【作者】 韩骏；

【导师】 董峰；

【作者基本信息】 天津大学 ， 检测技术与自动化装置， 2010， 博士

【摘要】 两相流是诸多工业中经常遇到的现象,其流动规律研究具有重要的科学价值和广泛的工程应用价值,两相流的测量也成为国内外科技工作者争相探索的热点课题。层析成像技术和软测量技术的产生和发展为解决多相流系统的参数检测问题提供了一条新的思路和方法,受到了国内外研究者的广泛关注。小波熵能够揭示数据在时-频空间中能量分布信息和变化的特征参数,近年来在工程中得到了广泛的应用和研究。小波网络是小波分析理论与神经网络理论相结合的产物,也是近来应用和研究的一个热点。对于电阻层析成像(电阻截面测量)和软测量技术、小波熵和小波网络四方面的研究是本论文工作完成的基础。本论文从两相流参数测量方法与流动机理出发,在电阻层析成像系统的传感器测量原理基础上,完成了水平管气水两相流的小波熵的流型特征提取和流型识别;在基于V型内锥差压测量原理和小波网络理论全面研究的基础上,完成了水平管油水、气水两相流的质量流量软测量。本文研究中主要完成的工作有:1、在对已有国内外小波网络的研究成果基础上,系统地对小波网络进行了拓扑结构和分类研究,提出了一种新的小波网络结构-混合递归Elman小波网络,并推导了信号分类Elman小波网络和混合递归Elman小波网络的训练算法;2、在研究电阻层析成像系统的传感器用于两相流参数测量方法的基础上,提出了截面测量信息数据的三种数据序列组织方法,完成了水平管气水两相流截面测量信息在三种数据组织方式下的五种小波熵特征提取,并给出了分析结果和比较的结论,并结合Elman小波网络实现了水平管气水两相流的流型识别。3、在对V型内锥差压测量原理和小波网络全面研究的基础上,提出了具有较好泛化能力的质量流量测量模型,实现了基于小波网络模型的油水、气水两相流的质量流量软测量,并通过实验结果验证了模型的正确性。更多还原

【Abstract】 Two-phase flow is frequently encountered in many industry phenomenons. Study of the behavior of flow has important scientific value and the value of a wide range of engineering applications. Two-phase flow measurement has become the hot topic to be explored by a domestic and foreign scientific and technical worker.The development of Tomography Technology and the Soft-sensing Technology provide a new way of thinking and methods for the solution of parameters detection question in multi-phase flow system, and it has been given extensive attention by researchers at home and abroad. Wavelet entropy can reveal information of energy distribution and the changing of characteristic parameters about the data in time- frequency space. In recent years, it has been widely used and studied in engineering. The wavelet network is the result which the wavelet analysis theory and the neural network theory unifies, and it also be a hot issues for research and development worldwide. The research in the four areas is the work foundation of the present paper completion.In light of the parametric measuring techniques and flow mechanism of two-phase flow, based on the study of the measurement method of Electrical Resistance Tomography (ERT) sensors in two-phase flow measurement and using wavelet entropy method, the feature extraction and flow pattern identification of the gas-water two-phase flow in horizontal pipe is completed. Based on the principle of V-cone differential pressure measurement and the comprehensive study of wavelet network theory, the soft-sensing of gas mass flow rate of oil-water and gas-water two-phase flow in horizontal pipe was done. In this paper, the main works accomplished are as follows:1. Based on the research results of wavelet network in the domestic and foreign, the topology and the classified research on the wavelet network has been carried on, a new wavelet network structure is proposed and it is the Hybrid Feedback (HF) Elman Wavelet Neural Network. The training algorithm of the Elman wavelet network for Signal Classification and the Hybrid Feedback (HF) Elman Wavelet Neural Network are derived. 2. Based on the study of the measurement method of ERT sensor in two-phase flow measurement, three organization methods of data series of section measurement information are proposed, and the extraction of five kinds of wavelet entropy of cross-section measurement information of gas-water two-phase flow in horizontal pipe is completed under three organization ways, and the analysis result and the comparison conclusion of using it are given, and the flow pattern identification has been achieved by using the Elman Wavelet Neural Network.3. Through the analysis of the principle of V-cone differential pressure measurement and the comprehensive study of wavelet network theory, the measurement model of mass flow-rate with good generalization ability is put forward. The soft-sensing method of mass flow-rate of oil-water and gas-water based on wavelet network model is achieved, and the validity of the model is verified by experimental results.更多还原