消防服用织物热防护性能与服用性能的研究
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摘要
本文从热防护性能和服用舒适性能出发,对国内外常用消防服面料的性能进行比较研究,探讨影响织物热防护性能和舒适性能的因素;并模拟消防服的多层复合系统,对各层织物进行不同的组合试验,以寻求舒适性与功能性最佳的消防服多层织物组合系统。
文中选择了国内外常用消防服外层织物,如NomexⅢA、PBI/Kevlar、Kermel、芳砜纶、芳纶、阻燃棉等十二种阻燃织物,防水透湿层分别为Nomex、阻燃棉与PTFE(聚四氟乙烯)膜的层压织物,隔热层为Nomex毡、芳砜纶毡和芳纶毡。论文首先对各层织物的阻燃性、热稳定性、表面抗湿性能、拉伸性能、撕裂性能、透湿性、耐静水压等性能进行了测试。
为了模拟消防服的多层复合系统,以外层织物、防水层织物及隔热层织物作为试样,利用混合正交设计方法进行试样组合。对不同组合织物的热防护性能(TPP值)和透湿性能(WVTR)进行了测试,并利用极差分析、方差分析方法对实验结果进行了统计处理,以探讨阻燃外层、防水透湿层和隔热层织物对组合织物系统整体热防护性能和舒适性能的影响。通过分析得到:对于组合织物系统的热防护性能,各层织物的影响程度为:外层大于防水层,防水层大于隔热层;对于组合织物系统的透湿性能,各层织物的影响程度为:防水层大于隔热层,隔热层大于外层。并且通过方差分析得到:在90%置信水平下,外层织物和防水层织物分别对组合织物系统的热防护性能和透湿性能具有显著的影响。
其次,对外层织物热防护性能的影响因素进行了分析。对于消防服用外层阻燃织物,其纤维的极限氧指数越大,则相应的织物越不易燃烧,热防护性能也越好。对于纤维组成相同的织物,其TPP值与织物的厚度、面密度具有显著的线性正相关性,即织物越厚重,TPP值越大,热防护性能也越好。除之以外,总热流量的大小及热源构成也对织物热防护性能有一定的影响,总热流量越大,造成模拟皮肤二度烧伤的时间越短;相反,总热流量越小,造成模拟皮肤二度烧伤的时间越长。而且,在84Kw/m~2高热量暴露下,对流/辐射(70/30)热源下织物的TPP值大于对流/辐射(50/50)热源下的TPP值,这说明辐射热的比例越大,皮肤越易受到损伤。
其次,论文讨论了不同热流量下水分对多层组合织物热防护性能的影响。以NI虚拟仪器为平台,以图形化编程语言Labview为工具开发了虚拟温度测试仪,并对织物中的含水量设计了四种不同的湿润状态。研究了不同强度和性质的热源下,外层织物水分的多少对多层组合织物热防护性能的影响。通过实验得到:在16.8 kw/m~2低辐射热流量暴露下,对于外层不同湿润状态的组合织物,模拟皮肤到达二度烧伤的时间为:33%水湿润状态时织物到达二度烧伤的时间最短,皮肤损伤最严重,织物66%水湿润状态时,二度烧伤时间与非湿润时相近;织物100%饱和湿润状态时热防护性能较非湿润时好。而在84 kw/m~2(50/50对流/辐射)高热流量暴露条件下,对于所有组合试样,湿润状态下的织物TPP值大于非湿润织物,即水分使织物的热防护性能上升。
最后,利用人工神经网络技术,建立了基于Matlab的织物物理性能与热防护性能之间的自适应性神经网络预测模型,并对模型进行仿真及验证。以外层织物的厚度、面密度、组织结构、经密、纬密、紧度、导热系数、极限氧指数、损毁长度共九个性能指标作为BP神经网络输入层的输入向量,输出向量为织物的TPP值,建立了消防服用外层织物的热防护性能预测模型。经过试样的训练和模型预测能力的验证,实际值与预测值的相关系数为0.952,预测平均误差为3.93%。通过比较分析得到,运用神经网络模型比线性回归模型能更精确地预测外层织物的热防护性能。
对于多层组合织物系统,以外层、防水层和隔热层织物的十二个物理指标作为BP神经网络的输入向量,输出向量为多层组合织物的TPP值,中间隐含层神经元个数为六,建立了消防服用多层组合织物的热防护性能预测模型。经过试样的训练和模型预测能力的验证,实际值与预测值的相关系数为0.962,预测平均误差为4.11%。因此,运用该模型能准确地预测多层组合织物的热防护性能。
Fire-fighter protective clothing is of great importance to firemen, as they are routinely exposed to heat and sometimes contacted with flames. So protective clothing should be comfortable and provide protection against fire and heat. Therefore, the objective of this paper is to study the thermal protective performance and comfort of firefighter-clothing fabrics and the factors which affect the thermal protective performance and comfort of fabrics. It is also to investigate optimal assembly of the overall materials in terms of their heat protection and moisture transmission.
In this paper, twelve outer fabrics such as NomexIIIA, PBI/Kevlar, Kermel, polysulfonamide, flame-resistant cotton, etc, are selected and two moisture barriers with PTFE, and three thermal liners are studied. The physical properties of each fabric are tested.
To simulate three-layer firefighter protective clothing system, the appropriate fabrics for firefighter protective clothing are selected, and the orthogonal design method is used. Thermal protective performance and moisture transmission of firefighter clothing are studied through different material combinations. Then experiment data are discussed by range analysis and variance analysis to study the effects of three layers for heat protection and moisture comfort. Results show that the priority order of three factors for TPP rating is outer fabric> moisture barrier> thermal liner, and the priority order of three factors for WVTR value is moisture barrier> thermal liner>outer fabric. Outer fabric and moisture barrier have significant effects on thermal protective performance and moisture transmission at 90 percent confidence level respectively.
Based on the above experiments, factors which influence thermal protective performance on outer fabrics are studied. Results show that fiber content, mass weight, and thickness of fabrics are dominant factors determining thermal protective performance in high intensity. Besides, exposure conditions such as intensity of the heat and property of the heat also affect thermal protective performance. With the greater heat exposure, time of the 2~(nd) degree burn is shorter. With 84Kw/m~2 mixtures of convective/radiant heat exposure, the TPP rating of 70/30 condition is better than that of 50/50 condition.
The effect of moisture on thermal protective performance is investigated in different radiant and convective heat exposures. Based on NI virtual instrument and Labview graphical programming language, a virtual instrument determining temperature has been developed. And four fabric preparations are evaluated. At the 16.8kw/m~2 radiant exposure, three-layer assemblies sprayed with little water (33%) on outer fabrics provide the weakest thermal protection. Time of the 2~(nd) degree burn for assemblies which are sprayed with much water (66%) on outer fabrics is similar to the assemblies without water. And three-layer assemblies with outer fabric soaked with water (100%) provide the greatest thermal protection. However, moisture enhances the thermal protection of three-layer assemblies at the 84kw/m2 convective and radiant exposure.
At last, a nonlinear correlation between fabric physical properties and thermal protective performance is developed in this paper. Based on Matlab neural network toolbox, BP neural networks using to predict the thermal protective performance of fabrics are developed. For outer heat resistant fabric (single-layer), a BP neural network with a single hidden layer is constructed including nine input nodes, eleven hidden nodes, and one output node. The input variables are mass weight, thickness, weave, thread density of warp and weft direction, yarn count of warp and weft direction, limited oxygen index, conductive coefficient, and damage length. And TPP rating is used as output variable. In the training process, the connection weights are modified with gradient-descent algorithm and adaptive learning rate to solve the two defects of the BP network. After training, the predicted ability of the proposed BP neural network is tested. The results indicate that correlation coefficient between the predicted and experimental value is 0.952, and average error is 3.93 percent.
For three-layer assemblies, a BP neural network with a single hidden layer is constructed including twelve input nodes, six hidden nodes, and one output node. The input variables are mass weight, thickness, limited oxygen index of each layer fabric. And TPP rating of three-layer assembly is used as output variable. The results show that correlation coefficient between the predicted and experimental value is 0.962, and average error is 4.11 percent. Therefore, evaluation of the fabric's thermal protective performance can be economical and accurate through the proposed BP prediction model.
文中选择了国内外常用消防服外层织物,如NomexⅢA、PBI/Kevlar、Kermel、芳砜纶、芳纶、阻燃棉等十二种阻燃织物,防水透湿层分别为Nomex、阻燃棉与PTFE(聚四氟乙烯)膜的层压织物,隔热层为Nomex毡、芳砜纶毡和芳纶毡。论文首先对各层织物的阻燃性、热稳定性、表面抗湿性能、拉伸性能、撕裂性能、透湿性、耐静水压等性能进行了测试。
为了模拟消防服的多层复合系统,以外层织物、防水层织物及隔热层织物作为试样,利用混合正交设计方法进行试样组合。对不同组合织物的热防护性能(TPP值)和透湿性能(WVTR)进行了测试,并利用极差分析、方差分析方法对实验结果进行了统计处理,以探讨阻燃外层、防水透湿层和隔热层织物对组合织物系统整体热防护性能和舒适性能的影响。通过分析得到:对于组合织物系统的热防护性能,各层织物的影响程度为:外层大于防水层,防水层大于隔热层;对于组合织物系统的透湿性能,各层织物的影响程度为:防水层大于隔热层,隔热层大于外层。并且通过方差分析得到:在90%置信水平下,外层织物和防水层织物分别对组合织物系统的热防护性能和透湿性能具有显著的影响。
其次,对外层织物热防护性能的影响因素进行了分析。对于消防服用外层阻燃织物,其纤维的极限氧指数越大,则相应的织物越不易燃烧,热防护性能也越好。对于纤维组成相同的织物,其TPP值与织物的厚度、面密度具有显著的线性正相关性,即织物越厚重,TPP值越大,热防护性能也越好。除之以外,总热流量的大小及热源构成也对织物热防护性能有一定的影响,总热流量越大,造成模拟皮肤二度烧伤的时间越短;相反,总热流量越小,造成模拟皮肤二度烧伤的时间越长。而且,在84Kw/m~2高热量暴露下,对流/辐射(70/30)热源下织物的TPP值大于对流/辐射(50/50)热源下的TPP值,这说明辐射热的比例越大,皮肤越易受到损伤。
其次,论文讨论了不同热流量下水分对多层组合织物热防护性能的影响。以NI虚拟仪器为平台,以图形化编程语言Labview为工具开发了虚拟温度测试仪,并对织物中的含水量设计了四种不同的湿润状态。研究了不同强度和性质的热源下,外层织物水分的多少对多层组合织物热防护性能的影响。通过实验得到:在16.8 kw/m~2低辐射热流量暴露下,对于外层不同湿润状态的组合织物,模拟皮肤到达二度烧伤的时间为:33%水湿润状态时织物到达二度烧伤的时间最短,皮肤损伤最严重,织物66%水湿润状态时,二度烧伤时间与非湿润时相近;织物100%饱和湿润状态时热防护性能较非湿润时好。而在84 kw/m~2(50/50对流/辐射)高热流量暴露条件下,对于所有组合试样,湿润状态下的织物TPP值大于非湿润织物,即水分使织物的热防护性能上升。
最后,利用人工神经网络技术,建立了基于Matlab的织物物理性能与热防护性能之间的自适应性神经网络预测模型,并对模型进行仿真及验证。以外层织物的厚度、面密度、组织结构、经密、纬密、紧度、导热系数、极限氧指数、损毁长度共九个性能指标作为BP神经网络输入层的输入向量,输出向量为织物的TPP值,建立了消防服用外层织物的热防护性能预测模型。经过试样的训练和模型预测能力的验证,实际值与预测值的相关系数为0.952,预测平均误差为3.93%。通过比较分析得到,运用神经网络模型比线性回归模型能更精确地预测外层织物的热防护性能。
对于多层组合织物系统,以外层、防水层和隔热层织物的十二个物理指标作为BP神经网络的输入向量,输出向量为多层组合织物的TPP值,中间隐含层神经元个数为六,建立了消防服用多层组合织物的热防护性能预测模型。经过试样的训练和模型预测能力的验证,实际值与预测值的相关系数为0.962,预测平均误差为4.11%。因此,运用该模型能准确地预测多层组合织物的热防护性能。
Fire-fighter protective clothing is of great importance to firemen, as they are routinely exposed to heat and sometimes contacted with flames. So protective clothing should be comfortable and provide protection against fire and heat. Therefore, the objective of this paper is to study the thermal protective performance and comfort of firefighter-clothing fabrics and the factors which affect the thermal protective performance and comfort of fabrics. It is also to investigate optimal assembly of the overall materials in terms of their heat protection and moisture transmission.
In this paper, twelve outer fabrics such as NomexIIIA, PBI/Kevlar, Kermel, polysulfonamide, flame-resistant cotton, etc, are selected and two moisture barriers with PTFE, and three thermal liners are studied. The physical properties of each fabric are tested.
To simulate three-layer firefighter protective clothing system, the appropriate fabrics for firefighter protective clothing are selected, and the orthogonal design method is used. Thermal protective performance and moisture transmission of firefighter clothing are studied through different material combinations. Then experiment data are discussed by range analysis and variance analysis to study the effects of three layers for heat protection and moisture comfort. Results show that the priority order of three factors for TPP rating is outer fabric> moisture barrier> thermal liner, and the priority order of three factors for WVTR value is moisture barrier> thermal liner>outer fabric. Outer fabric and moisture barrier have significant effects on thermal protective performance and moisture transmission at 90 percent confidence level respectively.
Based on the above experiments, factors which influence thermal protective performance on outer fabrics are studied. Results show that fiber content, mass weight, and thickness of fabrics are dominant factors determining thermal protective performance in high intensity. Besides, exposure conditions such as intensity of the heat and property of the heat also affect thermal protective performance. With the greater heat exposure, time of the 2~(nd) degree burn is shorter. With 84Kw/m~2 mixtures of convective/radiant heat exposure, the TPP rating of 70/30 condition is better than that of 50/50 condition.
The effect of moisture on thermal protective performance is investigated in different radiant and convective heat exposures. Based on NI virtual instrument and Labview graphical programming language, a virtual instrument determining temperature has been developed. And four fabric preparations are evaluated. At the 16.8kw/m~2 radiant exposure, three-layer assemblies sprayed with little water (33%) on outer fabrics provide the weakest thermal protection. Time of the 2~(nd) degree burn for assemblies which are sprayed with much water (66%) on outer fabrics is similar to the assemblies without water. And three-layer assemblies with outer fabric soaked with water (100%) provide the greatest thermal protection. However, moisture enhances the thermal protection of three-layer assemblies at the 84kw/m2 convective and radiant exposure.
At last, a nonlinear correlation between fabric physical properties and thermal protective performance is developed in this paper. Based on Matlab neural network toolbox, BP neural networks using to predict the thermal protective performance of fabrics are developed. For outer heat resistant fabric (single-layer), a BP neural network with a single hidden layer is constructed including nine input nodes, eleven hidden nodes, and one output node. The input variables are mass weight, thickness, weave, thread density of warp and weft direction, yarn count of warp and weft direction, limited oxygen index, conductive coefficient, and damage length. And TPP rating is used as output variable. In the training process, the connection weights are modified with gradient-descent algorithm and adaptive learning rate to solve the two defects of the BP network. After training, the predicted ability of the proposed BP neural network is tested. The results indicate that correlation coefficient between the predicted and experimental value is 0.952, and average error is 3.93 percent.
For three-layer assemblies, a BP neural network with a single hidden layer is constructed including twelve input nodes, six hidden nodes, and one output node. The input variables are mass weight, thickness, limited oxygen index of each layer fabric. And TPP rating of three-layer assembly is used as output variable. The results show that correlation coefficient between the predicted and experimental value is 0.962, and average error is 4.11 percent. Therefore, evaluation of the fabric's thermal protective performance can be economical and accurate through the proposed BP prediction model.
引文
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[47] I. Shalev, and R.L.Barker. Protective Fabrics: a Comparison of Laboratory Methods for Evaluating Thermal Protective Performance in Convective/Radiant Exposures. Textile Research Journal. 54(10): 648-654 (1984).
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[53] Song, G. Clothing Air Gap Layers and Thermal Protective Performance in Single Layer Garment. Journal of Industrial Textiles. 36( 3): 193-205 (2007).
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[29]ASTM D4108-87,Standard Test Method for Thermal Protective Performance of Materials for Clothing by Open-Flame Method(S)
[30]NFPA 1971,Standard on Protective Ensembles for Structural Fire Fighting and Proximity Fire Fighting,2007 Edition
[31]NFPA 1977,Standard on Protective Clothing and Equipment for Wildland Fire Fighting,2005 Edition
[32]ASTM F1939-99a Standard Test Method for Radiant Protective Performance of Flame Resistance Materials.ASTM F1939-99a
[33]EN 469 Protective Clothing for Firefighters—Requirements and Test Method for Protective Clothing for Firefighting.
[34]ASTM F1930-2000 Standard Test Method for Evaluation of Flame Resistant Clothing for Protection Against Flash Fire Simulations Using an Instrumented Manikin
[35]邱日祥.新型消防服应该具备的性能要求及检测方法.消防技术与产品信息,2000(7):16-17
[36]谌玉红,陈强,蒋毅.燃烧假人测试系统及其应用前景.中国个体防护装备,2007(1):40-42
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[39]邹振高,刘金文,卢延振.美国阻燃防护服测试标准现状及发展趋势.中国个体防护装备,2007(3):35-38
[40]GUOWEN,SONG.Modeling Thermal Protection Outfits for Fire Exposures.PH.D.North Carolina State University.2002
[41]Stoll A M,Chianta M A.Method and Rating System for Evaluation of Thermal Protection.Aerospace Medicine,1969,40(11):1232-1238.
[42]Henriques,F.C.Studies of Thermal Injuries V—The Predictability and the Significance of Thermal Induced Rate Processes Leading to Irreversible Epidermal Injury,Archives of Pathology,43,489-502(1947).
[43]朱方龙.热防护服隔热防护性能测试方法及皮肤烧伤度评价准则.中国个体防护装备,2006(4):26-31
[44]ASTM E457-96(2002) Standard Test Method for Measuring Heat-Transfer Rate Using a Thermal Capacitance(Slug) Calorimeter
[45]R.M.Perkins.Insulative Values of Single-Layer Fabrics for Thermal Protective Clothing.Textile Research Journal 49(4):202-212(1979).
[46]I.Shalev,R.L.Barker.Analysis of Heat Transfer Characteristics of Fabrics in an Open Flame Exposure.Textile Research Journal.53(8):475-481(1983).
[47] I. Shalev, and R.L.Barker. Protective Fabrics: a Comparison of Laboratory Methods for Evaluating Thermal Protective Performance in Convective/Radiant Exposures. Textile Research Journal. 54(10): 648-654 (1984).
[48] Y.M.Lee, R.L. Barker. Thermal Protective Performance of Heat-Resistant Fabrics in Various High Intensity Heat Exposures. Textile Research Journal. 57(3), 123-132(1987).
[49] R.L.Barker., Y. M.Lee. Analyzing the Transient Thermophysical Properties of Heat-Resistant Fabrics inTPP Exposures. Textile Research Journal. 57(6), 331-338(1987).
[50] R.L.Barker. The Effect of Nomex /Kevlar Fiber Blend Ratio and Fabric Weight on Fabric Performance in Static and Dynamic TPP Tests. Performance of Protective Clothing: Fifth Volume, 1996.
[51] Kutlu, B. A. Cireli .Thermal analysis and performance properties of thermal protective clothing. Fibres & Textiles in Eastern Europe. 13(3): 58-62 (2005).
[52] Sun, G., H. S. Yoo, X. S. Zhang, et al. Radiant protective and transport properties of fabrics used by wildland firefighters. Textile Research Journal. 70(7): 567-573 (2000).
[53] Song, G. Clothing Air Gap Layers and Thermal Protective Performance in Single Layer Garment. Journal of Industrial Textiles. 36( 3): 193-205 (2007).
[54] Torvi, D. A., J. D. Dale. Effects of variations in thermal properties on the performance of flame resistant fabrics for flash fires. Textile Research Journal. 68(11): 787-796 (1998).
[55] W. E. Mell, J. R. Lawson. A heat transfer model for firefighters' protective clothing. Fire Technology. 36(1): 39-68 (2000).
[56] Torvi, D. A. J. D. Dale. Heat transfer in thin fibrous materials under high heat flux. Fire Technology. 35(3): 210-231 (1999).
[57] G. W. Song, R. L. Barker, H. Hamouda, et al. Modeling the thermal protective performance of heat resistant garments in flash fire exposures. Textile Research Journal. 74(12): 1033-1040 (2004).
[58]Chitrphiromsri,P.and A.Kuznetsov Modeling heat and moisture transport in firefighter protective clothing during flash fire exposure.Heat and Mass Transfer.41(3):206-215(2005).
[59]Yorvi,D.A.and Y.G.Threlfall.Heat transfer model of flame resistant fabrics during cooling after exposure to fire.Fire Technology.42(1):27-48(2006).
[60]中华人民共和国国家标准GB8965—1998,阻燃防护服
[61]中华人民共和国安全行业标准GA88—94,消防隔热服性能及试验方法
[62]中华人民共和国安全行业标准GA140—1996消防指挥服
[63]中华人民共和国公共安全行业标准GA10—2002消防员灭火防护服
[64]丁雪梅,李育民等.Nomex纤维与棉纤维混纺织物热稳定性分析.纺织学报,1994(4):103-105
[65]沈兰萍,李一玲.双层防热阻燃织物的产品开发及其性能测试.西北纺织工学院学报,2000(1):10-13
[66]黄晓梅,季涛等.耐高温阻燃布的研制.产业用纺织品,2004(10)
[67]刘丽英.Kermel/ViScose FR混纺阻燃织物的开发.上海纺织科技,2004(1):49-50
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