基于传感器数据融合的小通道气液两相流参数测量新方法研究
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摘要
随着微加工技术和新材料技术的迅速发展,工业设备微型化、小型化的趋势日益明显,小通道尺度下的气液两相流参数测量已成为当前两相流研究领域的热点,相关参数检测方法的研究对小通道气液两相流参数测量有着十分重要的意义。本文以小通道中的气液两相流为研究对象,基于光学位置传感器和电容耦合式非接触电导测量(Capacitively Coupled Contactless Conductivity Detection, C4D)传感器,结合信息处理技术、传感器数据融合技术对小通道气液两相流流型、相含率的在线测量方法进行了研究。
     本学位论文的主要工作和创新点如下:
     1.研发了新型光学位置传感器和新型C4D传感器,并建立了一套基于光学位置传感器和C4D传感器的小通道气液两相流参数在线测量系统。研究结果表明,所研发的新型光学位置传感器和新型C4D传感器是成功的,均可有效用于小通道气液两相流参数测量。
     2.基于光学位置传感器,研究了小通道气液两相流流型辨识和相含率测量问题。首先,利用所获得的光学信号,对比研究了三种不同特征提取方法:1)采用统计分析进行特征提取;2)采用统计分析和小波分解进行特征提取;3)采用统计分析和经验模态分解(EMD)进行特征提取,并利用流型辨识实验对三种特征提取方法进行了验证。实验结果表明,三种特征提取方法用于流型辨识均是有效的。其中,采用统计分析的特征提取方法具有更好的实时性,且流型辨识结果也令人满意。在内径为4.0mm,3.0mm和1.8mm水平小通道下,典型流型的辨识准确率分别高于85.0%、75.0%和83.0%。其次,利用所获得的光学信号,分别结合物理模型分析和LS-SVM回归方法,建立了两种段塞流的相含率测量模型,并利用动态实验对所建立的模型进行了验证。实验结果表明,所建立的两种段塞流相含率测量模型均是有效的。其中,基于LS-SVM建立的段塞流相含率测量模型具有更高的测量准确率(基于LS-SVM模型的测量最大绝对误差为5.0%,基于物理模型的测量最大绝对误差为10.0%)。
     3、基于C4D传感器,研究了小通道气液两相流流型辨识和相含率测量问题。首先,利用C4D传感器获得的信号,并结合统计分析的特征提取方法,对不同管径下的气液两相流进行了流型辨识。然后,利用C4D传感器获得的信号,并结合LS-SVM回归方法,针对不同流型建立了典型流型下的相含率测量模型,并利用动态实验对所建立模型进行了验证。研究结果表明,所研发的C4D传感器用于流型辨识和相含率测量均为有效的。在内径为4.0mm,3.0mm和1.8mm水平小通道下的典型流型辨识准确率分别高于78.0%、88.0%和83.0%。在内径为3.0mm的水平小通道中,段塞流,泡状流,层状流和环状流下的相含率测量最大绝对误差分别为10.0%、3.5%、3.0%和5.0%。
     4.基于传感器数据融合技术,并结合光学位置传感器和C4D传感器测量信号,提出了小通道气液两相流的流型辨识和相含率测量新方法。所提出的流型辨识新方法为:首先分别利用光学位置传感器和C4D传感器的测量信号进行流型辨识,然后运用D-S证据理论对两种传感器的辨识结果进行融合进而获得流型。所提出的相含率测量新方法为:先判断当前流型,再根据流型选择相应的相含率测量模型(若为段塞流,则选择基于光学位置传感器的LS-SVM相含率测量模型,若为其他几种流型,则选择基于C4D传感器的LS-SVM相含率测量模型)进行相含率计算。研究结果表明,所提出的流型辨识新方法和相含率测量新方法均是有效的。基于传感器融合技术,三种管径下的流型辨识的准确率和相含率测量精度均有所提高,在内径为4.0mm,3.0mm和1.8mm水平小通道下的流型辨识准确率分别高于85.0%、97.0%和88.0%。在内径为3.0mm的水平小通道中,段塞流,泡状流,层状流和环状流下的相含率测量最大绝对误差分别为5.0%、3.5%、3.0%和5.0%。所提出的流型辨识新方法和相含率测量新方法充分利用了两种传感器在不同流型下测量信息的互补性,提高了流型辨识的准确率和相含率测量精度。
With the rapid development of micro-fabrication technology and new material technology, the miniaturized tendency of the industrial equipment becomes increasingly obvious. The study on gas-liquid two-phase flow parameter measurement in small channels has become a popular field in current two-phase flow research. This dissertation mainly aims at the online measurement of the flow pattern, void fraction of gas-liquid two-phase flow in small channels. Based on an optical position sensitive detector and a capacitively coupled contactless conductivity detection(C4D) sensor, and by using the information processing technology, the sensor data fusion technology, the research work on these parameters measurement are carried out.
     The main work and innovations are listed as follows:
     1. A new optical position sensitive detector and a new C4D sensor are developed, and an online parameter measurement system is built for gas-liquid two-phase flow in small channel. Research results show that the developed optical position sensitive detector and the developed C D sensor are successful, and they are both suitable for the parameter measurement of gas-liquid two-phase flow in small channel.
     2. Based on the optical position sensitive detector, flow pattern identification and void fraction measurement are studied for gas-liquid two-phase flow in small channel. First, using the obtained optical signal, three feature extraction methods are compared:1) using statistical analysis method for feature extraction;2) using the combination of statistical analysis and wavelet decomposition method for feature extraction;3)using the combination of statistical analysis and Empirical Mode Decomposition (EMD) method for feature extraction. Then, the effectiveness of these three feature extraction methods are verified by flow pattern identification experiment. Research results show that, these three feature extraction methods are all effective for flow pattern identification. The method which uses statistical analysis for feature extraction has good real-time performance, and the identification results using this method are satisfactory. In three pipes with the inner diameters of4.0mm,3.0mm and1.8mm, the identification accuracies for typical flow patterns are higher than85.0%,75.0%and83.0%, respectively. Meanwhile, based on the obtained optical signals, two void fraction measurement models are developed for slug flow based on physical model and LS-SVM regression method, respectively, and the effectiveness of the void fraction measurement models are testified by dynamic experiments. Experiment results show that these two developed void fraction measurement models are both effective. The measurement model based on LS-SVM can obtain higher accuracy of void fraction measurement (the maximum error are is than5.0%based on LS-SVM regression model, while the maximum errors is less than10.0%based on physical model).
     3. Based on C4D sensor, flow pattern identification and void fraction measurement are studied for gas-liquid two-phase flow in small channel. First, using the C4D sensor's signals, flow pattern identification are carried out in different pipes based on the statistical analysis method. Then, using the C4D sensor's signals, void fraction measurement models for typical flow patterns are developed, and the effectiveness of the models are verified by dynamic experiments. Research results show that the developed C4D sensor is effective for flow pattern identification and void fraction measurement of gas-liquid two-phase flow in small channel. In the pipes with the inner diameters of4.0mm,3.0mm and1.8mm, the identification accuracies for typical flow patterns are higher than78.0%,88.0%and83.0%, respectively. Besides, in the pipe with the inner diameter of3.0mm, the maximum errors of void fraction measurement under typical flow patterns (slug flow, bubble flow, stratified flow and annular flow) are all less than10.0%,3.5%,3.0%and5.0%, respectively.
     4. Based on sensor data fusion technique, and combing with the optical position detector and C4D sensor, a new flow pattern identification method and a new void fraction measurement method are proposed for gas-liquid two-phase flow in small channel. The proposed flow pattern identification method using the signals from the optical position detector and C4D sensor to identify the flow pattern respectively, and then the identification results obtained by two sensors are fused based on D-S theory, finally the flow pattern are determined. The proposed void fraction measurement method determines the current flow pattern firstly, and then selecting the proper void fraction measurement model according to the flow pattern identification results (if the flow pattern is slug flow, the LS-SVM model based on optical position detector is selected, else, the LS-SVM model based on C4D sensor is selected), finnally the void fraction is predicted by the selected model. Research results show that the proposed flow pattern identification method and the void fraction measurement method are both effective. Based on sensor data fusion technique, the performance of the flow pattern identification and the void fraction measurement are improved. In the pipes with the inner diameters of4.0mm,3.0mm and1.8mm, the identification accuracies for typical flow patterns are higher than85.0%,97.0%and88.0%, respectively. Besides, in the pipe with the inner diameter of3.0mm, the maximum errors of void fraction measurement under typical flow patterns (slug flow, bubble flow, stratified flow and annular flow) are all less than5.0%,3.5%,3.0%and5.0%, respectively. As the proposed flow pattern identification method and the void fraction method can sufficiently using the complementarity of the two sensors, the performance of the flow pattern identification and the void fraction measurement are improved.
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