排序方式: 共有23条查询结果,搜索用时 15 毫秒
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一个简单实用的神经网络环状流参数估计器 总被引:2,自引:2,他引:0
环状流是一种典型的两相流.人们常用线性反投影法(LBP)对其进行快速成像.但由于该算法精度不高,本质上是一种定性算法,得到的效果不能令人满意.本文以电阻抗层析测量系统(ERT)中LBP法为研究对象,分析其存在的缺陷和产生缺陷的原因.针对理想环状流这一特定流型提出并建立一个基于BP神经网络的参数估计器来计算管道内含油区面积.仿真表明,对环状流使用该估计器能够获得较好的结果. 相似文献
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A slotted orifice has many superiorities over a standard orifice. For single-phase flow measurement, its flow coefficient is insensitive to the upstream velocity profile. For two phase flow measurement, various characteristics of its differential pressure (DP) are stable and closely correlated with the mass flow rate of gas and liquid. The complex relationships between the signal features and the two-phase flow rate are established through the use of a back propagation (BP) neural network. Experiments were carried out in the horizontal tubes with 50ram inner diameter, ooerated with water flow rate in the range of 0.2m^3·h^-1 to 4m3·h^-1, gas flow rate in the range of 100m^3·h^-1 to 1000m^3·h^-1, and pressure at 400kPa and 850kPa respectively, where the temperature is ambient temperature. This article includes the principle of wet gas meter development, the experimental matrix, the signal processing techniques and the achieved results. On the basis of the results it is suggested that the slotted orifice couple with a trained neural network may provide a simple but efficient solution to the wet gas meter development. 相似文献
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本文根据电极传感器是等势体这一特性建立了一种新型的电流激励有限元线电极模型,真实的仿真了物理线电极模型。由得到的仿真结果表明,采用等势体线电极模型可以更细腻地反映实际传感器测量最敏感场域中的电场分布及其变化,有助于从理论上分析测量过程中,电流从流入电极到流出电极的空间分布规律,实现电阻层析测量系统中传感器的优化设计。 相似文献
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基于RBF神经网络和粒子群算法的ECT传感器结构优化 总被引:3,自引:3,他引:0
给出一种RBF神经网络与粒子群算法相结合的电容层析成像(ECT)传感器结构参数优化方法。该方法以敏感场整体灵敏度大小等系统性能为优化目标,基于管壁厚度、屏蔽层厚度、径向屏蔽插入管壁深度、径向电极宽度、电极宽度(中心夹角)、管壁材料的相对介电常数、屏蔽层填充物相对介电常数7种重要的结构参数进行试验。应用RBF神经网络对多组结构参数以及对应的系统性能指标进行学习,得到回归模型,并应用粒子群算法进行寻优。结果显示,该方法参数寻优范围大,局限性小,寻优过程收敛快。优化后的系统整体灵敏度增大,成像质量改进。 相似文献
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用图像相关系数和图像方差分析电阻层析测量反演算法的性能 总被引:1,自引:1,他引:0
根据不同两相流流型下获得的电阻抗层析测量结果,用图像相关系数和图像方差两个技术参数分别考察了线性反投影算法(LBP)、改进牛顿-拉夫逊类算法(MNR)、基于遗传算法的LBP-MNR-GA组合算法的反演能力.仿真表明,LBP对简单流型有较快和较好的反演能力,MNR反演复杂流型的能力比LBP强,LBP-MNRGA组合算法的反演能力最强. 相似文献
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A novel noninvasive approach, based on flow-induced vibration, to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted under the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a transducer installed on outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform. A “binary tree” multi-class support vector machine(MCSVM) classifier, with the normalized feature vector as inputs, and Gaussian radial basis function as kernel function, was developed to identify the three typical flow regimes in-cluding stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effec-tively flow regimes and its identification accuracy is about 93.3%. Comparing with the other classifiers, the MCSVM classifier has higher accuracy, especially under the case of small samples. The noninvasive measurement approach has great application prospect in online flow regime identification. 相似文献
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