Journal
JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS
Volume 163, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jaap.2022.105500
Keywords
Fire simulation, flame retardant; Genetic algorithm; Machine learning; Large eddy simulation; pyrolysis
Funding
- Australian Research Council (ARC Industrial Training Transformation Centre) [IC170100032]
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The combustibility of insulation and external facade is a critical issue in modern building structures. This study proposes a systematic framework using computational approaches and experimental techniques to assess the potential risks associated with building polymers. The framework includes a five-step procedure and aims to establish a numerical database for up-scaled modeling. The study focuses on the flammability and combustion gases releases of polyethelene (PE) and examines the effect of flame-retardants and carbon-based enhancing agents using CFD modeling.
Combustibility of insulation and external facade remains a critical issue for many countries as they are commonly found in modern building structures. While standardised fire tests can be carried out to examine the flammability limits of building polymers, they cannot inform us of the toxicity and smoke productions of the underlying materials. With recent advancements in computational approaches (i.e. surface regression porous media pyrolysis model as depicted in Fig. 1) and its synergy with experimental characterisation techniques, a systematic framework is proposed to robustly realise the potential risks associated with any building polymer samples. This involves a five-step procedure including (i) performing TGA on the core polymer materials to acquire thermal and gas decomposition data; (ii) extracting pyrolysis kinetics data from TGA results via an initial estimation of the Kissinger-Akahira-Sunose method; (iii) optimise kinetics parameters via a shuffled complex evolution (SCE) algorithms to match DTG curves for an accurate representation of the degradation behaviour; (iv) conduct reduce-scale simulations via computational fluid dynamics (CFD) in a cone calorimeter environment and compare against experimental data with/without aluminium covering for validation; (v) use the validated model to predict gaseous products, toxicity and soot particles releases and benchmark against Cone Calorimeter data. Once this database is established, it can be used for up-scaled modelling in a multiple-storey building setting. In this work, one of the most commonly applied building polymers for insulation applications and external facade systems: polyethelene (PE) is taken as an example to establish a numerical database. To learn whether the effect of flame-retardants that suppresses the flammability and intermediate combustion gases releases of PE can be replicated in the CFD modelling, the PE was also treated by plasma treatment subsequently immersed by graphene oxide as fillers. The reductions in heat, smoke and carbon monoxide releases were benchmarked against the pure PE data while the increased char formation offered by the carbon-based enhancing agent is examined from a numerical perspective.
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