Nanotechnology and bio-inspired flame retardants
Novel strategies to combine nano-particles with existing flame retardants through nano-filler technology.
Multi-scale computational models for fire propagation
Multi-scale computational models that capture the flammability properties, thermal degradation, and combustion characteristics of advanced lightweight materials and structures.
Innovative fire suppression and fire control approaches
Develop innovative fire suppression systems as well as practical fire control strategies.
Human behavioural models for crowd movement
Investigate how the evacuation of people in the event of building fire is affected by the human behavioural response to a fire situation subject to the burning of lightweight materials.
Fire Testing and Standards
Establish new fire tests and standards to ensure that more robust fire safety provisions are provided for advanced materials and applications.
MORE ABOUT OUR CENTRE
This Training Centre, which strongly aligns with the Advanced Manufacturing priority, brings together an outstanding team of 39 Chief and Partner Investigators from 27 organisations from Australia, Hong Kong, China, UK and USA to provide high quality training of 19 industry-focused fire researchers. Through joint ARC Discovery, Linkage, and other industry-supported projects, the five Australian universities of this Training Centre (UNSW, RMIT, WSU, USQ and Uni Adelaide) have established strong collaborative research track records in flame retardant materials, performance of structures under fire, and modelling of the propagation process of fire.
New ARC Discovery Project – Engineered interlayers of bio-retardant and nano-reinforcement on polymers
Our ARC Discovery Project (DP220101427) entitled “Engineered interlayers of bio-retardant and nano-reinforcement on polymers” led by CIs Prof Guan Heng Yeoh and Dr Anthony Chun Yin Yuen has been successful (totalling 332.3k). In collaboration with PI Prof Jaime Grunlan @TAMU, we aim to develop a highly effective lightweight coating for polymeric materials, with the aid Read more about New ARC Discovery Project – Engineered interlayers of bio-retardant and nano-reinforcement on polymers[…]
Recently, we have discovered an alternative approach to characterise the morphology of two-dimensional nanosheet structures via atomic physical measurements. This strategy allows us to measure quantitative neutron scattering measurements to comprehensively analysis the shape and dimensions of the nanosheets. As a first study, we attempt to study the thickness and size of a recently arisen Read more about Alternative fundemental material characterisation techniques via neutron beam scattering[…]
Evaluating the fire risk associated with cladding panels: An overview of fire incidents, policies, and future perspective in fire standards
Combining the effort by our research members, we have carried out a review on the past fire scenarios involving external wall systems. Please refer to the following link for this latest publication: https://onlinelibrary.wiley.com/doi/abs/10.1002/fam.2973 Abstract: Multifunctional building façades have become an increasingly critical component in modern buildings, especially after the tremendous scrutiny triggered by the utilization Read more about Evaluating the fire risk associated with cladding panels: An overview of fire incidents, policies, and future perspective in fire standards[…]
ARC Training Center for Fire Safety member Hengrui Liu and alumni Yu Han presented their research works at the International Symposium on Numerical Methods in Heat and Mass Transfer (ISNMHMT2020) in NingBo, China.
Partner with Jacobs Engineering (Dr David Lo Jacono and Jiayun Hao), the Smart Fire Sprinkler Team is the UNSW Maker Games Winners for the year 2020. The unique design of their bushfire prevention and fighting system “EMBER EYE” embraces advanced imaging processing, impact sprinkler technology, digital control algorithms and the effectiveness is verified by computational Read more about Congratulations to the Smart Fire Sprinkler (funded by Jacobs) in winning the Maker Games 2020[…]
Dear Colleagues, Lightweight, high-performance composite materials, such as fibre-reinforced polymers, are rapidly replacing conventional building materials. Nevertheless, these polymeric materials are often associated with life-threatening fire hazards due to the production of toxic substances in the event of fires. Fire incidents caused by these highly flammable polymers have increased dramatically over the last decade. Therefore, Read more about MDPI Molecules Special Issue “New Prospects in Flame-Retardant Materials”[…]
Due to the COVID-19 crisis, stuff and students stay at home to keep social distance. To take advantage of precious time, an online Molecular Dynamic (MD) simulation training workshop was delivered by the ARC Training Centre, which can build new research capacity for the stuff and students and fill the missing knowledge gap. During this Read more about Group Activities – MD Simulation Workshop[…]
Congratulations to Mr Cheng Wang, who has submited his PhD Thesis, for his thesis titled: “The development and implementation of a population balance method-based soot model in diffusion flames”! Cheng is currently working as a research fellow at UNSW.
Dear fellow Colleagues, On behalf of our Centre Director Prof Guan Heng Yeoh, also an advisory committee member of ICPER2020, we proudly invite you to submit your work on production, energy, sustainability and reliability. This conference will be held in the Borneo Convention Centre Kuching (BCCK), Kuching, Malaysisa, on 14-16 July 2020. The paper submission Read more about International Conference on Production, Energy & Reliability (ICPER2020) – Call for papers[…]
Inspired by the advanment of repid development in “machine learning” techniques, we now proposed using genetic algorithm theory to optimise the pyrolysis kinetics input data of any polymer composites for our fire field models (i.e. computational fluid dynamics based). Typically, pyrolysis kinetics is extracted via thermal gravemetry (TGA), where the solid decomposition is studied in Read more about Application of Machine Learning Techniques to Formulate Simulation Inputs for Fire Models[…]