanotubes and graphenes have been extensively used in flame-retardant polymeric materials. The surface char layer formed in combustion acts as protective barriers that limit the heat transfer into the unpyrolysed polymer and volatilization of combustible degradation products and diffusion of oxygen into the material. A numerical simulation tool Thermakin is used to simulate the thermal decomposition of the neat polymers (polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS)) and corresponding nano-composites (PP/multi-walled carbon nanotube (PP/MWCNT) and ABS/ graphene nano-sheets /NiFe-layered double hydroxide hybrid (ABS/GNS-LDH) in cone calorimetry experiments. PP/MWCNT forms a porous network while ABS/GNS-LDH forms a compact, dense char layer during combustion. With appropriate input parameters, the heat release rates (or mass loss rates) are predicted very well. Finally, the effect of input parameters on model outputs are discussed

Numerical Simulation of Decomposition of Polymer Nano-composites: Investigation of the Influence of the Char Structure

AbstractIn recent years, nano-particles such as nano-clays, carbon nanotubes and graphenes have been extensively used in flame-retardant polymeric materials. The surface char layer formed in combustion acts as protective barriers that limit the heat transfer into the unpyrolysed polymer and volatilization of combustible degradation products and diffusion of oxygen into the material. A numerical simulation tool Thermakin is used to simulate the thermal decomposition of the neat polymers (polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS)) and corresponding nano-composites (PP/multi-walled carbon nanotube (PP/MWCNT) and ABS/ graphene nano-sheets /NiFe-layered double hydroxide hybrid (ABS/GNS-LDH) in cone calorimetry experiments. PP/MWCNT forms a porous network while ABS/GNS-LDH forms a compact, dense char layer during combustion. With appropriate input parameters, the heat release rates (or mass loss rates) are predicted very well. Finally, the effect of input parameters on model outputs are discussed

Theoretical and experimental analysis of ceiling-jet flow in corridor fires

In tunnels or long corridors, the combustion products of the fire are confined to spread in one or two directions, forming a ceiling-jet flow. For safety assessment and emergency treatment, it is important to investigate and understand the behavior of the ceiling-jet flow. In this paper, a simple model has been presented, in terms of Richardson number and non-dimensional ceiling-jet thickness, to predict the temperature and the velocity of fire-induced ceiling-jet in a rectangular corridor. Besides, the location of hydraulic jump, occurring in ceiling-jet flow, has been estimated theoretically. In order to validate the theoretical predictions, a series of reduced-scale fire experiments were conducted in a 5 m long corridor. The predicted results, concerning non-dimensional excess temperature, agree favorably with experimental data in different fuels and heat release rates of the fire tests. Finally, the scaling issue has also been discussed and validated. (C) 2011 Elsevier Ltd. All rights reserved

Preparation and characterization of polystyrene/graphite oxide nanocomposite by emulsion polymerization

Abstract Polystyrene intercalated graphite oxide (GO) nanocomposite was prepared by emulsion polymerization reaction and characterized by X-ray diffraction (XRD), high resolution electron microscopy (HREM), and thermogravimetric analysis (TGA). It was shown that polystyrene can be intercalated into the interlayer space of GO and form exfoliated and intercalated nanocomposites. The thermal analysis demonstrated that the presence of GO enhances the char residue of the nanocomposite.

Classification and identification of soot source with principal component analysis and back-propagation neural network

Identification of soot sources is significant in fire investigation and forensic science. In this paper, principal component analysis (PCA) and a back-propagation (BP) neural network model have been used to classify and identify the soot samples from three different kinds of combustible material. Diesel, polystyrene and acrylonitrile butadiene styrene were burnt under the controlled combustion conditions in small-scale burn tests. Based on the matrix data from the GC-MS analysis data, two principal components have been obtained from PCA analysis with the cumulative energy content of 90.21%. Three different kinds of soot sample can be classified with 100% accuracy. A BP neural network model for predicting and identifying the soot source has been further developed. Accurate identification of the unknown samples has been achieved with this trained BP model. This pilot study indicates that PCA and BP neural network methods have potential in the analysis of soot to identify its principle pre-combustion source material. © 2013 Australian Academy of Forensic Sciences