Bushfire-related building losses cause adverse economic impacts to countries prone to bushfires.Building materials and components play a vital role in reducing these impacts.However,due to high costs of experimental s...Bushfire-related building losses cause adverse economic impacts to countries prone to bushfires.Building materials and components play a vital role in reducing these impacts.However,due to high costs of experimental studies and lack of numerical studies,the heat transfer behavior of building’s external components in bushfire-prone areas has not been adequately investigated.Often large-scale heat transfer models are developed using Computational Fluid Dynamics(CFD)tools,and the availability of CFD models for heat transfer in building components improves the understanding of the behavior of systems and systems of systems.Therefore,this paper uses a numerical modeling approach to investigate the bushfire/wildfire resistance of external Light gauge Steel Framed(LSF)wall systems.Both full-scale and small-scale heat transfer models were developed for the LSF wall systems.Experimental results of six internal and external LSF wall systems with varying plasterboard thickness and cladding material were used to validate the developed models.The study was then extended to investigate the bushfire resistance of seven external wall systems under two different bushfire flame zone conditions.The results illustrate the significant effects of fire curves,LSF wall components and configuration on the heat transfer across the walls.They have shown 1)the favorable performance of steel cladding and Autoclaved Aerated Concrete(AAC)panels when used on the external side of wall systems and 2)the adequacy of thin-walled steel studs’load-bearing capacity during bushfire exposures.This study has shown that most of the investigated external LSF walls could be reused with cost-effective retrofitting such as replacing the Fire Side(FS)steel cladding after bushfire exposures.Overall,this study has advanced the understanding of the behavior of external light steel framed walls under bushfire flame zone conditions.展开更多
In the rotary kiln alumina production process, because of the complexity and variability of rotary kiln burning zone conditions, some important quality index related process parameters can not be detected continuously...In the rotary kiln alumina production process, because of the complexity and variability of rotary kiln burning zone conditions, some important quality index related process parameters can not be detected continuously on-line.Detecting the different burning zone conditions on-line is a key factor for the whole process automation of alumina industry. The current method depends on flame observation by naked eye.In order to realize automated recognition of burning zone conditions, a method which learned experience and knowledge from naked eye observation was proposed to recognize burning zone conditions by utilizing the image processing technique and pattern classification method.At first, features were extracted from flame images of rotary kiln burning zone and were combined with some important process parameters to constitute a hybrid feature vector.Then a model with a binary tree based SVM (support vector machine) was constructed.At last, a flame image recognition system was developed.The system was successfully applied to a domestic alumina plant, and good economic benefit was realized.展开更多
基金the Australian Research Council(ARC Grant Nos.DE180101598 and DP200102704)Queensland University of Technology(QUT)for providing financial support.
文摘Bushfire-related building losses cause adverse economic impacts to countries prone to bushfires.Building materials and components play a vital role in reducing these impacts.However,due to high costs of experimental studies and lack of numerical studies,the heat transfer behavior of building’s external components in bushfire-prone areas has not been adequately investigated.Often large-scale heat transfer models are developed using Computational Fluid Dynamics(CFD)tools,and the availability of CFD models for heat transfer in building components improves the understanding of the behavior of systems and systems of systems.Therefore,this paper uses a numerical modeling approach to investigate the bushfire/wildfire resistance of external Light gauge Steel Framed(LSF)wall systems.Both full-scale and small-scale heat transfer models were developed for the LSF wall systems.Experimental results of six internal and external LSF wall systems with varying plasterboard thickness and cladding material were used to validate the developed models.The study was then extended to investigate the bushfire resistance of seven external wall systems under two different bushfire flame zone conditions.The results illustrate the significant effects of fire curves,LSF wall components and configuration on the heat transfer across the walls.They have shown 1)the favorable performance of steel cladding and Autoclaved Aerated Concrete(AAC)panels when used on the external side of wall systems and 2)the adequacy of thin-walled steel studs’load-bearing capacity during bushfire exposures.This study has shown that most of the investigated external LSF walls could be reused with cost-effective retrofitting such as replacing the Fire Side(FS)steel cladding after bushfire exposures.Overall,this study has advanced the understanding of the behavior of external light steel framed walls under bushfire flame zone conditions.
文摘In the rotary kiln alumina production process, because of the complexity and variability of rotary kiln burning zone conditions, some important quality index related process parameters can not be detected continuously on-line.Detecting the different burning zone conditions on-line is a key factor for the whole process automation of alumina industry. The current method depends on flame observation by naked eye.In order to realize automated recognition of burning zone conditions, a method which learned experience and knowledge from naked eye observation was proposed to recognize burning zone conditions by utilizing the image processing technique and pattern classification method.At first, features were extracted from flame images of rotary kiln burning zone and were combined with some important process parameters to constitute a hybrid feature vector.Then a model with a binary tree based SVM (support vector machine) was constructed.At last, a flame image recognition system was developed.The system was successfully applied to a domestic alumina plant, and good economic benefit was realized.
