核态池沸腾中汽化核心密度和汽化核心处温度波动的研究
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摘要
本文提出了一种新的基于沸腾壁面微结构分析的预测核态池沸腾中汽化核心密度的方法。通过分析沸腾壁面的二维横断面识别出能够贮藏气体的凹坑以及其尺寸。大量的这些凹坑的开口直径组成一个集合。不同的测量尺度被用以度量这个集合，得到不同的测量结果。对于某一测量尺度，相应的测量结果是上述集合中大于测量尺度的元素的数量除以所考查的二维横断面对应于沸腾壁面上的面积。这里的测量尺度和测量结果可以被分别视为最小成核直径和汽化核心密度。因为根据经典成核理论，只有开口直径大于最小成核直径的贮藏气体的凹坑才能被活化为汽化核心。将上述离散的测量尺度和测量结果的数据回归成一个函数关系式，此关系是用以预测汽化核心密度和最小成核半径之间的关系。基于上面预测方法的预测结果比传统的统计方法预测的结果更接近实验数据，并且预测结果显示出了汽化核心密度和最小成核半径之间的分形关系。
利用数字图像处理中的边缘检测法对在单组分(R113)和双组(R11-R113)工质高热流密度核态池沸腾实验中用高速CCD摄像仪拍摄到的气泡图像进行了分析，识别出了沸腾中的汽化核心密度。在高热流密度条件下，气泡图像变得很模糊，仅凭人眼去识别气泡图像中所包含的气泡的个数变得很困难。但是在边缘检测方法的帮助下，高热流密度下气泡图像中模糊的气泡的边界被检测出来，使得气泡数量的统计更准确。通过考虑气泡合并这个引起识别出的气泡的数量和这些气泡下面的汽化核心的数量之间存在差异的因素，将识别出的气泡数量加以修正，得到汽化核心的数量。绘制了高热流密度下汽化核心密度随热流密度变化的曲线。
运用数学形态学对以纯水和掺有三氧化二铝纳米粒子的水作为工质的高热流密度核态池沸腾中反映沸腾壁面温度分布的红外图像进行了分析，识别出了沸腾中的汽化核心密度。在高热流密度下，大量红外图像中对应于汽化核心的暗点像素团聚集在一起，人眼无法识别出汽化核心的数量。但是，在对红外图像进行了形态处理后，汽化核心的数量被成功地检测出来。绘制了高热流密度下汽化核心密度随热流密度变化的曲线。
以非线性时间序列分析技术为基础，研究了三个不同的核态池沸腾中汽化核心处的温度波动。计算了汽化核心处温度时间序列的熵，其定量描述了汽化核心处温度波动的无序性。重建了汽化核心处温度时间序列对应的二维和三维吸引子，计算了定量反映吸引子自相似特性和复杂特性的关联维数。
A new method has been presented to predict the nucleation site density in nucleate pool boiling based on the investigation of the microstructure of boiling surface. The investigation is made by analyzing the two dimensional cross sections of boiling surface, which leads to the identification of gas trapping cavities and their dimensions as a result. A lot of data of the mouth diameters of the gas trapping cavities constitutes a data set. Many different measurement scales then are used to measure the data set. For a certain measurement scale, there is a corresponding measurement result which is division of the number of the data (in the data set) greater than the measurement scale by the area on the boiling surface the analyzed two dimensional cross sections correspond to. The measurement scale and the measurement result here can be viewed as the critical cavity diameter required for nucleation and the nucleation site density respectively because, according to classical heterogeneous nucleation theory, only those gas trapping cavities whose mouth diameters are greater than the critical cavity diameter can be activated to serve as nucleation sites. The above discrete data of measurement scales and measurement results are fitted by a mathematical formula which is used to predict the relationship between the nucleation site densities and the critical cavity radius. The prediction based on the above method agrees better with the experimental data than that based on traditional statistical method does and shows that it is the fractal relationship between the nucleation site density and the critical cavity radius.
The edge detection methods in digital image processing have been employed to identify the nucleation site densities in pure component (R113) and binary mixture component (R11-R113) nucleate pool boiling in high heat fluxes through analyzing the bubbles images captured by a high-speed CCD-camera in the boiling experiments. In high heat fluxes, the images become blurring and it is very difficult for the naked eyes of people to identify accurately the number of the bubbles in the images. However, with the help of edge detection method, the blurring boundaries between the bubbles in the images captured in high heat fluxes have been detected; making the counting of the bubbles numbers more accurate. The difference between the numbers of the bubbles in the images and the numbers of the nucleation sites under the bubbles, which is mainly caused by the coalescences of the bubbles, has been considered to correct the detected bubbles numbers to get the actual numbers of the nucleation sites. The change of the nucleation site densities with the high heat fluxes has been plotted.
Mathematical morphology has been used to identify the nucleation site densities in pure water and nanofluid (water plus Al_2O_3 nanoparticles) nucleate
pool boiling in high heat fluxes through analyzing the infrared images reflecting the temperature distribution on the boiling surfaces. In high heat fluxes, plenty of dark spots (in the infrared images) corresponding to the nucleation sites aggregate together; it is impossible for human naked eyes to identify the spots numbers. However, after the morphological processing of the infrared images, the nucleation sites numbers are successfully identified. And the change of the nucleation site densities with the high heat fluxes has been plotted.
Based on nonlinear time-series analysis technique, the fluctuations of the temperature at the nucleation sites in three different nucleate pool boiling experiments have been investigated. The entropies of the temperature-time series at the nucleation sites are calculated, which quantitatively describe the disorder character of the temperature fluctuation at the site. The two and three dimensional attractors corresponding to the time series are constructed, the correlation dimensions, quantitatively measuring the self-similar and complex character of the attractors, are calculated.
利用数字图像处理中的边缘检测法对在单组分(R113)和双组(R11-R113)工质高热流密度核态池沸腾实验中用高速CCD摄像仪拍摄到的气泡图像进行了分析，识别出了沸腾中的汽化核心密度。在高热流密度条件下，气泡图像变得很模糊，仅凭人眼去识别气泡图像中所包含的气泡的个数变得很困难。但是在边缘检测方法的帮助下，高热流密度下气泡图像中模糊的气泡的边界被检测出来，使得气泡数量的统计更准确。通过考虑气泡合并这个引起识别出的气泡的数量和这些气泡下面的汽化核心的数量之间存在差异的因素，将识别出的气泡数量加以修正，得到汽化核心的数量。绘制了高热流密度下汽化核心密度随热流密度变化的曲线。
运用数学形态学对以纯水和掺有三氧化二铝纳米粒子的水作为工质的高热流密度核态池沸腾中反映沸腾壁面温度分布的红外图像进行了分析，识别出了沸腾中的汽化核心密度。在高热流密度下，大量红外图像中对应于汽化核心的暗点像素团聚集在一起，人眼无法识别出汽化核心的数量。但是，在对红外图像进行了形态处理后，汽化核心的数量被成功地检测出来。绘制了高热流密度下汽化核心密度随热流密度变化的曲线。
以非线性时间序列分析技术为基础，研究了三个不同的核态池沸腾中汽化核心处的温度波动。计算了汽化核心处温度时间序列的熵，其定量描述了汽化核心处温度波动的无序性。重建了汽化核心处温度时间序列对应的二维和三维吸引子，计算了定量反映吸引子自相似特性和复杂特性的关联维数。
A new method has been presented to predict the nucleation site density in nucleate pool boiling based on the investigation of the microstructure of boiling surface. The investigation is made by analyzing the two dimensional cross sections of boiling surface, which leads to the identification of gas trapping cavities and their dimensions as a result. A lot of data of the mouth diameters of the gas trapping cavities constitutes a data set. Many different measurement scales then are used to measure the data set. For a certain measurement scale, there is a corresponding measurement result which is division of the number of the data (in the data set) greater than the measurement scale by the area on the boiling surface the analyzed two dimensional cross sections correspond to. The measurement scale and the measurement result here can be viewed as the critical cavity diameter required for nucleation and the nucleation site density respectively because, according to classical heterogeneous nucleation theory, only those gas trapping cavities whose mouth diameters are greater than the critical cavity diameter can be activated to serve as nucleation sites. The above discrete data of measurement scales and measurement results are fitted by a mathematical formula which is used to predict the relationship between the nucleation site densities and the critical cavity radius. The prediction based on the above method agrees better with the experimental data than that based on traditional statistical method does and shows that it is the fractal relationship between the nucleation site density and the critical cavity radius.
The edge detection methods in digital image processing have been employed to identify the nucleation site densities in pure component (R113) and binary mixture component (R11-R113) nucleate pool boiling in high heat fluxes through analyzing the bubbles images captured by a high-speed CCD-camera in the boiling experiments. In high heat fluxes, the images become blurring and it is very difficult for the naked eyes of people to identify accurately the number of the bubbles in the images. However, with the help of edge detection method, the blurring boundaries between the bubbles in the images captured in high heat fluxes have been detected; making the counting of the bubbles numbers more accurate. The difference between the numbers of the bubbles in the images and the numbers of the nucleation sites under the bubbles, which is mainly caused by the coalescences of the bubbles, has been considered to correct the detected bubbles numbers to get the actual numbers of the nucleation sites. The change of the nucleation site densities with the high heat fluxes has been plotted.
Mathematical morphology has been used to identify the nucleation site densities in pure water and nanofluid (water plus Al_2O_3 nanoparticles) nucleate
pool boiling in high heat fluxes through analyzing the infrared images reflecting the temperature distribution on the boiling surfaces. In high heat fluxes, plenty of dark spots (in the infrared images) corresponding to the nucleation sites aggregate together; it is impossible for human naked eyes to identify the spots numbers. However, after the morphological processing of the infrared images, the nucleation sites numbers are successfully identified. And the change of the nucleation site densities with the high heat fluxes has been plotted.
Based on nonlinear time-series analysis technique, the fluctuations of the temperature at the nucleation sites in three different nucleate pool boiling experiments have been investigated. The entropies of the temperature-time series at the nucleation sites are calculated, which quantitatively describe the disorder character of the temperature fluctuation at the site. The two and three dimensional attractors corresponding to the time series are constructed, the correlation dimensions, quantitatively measuring the self-similar and complex character of the attractors, are calculated.
引文
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