Diagnostic flow metering using ultrasound tomography

For an accurate flow metering without considering the influences of flow control devices such as valves and elbows in closed conduits, velocity distribution in the cross-sectional area must be integrated. However, most flow meters, including multi-path ultrasonic, electromagnetic or Coriolis mass flow meters, require assumptions on the fully-developed turbulent flows to calculate flow rates from physical quantities of their own concern. Therefore, a long straight pipe has been a necessary element for accurate flow metering because the straight pipe can reduce flow disturbances caused by flow control devices. To reduce costs due to the installation of long straight pipes, another flow metering technique is required. For example, flow rates can be estimated by integrating velocity distributions in the crosssection of conduits. In the present study, ultrasound tomography was used to find the velocity distribution in the cross-section of a closed conduit where flow was disturbed by a Coriolis mass flow meter or a butterfly valve. A commercial multi-path ultrasonic flow meter was installed in the pipeline to measure the line-averaged velocity distribution in the pipe flow. The ultrasonic flow meter was rotated 180° at intervals of 10° to construct line-averaged velocity distributions in Radon space. Flow images were reconstructed by using a backprojection algorithm (inverse Radon transform). Flow diagnostic parameters were defined by calculating statistical moments, i.e., average, standard deviation, skewness, and kurtosis, based on the normalized velocity distribution. The flow diagnostic parameters were applied to flow images to find whether the parameters could discern flow disturbances in the reconstructed velocity distribution.     

基于多特征参数和概率神经网络的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的非平稳特性,提出了一种基于多特征参数和概率神经网络的滚动轴承故障诊断方法。首先利用经验模态分解（EMD）方法将采集到的滚动轴承原始振动信号分解为有限个固有模式函数（IMF）之和,然后提取表征故障信息的若干个IMF的能量、峭度和偏度作为概率神经网络的输入参数来进行故障分类。试验结果表明,该方法可以准确、有效地识别滚动轴承的工作状态和故障类型,是一种可行的滚动轴承故障诊断方法。     

Identification of two-phase flow regime using ultrasonic phased array

Flow regime is one of the key characteristics of gas-liquid two-phase pipe-flows and its identification is essential for several industrial applications. In this paper, the ultrasonic phased array technology is used to identify flow regimes of two-phase (air-water) vertical flow. The ultrasonic phased array can perform multi-point, omnidirectional detection to obtain high-resolution data suitable for image processing. The scanned images, which have distinctive features, are subjected to a series of image-treatment techniques, such as principle component analysis, to extract information necessary for flow regime identification. The K-nearest neighbors (KNN) classification algorithm is then used to identify flow regimes with high accuracy.     

Neural network approach to ultrasonic flow measurements

Mathematical models and numerical methods offer a flexible tool to investigate flow disturbance effects on flowmeters of different types. In this paper a simple neural network based approach has been used to study the velocity profile dependence of ultrasonic flowmeters. Neural networks have been used in two ways: to interpolate the velocity profiles in the points needed for the modelling of ultrasonic flow measurement, and to compute the weights for different paths of multipath ultrasonic flowmeters. In the former case two types of neural networks, multilayer perceptron networks and radial basis function networks, have been investigated. In the latter case, a single layer neural network with linear neurons is first trained with known velocity profiles, and the weights determined by the network have then been used in the computation of the errors in other piping configurations. The results have been compared with the errors computed with the weights for different paths given in Pannel CN, Evans WAB, Jackson DA. A new integration technique for flowmeters with chordal paths, Flow Measurement and Instrumentation 1990;1:216–224.     

Application of electrical resistance tomography to two-phase pipe flow parameters measurement

This paper introduces the TERT-IV prototype developed by Tianjin University. The application of the TERT-IV system to measurement parameters of two-phase flow has been studied. The methods of analyzing measured data of ERT system are presented and applied to identify flow regimes and estimate void fraction. For the several typical flow regimes, the methods of principal component analysis and artificial neural network to identify the two-phase flow regimes is presented, and that is proved to have higher recognition rate by experimental test. For the different phase distribution on a pipe cross-section, the methods of relative changes summation and polynomial regression are used to estimate void fraction, and are proved to be possible by comparing the results of simulation calculation to the analytic results of experimental measured data.The research results show that the method is feasible using feature extraction and analysis data to measure the parameters of two-phase flow under the different flow conditions, and prove that it is possible to monitor on-line the transportation process of air/water two-phase flow using ERT system.     

Modeling subcritical multi-phase flow through surface chokes with new production parameters

The produced hydrocarbons from underground reservoirs must eventually pass through surface chokes installed to control the surface flow rate at an optimum value, which should regularly be checked against the recommendations of the production engineers to prevent problems such as water coning. Accurate prediction of the surface flow rate is, therefore, crucial as it will lead to fulfilling the development plan goals of the reservoir and production optimization. In this regard, many correlations have been developed to predict the flow rate through surface choke and most of them being developed from only one dataset gathered from a single reservoir, hence with limited prediction capability and high error. Furthermore, these correlations predict the oil flow rate only as a function of wellhead pressure, gas-oil ratio, and choke size. In this study, two machine learning techniques are used to develop models for better prediction of the multi-phase flow rate for the oil wells using two new parameters of basic sediment and water (BS&W) and fluid temperature which were overlooked previously. A total of 182 production tests were utilized in developing these models which are covering a wide range of data. Graphical and statistical approaches are utilized to compare the forecasted values against the field data. Furthermore, absolute error is used as a statistical approach to assess the developed models based on machine learning in comparison to conventional correlations available in the published literature. The findings illustrate that an acceptable relation exists between the field data and predicted values with coefficients of determination equal to 0.9840 and 0.9706 for artificial neural network (ANN) and least squares support vector machine coupled simulated annealing (LSSVM-CSA), respectively, based on total datapoints. The results from this study will greatly assist petroleum engineers to have particular estimations of liquid flow rates from wellhead chokes.     

Application of artificial neural networks instead of the orifice plate discharge coefficient

Differential pressure flowmeters are very often used in many industries. Therefore, the improvement of this method of flow measurement is an important task of flow measurement and instrumentation. One of the important characteristics of differential pressure flowmeters is the discharge coefficient of the flow transducers. A large number of studies and publications were devoted to modeling this coefficient. Therefore, in the framework of this research, this coefficient is simulated using artificial neural networks. The neural representation of this characteristic is made in the form of a multilayer perceptron. In this paper, we replace the traditional equation for the discharge coefficient with an artificial neural network. The advantages and disadvantages of such application of neural networks as discharge coefficients are discussed. The analysis of the results of gas flow measurement, where the neural network is used instead of the traditional equation, is presented. The estimation of flow rate measurement errors with such an approach is made; the error of calculation of the discharge coefficient is estimated.     

Identification of gas-liquid two-phase flow patterns in a horizontal pipe based on ultrasonic echoes and RBF neural network

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall. A two-dimensional acoustic pressure numerical model is established to investigate the ultrasonic pulse transmission behavior between the wall-gas and wall-liquid interface. Experiments were also carried out at a horizontal air-water two-phase flow loop to measure the ultrasonic echo pulse signals of stratified flow, slug flow, and annular flow. It is interesting to find that the attenuation of the ultrasonic pulse at the wall-liquid interface is faster than the attenuation at the wall-gas interface. An RBF neural network is constructed for online flow pattern identification. The normalized envelop area and the area ratios of the echo spectrum are selected as the input parameters. The results show that the stratified flow, slug flow, and annular flow can be identified with an accuracy of 94.0%.     

Identification of gas/solid two-phase flow regimes using electrostatic sensors and neural-network techniques

In the gas/solid two-phase system, solid particles can accumulate a large number of electrostatic charges because of collision, friction and separation between particles or between particles and the wall. Through the detection and processing of the induced fluctuation charge signal, a measuring system can obtain two-phase flow parameters, such as flow regime, concentration and velocity. A novel methodology via introducing the characteristics of speech emotion recognition into flow regime identification is proposed for improving the recognition rate in gas/solid two-phase flow systems. Three characteristics of electrostatic fluctuation signals detected from an electrostatic sensor are extracted as the input of back propagation (BP) neural networks for flow regime identification. They are short-term average energy, Mel-frequency cepstral coefficients (MFCC) and cepstrum. The results show that the method based on each characteristic of the electrostatic fluctuation signal and BP neural networks can identify the three flow regimes of gas/solid two-phase flow in a horizontal pipe, and the identification rate of the method based on the three characteristics and BP neural networks is up to 97%, much higher than the methods based on a single characteristic.     

基于经验模态分解和BP神经网络的油气两相流流型辨识

基于经验模态分解（empidcal mode decomposition，EMD）BP神经网络，提出了油气两相流流型辨识的新方法。应用EMD将差压信号分解成不同频率尺度上的单组分之和，并提取组分的归一化能量作为流型辨识特征量。BP神经网络以这些能量特征量为输入对油气两相流不同流型（包括泡状流、塞状流、层状流、弹状流和环状流）进行分类。实验结果表明，本文提出的流型辨识方法是有效的，其中泡状流、塞状流、层状流、弹状流和环状流的辨识精度分别为100％、89．4％,93．3％、96.3％和96.9％。     

Effect of surface roughness parameters on mixed lubrication characteristics

In this paper, a computer program was developed to generate non-Gaussian surfaces with specified standard deviation, autocorrelation function, skewness and kurtosis, based on digital FIR technique. A thermal model of mixed lubrication in point contacts is proposed, and used to study the roughness effect. The area ratio, load ratio, maximum pressure, maximum surface temperature and average film thickness as a function of skewness and kurtosis are studied at different value of rms. Numerical examples show that skewness and kurtosis have a great effect on the contact parameters of mixed lubrication.     

Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps

The accurate prediction of flow regimes is vital for the analysis of behaviour and operation of gas/liquid two-phase systems in industrial processes. This paper investigates the feasibility of a non-radioactive and non-intrusive method for the objective identification of two-phase gas/liquid flow regimes using a Doppler ultrasonic sensor and machine learning approaches. The experimental data is acquired from a 16.2-m long S-shaped riser, connected to a 40-m horizontal pipe with an internal diameter of 50.4 mm. The tests cover the bubbly, slug, churn and annular flow regimes. The power spectral density (PSD) method is applied to the flow modulated ultrasound signals in order to extract frequency-domain features of the two-phase flow. Principal Component Analysis (PCA) is then used to reduce the dimensionality of the data so as to enable visualisation in the form of a virtual flow regime map. Finally, a support vector machine (SVM) is deployed to develop an objective classifier in the reduced space. The classifier attained 85.7% accuracy on training samples and 84.6% accuracy on test samples. Our approach has shown the success of the ultrasound sensor, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which is beneficial to operators in industrial practice. The use of a non-radioactive and non-intrusive sensor also makes it more favorable than other existing techniques.     

IDENTIFICATION OF GAS–SOLID TWO-PHASE FLOW REGIMES USING HILBERT–HUANG TRANSFORM AND NEURAL-NETWORK TECHNIQUES

This work presents a new methodology for flow regime identification in a gas–solid two-phase flow system. The approach of identification employs the artificial neural network (ANN) technique, considering the applications with electrostatic sensor as a measuring device and Hilbert–Huang transformation (HHT) as the post-processing method. The electrostatic fluctuation signals detected from an electrostatic sensor are processed using HHT to gain the Hilbert marginal spectrums. Then four characteristic parameters of the marginal spectra are extracted as the input of BP neural network for flow regime identification. They are subband energy (SE), first-order difference of subband energy (DSE), subband energy cepstrum coefficients (SECC), and first-order difference of the subband energy cepstrum coefficients (DSECC). The results show that the characteristic parameters of the Hilbert marginal spectrum of the electrostatic signal can identify the three flow regimes of gas–solid two-phase flow in a horizontal pipe, especially the DSECC.     

Modeling the effect of skewness and kurtosis on the static friction coefficient of rough surfaces

Engineering surfaces possess roughnesses that exhibit asymmetrical height distributions. However, the Gaussian distribution is most often used to characterize the topography of surfaces, and is also used in models to predict contact and friction parameters. In this paper, the effects of kurtosis and skewness on different levels of surface roughness are investigated independently. This is accomplished by adopting the Pearson system of frequency curves and used in conjunction with a static friction model for rough surfaces to calculate the friction force and friction coefficient. This study is the first attempt to independently model the effect of kurtosis and skewness on the static friction and friction coefficient. It is predicted that surfaces with high kurtosis and positive skewness exhibit lower static friction coefficient compared to the Gaussian case. More importantly, it is predicted that, for high kurtosis values, the static friction coefficient decreases with decreasing external force rather than increasing as seen with increasing skewness. This is a very promising result for applications involving smooth lightly loaded contacts such as magnetic storage devices and microelectromechanical systems. The practical significance of the present model is specifically demonstrated on static friction predictions in magnetic storage head–disk interfaces. Such predictions can be used to determine the optimal characteristics of such devices prior to fabrication to achieve lower friction in terms of surface roughness, mechanical properties, apparent contact area, and operational environment.     

The contact behavior of elastic/plastic non-Gaussian rough surfaces

A three-dimensional contact analysis was conducted to investigate the contact behavior of elastic--perfectly plastic solids with non-Gaussian rough surfaces. The effect of skewness, kurtosis and hardness on contact statistics and the effect of skewness and kurtosis on subsurface stress are studied. Non-Gaussian rough surfaces are generated by the computer with skewness, Sk, of −0.3, 0.0 and 0.3, and kurtosis, K, of 2.0, 3.0 and 4.0. Contact pressures and subsurface stresses are obtained by contact analysis of a semi-infinite solid based on the use of influence functions and patch solutions. Variation of fractional elastic/plastic contact area, maximum contact pressure and interplanar separation as a function of applied load were studied at different values of skewness and kurtosis. Contact pressure profiles, von Mises stresses, tensile and shear stress contours as a function of friction coefficient were also calculated for surfaces with different skewness and kurtosis. In this study, it is observed that surfaces with Sk = 0.3 and K = 4 in the six surfaces considered have a minimum contact area and maximum interplanar separation, which may provide low friction and stiction. The critical material hardness is defined as the hardness at which severe level of plastic asperity deformation corresponding to the Greenwood and Williamson’s cut-off A _plastic/A _real = 0.02 occurs for a given surface and load condition. The critical material hardness of surfaces with Sk = 0.3 and K = 4 is higher than that of other surfaces considered.     

Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function

The problem of how to accurately measure the flow rate of oil–gas–water mixtures in a pipeline remains one of the key challenges in the petroleum industry. This paper proposes a new methodology for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems using dual energy fan-beam gamma-ray attenuation technique and artificial neural networks. The novelty of this study in comparison with previous works, is using just 4 extracted features (photo peaks of ²⁴¹Am and ¹³⁷Cs in 2 detectors) from the gamma ray spectrums instead of using the whole gamma ray spectrum, which reduces the undesired noises and also improves the speed of recognition in real situations. Radial basis function was used for developing the neural network model in MATLAB software in order to classify the flow patterns (annular, stratified and homogenous) and predict the value of volume fractions. The ideal and static theoretical models for flow regimes have been developed using MCNP-X code. The proposed networks could correctly recognize all the three different flow regimes and also determine volume fractions with mean absolute error of less than 5.68% according to the recognized regime.     

Contact Analysis of Non-Gaussian Surfaces for Minimum Static and Kinetic Friction and Wear

Most statistical contact analyses assume that surface heights and peak (summit) height distributions follow a Gaussian distribution. However, engineering surfaces are frequently non-Gaussian with a degree of non-Gaussian character dependent upon materials and surface finishing processes used. For example, magnetic rigid disk surfaces used in magnetic storage industry are highly non Gaussian. The use of a Gaussian analysis in such cases can lead to erroneous results. This study for the first time presents a method to carry out a statistical analysis of non-Gaussian surfaces. Real area of contact, number of contacts, contact pressure and meniscus force (in wet interfaces) are calculated for probability density functions having different skewness and kurtosis. From these curves, the optimum value of skewness and kurtosis can be predicted for minimum static/kinetic friction. It is found that a range of positive skewness (between 0.3–0.7) and a high kurtosis (greater than five) significantly lower the real area of contact and meniscus contribution implying low friction and wear. Also, sensitivity of film thickness to static friction goes down for a surface with a positive skewness and a high kurtosis.     

Electric spark discharge as an ultrasonic generator in flow measurement situations

In this paper, a gap discharge approach to create acoustic signals for ultrasonic low pressure gas flow measurements is investigated. The objective is to develop an ultrasonic gas flow meter system that is capable of operation in extreme industrial environments. These environments might have extremely high temperatures (1200 °C), moisture, steam, dust, low gas pressure and large transmission distances.Most other types of ultrasonic transducers found show sensitivity to such conditions: their operation suffers, or they may even stop functioning if exposed to such environments. The development of new transducer technology is therefore crucial to allow ultrasonic flow measurements in extreme industrial environments. In this paper, the gap discharge emitter is evaluated as a candidate to be used in these applications. Its capabilities as a sound source are investigated, and its impact on flow meter performance is estimated. It can be concluded that, despite the uncertainties it introduces to a flow meter system, it stands out as a strong candidate to be used as an acoustic emitter in a gas flow meter system for extreme environments.     

Separated two-phase flow regime parameter measurement by a high speed ultrasonic pulse-echo system

In this work, a high speed ultrasonic multitransducer pulse-echo system using a four transducer method was used for the dynamic characterization of gas-liquid two-phase separated flow regimes. The ultrasonic system consists of an ultrasonic pulse signal generator, multiplexer, 10 MHz (0.64 cm) ultrasonic transducers, and a data acquisition system. Four transducers are mounted on a horizontal 2.1 cm inner diameter circular pipe. The system uses a pulse-echo method sampled every 0.5 ms for a 1 s duration. A peak detection algorithm (the C-scan mode) is developed to extract the location of the gas-liquid interface after signal processing. Using the measured instantaneous location of the gas/liquid interface, two-phase flow interfacial parameters in separated flow regimes are determined such as liquid level and void fraction for stratified wavy and annular flow. The shape of the gas-liquid interface and, hence, the instantaneous and cross-sectional averaged void fraction is also determined. The results show that the high speed ultrasonic pulse-echo system provides accurate results for the determination of the liquid level within +/-1.5%, and the time averaged liquid level measurements performed in the present work agree within +/-10% with the theoretical models. The results also show that the time averaged void fraction measurements for a stratified smooth flow, stratified wavy flow, and annular flow qualitatively agree with the theoretical predictions.     

基于Elman神经网络的压力传感器温补偿的研究

当受到温度、电磁波等外界因素的影响,压力传感器的测量精度较低,测量值容易有较大的波动.因此,当压力传感器运用于汽车等移动设备时性能下降.以对温度极其敏感的CYJ-101压力传感器为例,建立18组样本数据在Elman模型的神经网络中进行温度补偿训练.仿真结果显示,通过Elman神经网络的温度补偿,传感器的测量误差降低约2...