沸腾换热论文基于神经网络的竖直矩形细通道内沸腾换热汽液两相流

1、 沸腾换热论文：基于神经网络的竖直矩形细通道内沸腾换热汽液两相流型识别研究【中文摘要】在沸腾换热的研究中发现,汽液两相流动介质的相界面分布状况,即流型,极大地影响着汽液两相流的流动特性和传热性能,同时也对流动参数的准确测量以及两相流系统运行特性的确定具有很大的影响作用。因此,沸腾换热汽液两相流流型识别的研究一直是两相流参数分析一个重要组成部分。本课题针对流型识别存在的不足,提出了利用神经网络进行流型识别的方法。首先,本课题以去离子水为被加热工质,对槽道宽度分别为2mm、1.5mm、1mm和0.5mm的实验件进行沸腾换热实验。实验过程实现了被测实验件内部沸腾状态的可视化,并且测量了工质的体积流量

2、、实验段入口处温度和压力和实验件的压差波动信号,同时在实验件上沿工质流动方向设置多组热电偶,测量不同位置的温度值。然后,运用matlab小波降噪的方法对获取的信号进行处理。选择小波模块为wavelet 2-D,选择母小波为haar,选择阈值模式为unscaled white noise和horizontal details coefs,选择阈值函数为软阈值。经过处理后的信号可以更好的辅助实验者确定流型的类别,减少主观判断错误的发生。最后,通过实验获得的各点温度、液体体积流量和压差等数据,计算得到一个无量纲数。并以此无量纲数和热电偶处测得的温度值为输入向量,以各向量所对应的三种流型单相流(001

3、),弹状流(011),受限弹状流(111)为输出向量。本文建立了四种神经网络：BP,RBF, SOM和Elman神经网络。实验过程中测得的数据其中一部分用于神经网络的建立和训练,另外一部分数据用于对所建立神经网络进行验证。神经网络的识别结果表明：利用BP和Elman神经网络进行流型的识别效果较好,识别率在90%以上,而利用SOM进行识别正确率低于50%,RBF神经网络的识别能力介于两者之间。从而,BP和Elman神经网络可以作为流型的分类器。针对这两种神经网络的良好的分类效果,本文建立了流型识别的用户界面,用户可以在输入栏中输入数据,直接得到输出结果。本文提供了一种识别流型的新方法,与其他识别

4、方法相比,减少了因主观而造成的识别误差,提高了流型的识别率。【英文摘要】It is found in the studies of boiling heat transfer that the vapor-liquid two-phase flow situation on the interface-flow pattern, can greatly affect not only the two-phase flow and heat transfer characteristics of the vapor-liquid, but also the accurate measuremen

5、t of flow parameters and the determination of the operating characteristics of two-phase system. So the analysis of the pattern recognition of boiling heat transfer is an important part for the vapor-liquid two-phase flow.Firstly, water is taken as the working fluid. The width of the channel is set

6、to be 2mm,1.5mm,1mm and 0.5mm for the boiling heat transfer experiments. The channel is covered with plexi-glass, which is nature to be transparent to achieve the visualization of the experiment. In the study, mass flow rate, temperature, pressure at the entrance of the test section, and fluctuation

7、 signal of the differential pressure are measured, besides, thermo-couples are set along the direction of flow to measure the temperature value of different positions.Then the signal obtained use wavelet denoising methods of matlab for processing. The wavelet mode selects wavelet 2-D, the mother wav

8、elet selects haar, the threshold mode selects the unscaled white noise and horizontal details coefs, threshold function selects the soft threshold. After treatment, the signal can greatly assist the experimenter to determine the flow pattern, and reduce subjective errors. After the signal analysis a

9、nd processing, it can be learned in this experiment that there are three types of flow pattern:single-phase flow, slug flow and limited slug flow.Finally, a dimensionless number is gained by computing the experiments temperature, fluid velocity and differential pressure and other datum. The dimensio

10、nless number is taken as the input vector with the temperature measured by thermo-couples; three corresponding flow:single-phase flow (0 01), slug flow (011) and limited slug flow (111) are taken as the output vectors. In this paper, four neural networks:BP, RBF, SOM, and Elman neural networks are e

11、stablished. Part of the datum is used for the establishment and tra i n i ng of neural networks, other part of the datum i s used for neural network validation. The results show that the recognition of flow pattern of BP and Elman neural network are better than another two networks, with the rate of

12、 90% or more, while the correct rate of SOM is less than 50%. And the recognition rate of RBF neural network is just between them. According to the classification of the good results of the two neural networks, the user interface is established, so the users can output the data directly.This paper p

13、rovides a new method of identifying flow patterns. Comparing with other identification methods, the subjective recognition errors are decreased and the recognition rate of flow can be improved.【关键词】沸腾换热 流型 神经网络 识别【英文关键词】boiling heat transfer flow pattern neural networks recognition【目录】基于神经网络的竖直矩形细通道

14、内沸腾换热汽液两相流型识别研究摘要13-14ABSTRACT14-15第1章 绪论16-221.1 课题研究的背景及意义16-171.2 汽液两相流流型识别的主要方法17-201.2.1 流型的直接测量法17-181.2.2 流形的间接测量法18-191.2.3 基于神经网络的流形识别方法19-201.3 本文的主要研究内容20-22第二章 细通道内沸腾汽液两相流动的实验研究22-342.1 实验系统简介22-252.2 实验件252.3 所需实验仪器及精度25-262.4 实验步骤26-292.4.1 需要测量的参数272.4.2 热电偶布置272.4.3 温度的测量27-282.4.4 压

15、差的测量282.4.5 实验数据28-292.5 实验所观察到的流型29-322.5.1 流型的定义29-302.5.2 实验中观察到的流型30-322.6 本章小结32-34第三章 基于连续小波变换的信号处理34-423.1 连续小波变换34-393.1.1 连续小波的二维特征34-353.1.2 连续小波变换的性质35-393.1.3 小波变换的依据393.2 连续小波变换的图片处理39-413.2.1 连续小波的GUI39-403.2.2 信号的降噪处理40-413.3 本章小结41-42第四章 基于神经网络的沸腾汽液两相流流型识别42-724.1 神经网络基本理论42-494.1.1

16、神经网络发展历程42-444.1.2 神经网络研究内容444.1.3 神经元的模型44-464.1.4 神经元的连接方式46-494.2 BP神经网络模型49-554.2.1 BP神经网络基础49-504.2.2 BP神经网络的学习算法504.2.3 BP神经网络的流型识别50-554.3 径向基函数网络模型55-594.3.1 径向基函数神经网络基础55-564.3.2 基于径向基神经网络的学习算法56-584.3.3 基于径向基神经网络的流型识别58-594.4 自组织神经网络模型59-624.4.1 自组织神经网络基础59-604.4.2 自组织特征映射神经网络(SOM)结构60-614.4.3 自组织特征映射神经网络学习算法614.4.4 基于自组织神经网络的流型识别61-624.5 反馈型神经网络模型62-684.5.1 反馈型神经网络基础62