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用于仿真计算的缺陷模型如下:
含分层缺陷的三维模型如图1所示时,因为具有轴对称性,当采用具有轴对称特性的ANSYS单元进行解算时,该模型可以等同为如图2所示的二维模型。为了便于计算,文中接下来采用二维模型进行仿真研究。模型中画有网格的区域为分层缺陷,为空气,本底材料为玻璃纤维增强塑料(Glass Fiber Reinforced Plastic,GFRP)。这里L为试件厚度,l为缺陷埋深,d为缺陷厚度,R为圆柱形试件半径,rd为缺陷半径,q为加热的热流密度。
对于脱粘缺陷的情况,需要将缺陷区域右侧的I区换成胶层,缺陷和I区的上方换为GFRP蒙皮或涂层,缺陷和I区的下方换为芯材或基体,来模拟夹层结构或涂层的脱粘缺陷。文中以上述模型为基础着重研究分层缺陷的仿真。仿真对比分析的主要参数见表1。25 ℃空气的热物理性质参数由其20 ℃和30 ℃的数值插值得到。
研究中涉及两种材料:GFRP和空气,它们的热物理性质参数见表2。
表 1 主要模型参数
Table 1. Main parameter in the model
Specimen thickness
L/mDefect thickness
d/mDefect depth
l/mSpecimen radius
R/mDefect radius
rd/mHeating pulse duration
t/sHeating flux density
q/W·m−20.0025 0.00005 0.0005 0.020 0.0075 0.002 0.84×107 表 2 材料热物理性质参数
Table 2. Thermal properties of materials
在进行仿真计算之前,需要定义如下可检信息参数,同一时刻缺陷和无缺陷的温差ΔT(t)可表示为:
$$ \Delta T(t)=\theta_d(t)- \theta_{nd}(t) $$ (1) 同一时刻温度对比度C(t)可表示为:
$$ C{{(t)}} = \frac{{\Delta T(t)}}{{{\theta _{nd}}(t)}}$$ (2) 式中:θd(t)为有缺陷区的表面温升;θnd(t)为无缺陷区的表面温升。选取缺陷中心点作为缺陷区的参考点,模型边缘处的点作为无缺陷区的参考点,见图2。温差ΔT(t)反映的是缺陷可检性,如果ΔT(t)小于、等于环境噪声,则缺陷很难被检测到。温度对比度C(t)反映的是温差ΔT(t)相对于无缺陷信号的大小,在图像上反映的是缺陷的清晰程度。
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日照均匀的情况下,日照造成检测前被检测部位的初始温度相同,同前面的环境温度影响相近似。文中主要研究日照造成被检测部位初始温度不均匀的影响。
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为了模拟由于日照造成的检测部位初始温度不均匀,首先取地面接受到的太阳辐射热流密度为q = 150 W/ m2[15],在模型上表面左半部分施加该热流,加热时长为60 s,见图9模型中左上红线部分。然后,再于模型的上表面全部施加热流q = 0.84×107 W/ m2,加热时长为0.002 s,即闪光灯激励加热,此后开始正常检测过程,这一过程总时长为10 s。模型上表面左半部分施加的热流情况参见公式(3),右半部分施加的热流情况参见公式(4)。对应的环境温度为25 ℃。均匀温度条件下进行的常规检测中,对应的环境温度和试块温度同为25 ℃。两种模型下加热面在冷却阶段的对流换热系数均为10 W/(m2·K),其他边界在加热阶段和冷却阶段均为绝热边界条件。两种模型中温度差△T(t)和温度对比度C(t)的对比分别见图10和图11。
图 10 日照不均与均匀温度下温差ΔT(t)的仿真结果
Figure 10. Simulation result of temperature difference ΔT(t) in uneven sunshine and in homogeneous temperature
图 11 日照不均与均匀温度下温度对比度C(t)的仿真结果
Figure 11. Simulation result of temperature contrast C(t) in uneven sunshine and in homogeneous temperature
$$ q(t)=\left\{\begin{array}{c}150,0 \leqslant t\leqslant 60\;{\rm s}\\ {8.4\times 10}^{6},60\;{\rm s} < t\leqslant 60.002\;{\rm s}\\ 0,60.002\;{\rm s} < t\leqslant 70\;{\rm s}\end{array}\right.$$ (3) $$ q(t)=\left\{\begin{array}{c}0,0\leqslant t\leqslant 60\;{\rm s}\\ {8.4\times 10}^{6},60\;{\rm s} < t\leqslant 60.002\;{\rm s}\\ 0,60.002\;{\rm s} < t\leqslant 70\;{\rm s}\end{array}\right. $$ (4) 可见,日照不均使得检测实施前有缺陷处被阳光加热升温,参考的无缺陷区没有被阳光加热,因而造成缺陷区与无缺陷区的温差ΔT(t)和温度对比度C(t)发生明显变化。此例中温差ΔT(t)最大值ΔTm和温度对比度C(t)最大值Cm较均匀温度模型下的最大值均明显增大,不利于红外热成像的缺陷检测与识别。
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将试块第一列埋深为0.5 mm的缺陷右侧用铝箔遮盖,然后放到太阳下直晒1 min,然后迅速拿回到室内进行检测实验。将埋深为0.5 mm缺陷的0.15~10 s范围内温差ΔT(t)曲线和温度对比度C(t)曲线与试块温度均匀(25 ℃)时的检测结果进行对比。图12为t =1.102 s时刻的红外热成像图,图中左侧区域明显要亮于(温度高于)右侧区域。图13为日照不均引起温差ΔT(t)变化的实验结果。图14为日照不均引起温度对比度C(t)变化的实验结果。
图 12 实验前日照不均的热像图t =1.102 s
Figure 12. Thermal image t =1.102 s of the specimen in uneven sunshine before experiment
图 13 日照不均与均匀温度下温差ΔT(t)的实验结果
Figure 13. Experiment result of temperature difference ΔT(t) in uneven sunshine and in homogeneous temperature
图 14 日照不均与均匀温度下温度对比度C(t)的实验结果
Figure 14. Experiment result of temperature contrast C(t) in uneven sunshine and in homogeneous temperature
通过日照不均和均匀温度下实验结果对比,可见日照不均引起ΔTm和Cm明显增高,这与仿真结果得到的变化趋势相一致。这样的变化易造成太阳直射区域无缺陷区与参考的未被阳光加热的无缺陷区之间出现明显的ΔTm和Cm,易将该无缺陷区误判为缺陷。
若被阳光直接照射的不是缺陷区,而是参考的无缺陷区,则ΔTm和Cm的变化趋势相反,都呈减小的趋势,从而降低了缺陷的可检性和清晰程度,易造成漏检。
Effects of environmental factors on infrared flash thermography nondestructive testing in outfield detection
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摘要: 为了研究外场环境下实施红外热成像检测时,环境温度、日照、风速等外场环境因素对检测的影响机理及规律,文中以闪光灯激励红外热像法检测玻璃纤维增强塑料层压板分层缺陷为例,通过轴对称分层缺陷的物理建模、有限元仿真计算和基于试块的实验研究,得到了温差、温差最大值、温度对比度和温度对比度最大值等可检信息参数随各个环境因素变化的规律。文中对比了25 ℃和30 ℃环境温度下的仿真结果和实验结果,对比了日照不均和均匀温度下的仿真结果和实验结果,对比了对流换热系数为10 W/(m2·K)与100 W/(m2·K)的仿真结果和正常散热与强制散热的实验结果。基于以上仿真结果和实验结果,得出了如下结论:随着环境温度的升高,温度对比度最大值下降,缺陷清晰度下降,不利于缺陷的检出;日照不均使得温差最大值和温度对比度最大值或者增大或者减小,会造成误判或缺陷漏检;随着风速的增大,温差最大值和温度对比度最大值变小,缺陷的可检性变差,缺陷的清晰程度下降,不利于缺陷检测。Abstract: To reveal mechanism of influence of environmental factors such as ambient temperature, sunshine and wind velocity on infrared thermographic nondestructive testing, modeling of a delamination in a glass fiber reinforced plastic specimen in infrared flash thermography testing was made and studied. The model was an axial symmetry model. Thermal signals to be detected in infrared thermographic nondestructive testing, such as temperature difference, the maximum temperature difference, temperature contrast and the maximum temperature contrast, were calculated and analyzed using finite element method in that model. Several experiments with special process treatment on that specimen were carried out. Evolution of the thermal signals with changes of those environmental factors was researched. Comparison of results from modeling and experiment in ambient temperature 25 ℃ and 30 ℃ was made. Results from modeling and experiments in uneven sunshine and in homogeneous temperature were compared too. Results in deferent wind speed were presented at the end of this paper. Convective heat transfer coefficients in the model were 10 W/(m2·K) and 100 W/(m2·K). The simulation results and the experiment results show that the maximum temperature contrast declines with increase of ambient temperature. Decline of the maximum temperature contrast means that clarity of defect gets worse, and detection of defect becomes more difficult. The maximum temperature difference and the maximum temperature contrast increase or decrease due to uneven sunshine on the specimen. Those changes lead to misjudgment or missing detection. Increase of wind velocity may reduce the maximum temperature difference and the maximum temperature contrast, and result in worse detectability and clarity of defect.
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表 1 主要模型参数
Table 1. Main parameter in the model
Specimen thickness
L/mDefect thickness
d/mDefect depth
l/mSpecimen radius
R/mDefect radius
rd/mHeating pulse duration
t/sHeating flux density
q/W·m−20.0025 0.00005 0.0005 0.020 0.0075 0.002 0.84×107 -
[1] Wang Yaqiong. Research on maintenance and detection of civil aircraft based on infrared thermal Image detection [J]. Modern Manufacturing Technology and Equipment, 2019(11): 169-170. (in Chinese) doi: 10.3969/j.issn.1673-5587.2019.11.086 [2] Cao Qiang, Han Daorong. Application of NDT in military aircraft maintenance [J]. Aviation Maintenance & Engineering, 2015(4): 74-77. (in Chinese) doi: 10.3969/j.issn.1672-0989.2015.04.024 [3] 李秀芬, 陈俊文, 姜洋等. 飞机结构件在役渗透及磁粉检测技术应用研究[C]. 第十一届全国磁粉渗透检测技术年会, 2017年山东济宁: 154-158 Li Xiufen, Chen Junwen, Jiang Yang, et al. Application research of penetrant inspection and magnetic particle inspection in aircraft components in service[C]//The 11th National Conference of Penetrant Inspection and Magnetic Particle Inspection, 2017: 154-158. (in Chinese) [4] Huang Huabing, Xu Mao, Peng Zhiwei. In-situ testing method of R area for aircraft structure [J]. Nondestructive Testing, 2019, 41(3): 38-41. (in Chinese) doi: 10.11973/wsjc201903010 [5] Ma Jianhui, Yang Guang, Liu Yong. The in-situ detection of aero-engine turbine blade with endoscope and fluorescence penetration [J]. Nondestructive Testing, 2020, 42(6): 50-53. (in Chinese) doi: 10.11973/wsjc202006011 [6] Wang Dan, Ning Ning, Yang Pengfei, et al. In-situ nondestructive testing technology for the integral composite panel of the fuel tank [J]. Nondestructive Testing, 2018, 40(11): 33-36,55. (in Chinese) doi: 10.11973/wsjc201811009 [7] Liu Bingwei, Zhong Mian, Fu Hangjun, et al. Defect extraction and quantitative analysis of composite materials based on infrared detection[C]//Proc of SPIE, 2020, 11455: 114550M. [8] Liu Yingtao, Mu Rende, Guo Guangping, et al. Infrared flash thermographic nondestructive testing of defects in thermal barrier coating [J]. Journal of Aeronautical Materials, 2015, 35(6): 83-90. (in Chinese) doi: 10.11868/j.issn.1005-5053.2015.6.014 [9] Liu Yingtao, Guo Guangping, Yang Danggang, et al. Pulsed thermography of composite components used in aerospace applications [J]. Journal of Aeronautical Materials, 2012, 32(1): 72-77. (in Chinese) doi: 10.3969/j.issn.1005-5053.2012.1.015 [10] Wang Zhiyong, Liu Yingtao, Wang Xiaohu, et al. Application of infrared thermography in research of radar absorbing coating defects [J]. Journal of Aeronautical Materials, 2012, 32(3): 91-95. (in Chinese) doi: 10.3969/j.issn.1005-5053.2012.3.016 [11] Maldague X. Nondestructive Evaluation of Materials by Infrared Thermography[M]. London: Springer, 1993. [12] 杨世铭, 陶文铨. 传热学[M]. 第3版. 北京: 高等教育出版社, 1998: 420-424 Yang Shiming, Tao Wenquan. Heat Transfer[M]. 3rd Edition. Beijing: Higher Education Press, 1998: 420-424. (in Chinese) [13] Liu Yingtao, Tang Jia, Guo Xingwang, et al. Effects of composites translucence on flash infrared thermal detection [J]. Laser & Infrared, 2017, 47(10): 1264-1270. (in Chinese) doi: 10.3969/j.issn.1001-5078.2017.10.014 [14] Guo Xingwang, Guan Heqing, Liu Yingtao, et al. Spectrum characteristics and light source selection for infrared thermal imaging testing of semitransparent materials [J]. Infrared and Laser Engineering, 2017, 46(1): 0104001. (in Chinese) doi: 10.3788/IRLA201746.0104001 [15] Lin Zhengyun. The distributions of global radiation and ground radiation balance in Fujian province [J]. Acta Energiae Solaris Sinica, 1994, 15(3): 248-256. (in Chinese)