Discharge coefficient of rectangular sharp-crested side weirs,Part I: Traditional weir equation

A comprehensive study was performed to examine the flow characteristics over rectangular sharp-crested side weirs based on the traditional weir equation. To obtain a generally convenient discharge coefficient relationship, series of experiments were conducted according to manipulation of different prevailing parameters. The flow regime was consistently subcritical for upstream Froude numbers ranging from 0.08 to 0.91. Furthermore, experimental data sets of the former investigators were also applied. In order to identify the most important parameters affecting the discharge coefficient of rectangular sharp-crested side weirs, a sensitivity analysis was carried out based upon an artificial neural network modeling. Results of the sensitivity analysis indicated the Froude number to be the most influential parameter on discharge coefficient. Accordingly, a power equation is derived for estimating the discharge coefficient, which is applicable for both sub- and supercritical flow conditions simultaneously. Moreover, considering all the influential parameters, a nonlinear correlation was obtained with the highest precision to determine the discharge coefficient of sharp-crested rectangular side weirs.     

Research on the measuring characteristics of a new design butterfly valve flowmeter

Flowmeters and control valves are important components of flow measurement and control in heating, ventilating, and air conditioning (HVAC) system, which directly or indirectly impact building room comfort and energy costs. Valves as resistance components produce differential pressure which in turn can be used for flow measurement. This paper studies the function among valve opening position, pressure difference and flowrate of a new designed butterfly valve. The flow model of the butterfly valve is established based on the Bernoulli equation, the discharge coefficient C under different valve opening conditions are studied by CFD simulations and verified by experiments. The simulation results show that the discharge coefficient C reached a stable value of 0.67–0.70 as Reynolds number exceeded 5000, and the permanent pressure loss ratio is range from 0.95 to 0.37 corresponding to opening range from 10° to 70°. The correctness of the simulation results of C is verified by experiments, in which C is about 0.60. With the corrected values obtained from experiments, the simulation results are instructive to practice. The new designed butterfly valve flowmeter can be used efficiently in HVAC system, especially in variable air volume (VAV) air conditioning system. And the work of this paper offers a reference for other types of valve flowmeters in fluid control processes.     

A neural network approach for prediction of discharge in straight compound open channel flow

Most natural rivers and streams consist of two stage channels known as main channel and flood plains. Accurate prediction of discharge in compound open channels is extremely important from river engineering point of view. It helps the practitioners to provide essential information regarding flood mitigation, construction of hydraulic structures and prediction of sediment load so as to plan for effective preventive measures. Discharge determination models such as the single channel method (SCM), the divided channel method (DCM), the coherence method (COHM) and the exchange discharge method (EDM) are widely used; however, they are insufficient to predict discharge accurately. Therefore, an attempt has been made in this work to predict the total discharge in compound channels with an artificial neural network (ANN) and compare with the above models. The mean absolute percentage error with artificial neural networks is found to be consistently low as compared to other models.     

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.     

Flow profile identification with multipath transducers

Accuracy of flow rate determination is very critical to an ultrasonic gas flowmeter, which is sensitive to the flow profile under measurement and cannot be obviously improved with traditional weighted integration methods for multipath transducers. Therefore, on one hand, more attentions are paid to intelligent learning algorithms (e.g. artificial neural networks) in recent studies to accurately construct the mapping relation between multipath velocities and the flow rate. However, the bottleneck that a trained network is only customized for a certain flow profile greatly restricts its application. On the other hand, many researchers turn to reconstruct the flow field but so far all flow visualization methods cost too much and are difficult to realize online. In this paper, an intelligent method of flow profile identification for multipath ultrasonic flowmeters is developed to solve the above predicament. Based on support vector machine, a multiclass identifier is constructed to automatically identify the flow profile to be measured from 65 typical flow patterns. Extremely high identification accuracy of 99.49% for test and 100% for training is achieved while the test result still reaches 88.46% even when a measurement uncertainty of ±2% is considered. A comparison with extreme leaning machine further reveals the identification performance and robustness of the proposed flow profile identifier. Different piping configurations, installation positions and angles can be therefore accurately identified, which indirectly realizes the flow visualization and can comprise an intelligent system of ultrasonic flow measurement when combined with the intelligent flow rate determination algorithms.     

Estimation of flow rates of individual phases in an oil-gas-water multiphase flow system using neural network approach and pressure signal analysis

Up until now, different methods, including; flow pressure signal, ultrasonic, gamma-ray and combination of them with the neural network approach have been proposed for multiphase flow measurement. More sophisticated techniques such as ultrasonic waves and electricity, as well as high-cost procedures such as gamma waves gradually, can be replaced by simple methods. In this research, only flow parameters such as temperature, viscosity, pressure signals, standard deviation and coefficients of kurtosis and skewness are used as inputs of an artificial neural network to determine the three phase flow rates. The model is validated by the field data which were obtained from separators of two oil fields and 6 wells over ten-month with 8 h interval (totally 5400 sets of data). A linear relation can be observed between the actual data and the predictions which were obtained from separators and neural network approach, respectively. Furthermore, it is shown that using feed forward neural network with Levenberg–Marquardt algorithm which has two hidden layers is sufficient to determine the flow rates. Also, it is tried to see the effect of flow regimes on the results of neural network approach by determining kurtosis and skewness coefficients for different flow regimes in a horizontal pipeline.     

Research on calculation method of free flow discharge based on artificial neural network and regression analysis

Taking the Huaidian Sluice on the Shaying River in China as an example, this paper establishes the calculation model of the free flow based on artificial neural network and regression analysis. Four forms of discharge coefficient calculation equations were obtained by regression analysis, and three neural network models were established. The model is fully verified by using the measured data. The experimental results show that the third-order polynomial and multilayer perceptron neural network have better adaptability. The advantages and disadvantages of the different methods are analyzed and the cause of the error is identified. It provides a theoretical basis for dealing with the discharge calculation of small and medium dam.     

在科学研究和生物制药学系统中实现可追踪的气体流量监测与控制

科学和制药进展方面越来越多的研究都依赖于对细胞生长和复杂生化反应的了解和控制。在以上过程中实现气体精确和可追踪的监测和控制很有必要。对诸如二氧化碳、氮气、空气等气体的测定与控制已经可以通过一个简单的可变截面流量计成功实现。但可变截面流量计在精确度、可追踪性和最大允许压力等方面仍然具有明显的局限性。本文所介绍的热式质量流量计和控制系统为流量测定和控制提供了更高的准确度和直接的可追溯性,且此准确度和追溯性几乎不受压力和温度变化的影响。     

Improved numerical integration method for flowrate of ultrasonic flowmeter based on Gauss quadrature for non-ideal flow fields

Multipath ultrasonic flowmeters with large diameter are widely used in industry. And their measurement performances are sensitive to velocity profiles in conduits. Gauss–Jacobi and Optimized Weighted Integration for Circular Sections (OWICS) method are commonly applied in flow measurement of multipath ultrasonic flowmeters, both of which assume ideal flow in pipes. They are not proper for non-ideal flow measurement. Therefore, an improved numerical integration method for flowrate based on Gauss quadrature is proposed. With this method, optimum relative path heights and corresponding weights are determined according to specific disturbed flows. By comparison Gauss–Jacobi, OWICS with the improved method, the validity of the proposed method is verified for typical disturbed flows based on both theoretical analysis and experiments, and measurement performances of ultrasonic flowmeters are improved significantly.     

动态流量软测量系统中神经网络训练策略的研究

将神经网络运用到动态流量的软测量中，探索解决液压伺服系统中对瞬时动态流量的测试问题是该领域的二个难点和热点问题，该文在概要介绍了神经网络动态流量软测量系统的总体设计和神经网络结构的确定方法之后，重点讨论了神经网络的训练策略，最后通过实验验证了该训练策略的性能。     

超声波流量计影响因素的分析及对策

气体超声波流量计在信号、硬件电路、流场等因素的问题,严重制约了产品的计量精度、稳定性、重复性等基本指标,制约了产品化的发展.分别从上述三方面深入研究了超声波流量计影响因素,并提出一些针对性的解决方法,对今后深入研究超声波流量,提高流量计的适应性和精度具有实际作用.     

级环境下斜流压气机叶片扩压器气动优化设计

以某斜流压气机的叶片扩压器为研究对象,采用人工神经网络和遗传算法相结合对其进行气动优化设计.优化后,斜流级总压比提高了3.85％,效率提高了2.07％.叶片扩压器静压恢复系数和总压恢复系数也分别大幅提高至0.7和0.95的水平.与原方案相比,扩压器叶片最大负荷点从10％弦长后移至25％弦长.扩压器叶栅通道靠近机匣区域的...     

Pressure effects on Coriolis mass flowmeters

The current status of available work regarding the pressure effect on Coriolis mass flowmeters is reviewed, which shows significant improvement in the latest generation of Coriolis flowmeters. A theoretical method using the linear damping model is proposed to understand the pressure effect. This new method applied to Coriolis flow sensors provides intuitive insight into the flow-generated signal by studying undamped natural frequencies and mode shapes. Most importantly this method can be used to model virtually any shape and configuration of flow sensors as found in the practical design. It is found that when the pressure changes it alters the superimposed contribution and the mass flow measurement can deviate from the reference condition. Experimental results from both low and high pressure flow tests are reported, which are in general agreement with the theoretical prediction. Further specific work is finally suggested which may advance our understanding and improve the Coriolis mass flow measurement technology.     

Approximation of the discharge coefficient of differential pressure flowmeters using different soft computing strategies

Due to its importance in flow measurement and instrumentation, as well as its frequent application in differential pressure flowmeters, orifice discharge coefficient (C_d) needs to be estimated precisely. In this study, different soft computing models (including multiple linear regression (MLR), group method of data handling (GMDH), multivariate adaptive regression splines (MARS), M5P tree model, and random forest (RF)) were employed for the first time in estimation of the C_d value, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), scattering index (SI), and Nash–Sutcliffe model efficiency coefficient (NSE) were used as the statistical indicators for validating the performance of each soft computing model. The statistical indicators approved the superiority of the RF model over the other models, while the MARS model also showed a competitive prediction potential over M5P, GMDH, and MLR models. The findings of this computational study clearly demonstrated that the implemented soft computing strategy had the capability to be used in precise estimation of the C_d of the orifice meter, specifically, in situations where the measurement of the parameters in deterministic equation is not practically feasible.     

Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks

Weirs are small overflow dams used to alter and raise water flow upstream and regulate or spill water downstream watercourses and rivers. This paper presents the application of artificial neural network (ANN) to determine the discharge coefficient (Cd) for a hollow semi-circular crested weirs. Eighty five experiments were performed in a horizontal rectangular channel of 10 m length, 0.3 m width and 0.45 m depth for a wide range of discharge. The results of examination for discharge coefficient were yielded by using multiple regression equation based on dimensional analysis. Then, the results obtained were also compared using ANN techniques. A multilayer perceptron MLP algorithm FFBP network was developed. The optimal configuration of ANN was [2,10,1] which gave mean square error (MSE) and correlation coefficient (R) of 0.0011 and 0.91, respectively. Performances of ANN model reveal that the Cd could be better estimated by the ANN technique in comparison with Cd obtained using statistical approach.     

Measurement of steam flow rates using a clamp-on ultrasonic flowmeter with various wetness fractions

Most of the heat in industrial plants is supplied by steam. To minimize energy waste, measuring the steam flow rates in existing pipes is important. Clamp-on ultrasonic flowmeters are used for this purpose, for which the sensors are attached to the pipe wall. However, flow conditions that can be used are limited because the signal-to-noise ratio of the ultrasonic signal in a steam flow is low. Furthermore, the steam wetness increases with heat losses, which may affect measurement results. Therefore, flow rate measurements in wet steam flows using clamp-on ultrasonic flowmeters have not been fully established. In this study, steam flow rates with various wetness fractions and system pressures were measured using a laboratory-made clamp-on ultrasonic flowmeter. The results show that flow rates in wet steam could be determined within a 10% error under general conditions in a steam piping system, although the conversion factor from line-average to area-average velocities was calibrated in superheated conditions, and the speed of sound in saturated conditions at each pressure was used. However, the error of the flow rates tended to increase with the wetness fraction and was biased toward positive values. The speed of sound and liquid volume fraction were evaluated at different wetness fractions. The flow rate error due to the change in sound speed was less than 1%, and 1.2% of the flow rates were overestimated owing to the liquid volume fraction. The velocity distribution in wet steam was considered different from that in the superheated steam owing to the existence of the liquid phase, and the change in velocity profile may lead to an overestimation of the steam flow rates in the wet steam condition.     

Numerical analysis of discharge coefficients in aerostatic bearings with orifice-type restrictors

Determining film pressure by solving the Reynolds equation is more effective than conducting bearing experiments or computational fluid dynamics simulations. The Reynolds equation can be solved easily in a numerical model; however, the accuracy of each model is dominated by the orifice discharge coefficient. This study compared two orifice flow models in inherent orifice restrictors and investigated the influences of geometry and flow parameters on the discharge coefficient. The results indicate that both orifice flow models can be used in inherent orifice restrictors, and the discharge coefficient of orifice-type restrictors is sensitive to the orifice diameter and film thickness.     

An intelligent approach for calculating natural gas compressibility factor and its application in ultrasonic flow meters

The current study presents an intelligent method for calculating natural gas compressibility factor. The method requires three easily measurable properties including pressure, temperature, and speed of sound as inputs. As sound speed could be measured with ultrasonic flow meters, temperature, and pressure with appropriate sensors, the real-time natural gas compressibility factor could be calculated easily. The presented method eliminates the high cost of determining compressibility based on measuring natural gas composition. Artificial Neural Network is employed to develop the method. The artificial neural network is trained in a way to accept pressure, temperature, and speed of sound as inputs. To train an artificial neural network, the 30,000 random datasets of natural gas compositions were utilized. To check the validity and accuracy of the developed artificial neural network, four different natural gas compositions are selected and the compressibility factor are compared with the GERG-2008 equation of state (as standard and accurate method for calculating natural gas properties) results. The results reported the average absolute percent deviation is less than 0.7% for the compressibility factor calculation by utilizing the proposed method.     

Acoustic emission data assisted process monitoring

Gas-liquid two-phase flows are widely used in the chemical industry. Accurate measurements of flow parameters, such as flow regimes, are the key of operating efficiency. Due to the interface complexity of a two-phase flow, it is very difficult to monitor and distinguish flow regimes on-line and real time. In this paper we propose a cost-effective and computation-efficient acoustic emission (AE) detection system combined with artificial neural network technology to recognize four major patterns in an air-water vertical two-phase flow column. Several crucial AE parameters are explored and validated, and we found that the density of acoustic emission events and ring-down counts are two excellent indicators for the flow pattern recognition problems. Instead of the traditional Fair map, a hit-count map is developed and a multilayer Perceptron neural network is designed as a decision maker to describe an approximate transmission stage of a given two-phase flow system.     

A study of mass flow rate measurement based on the vortex shedding principle

Mass flow rate measurement is very important in the majority of industry processes because the mass of fluid is not affected by ambient temperature and pressure as the volume will be. Conventional mass flow rate is normally derived from the volumetric flow rate multiplied by fluid density. The density can be obtained by a densitometer or calculated according to the temperature and pressure measured by a thermometer and pressure gauge respectively. However the measurement accuracy is not always satisfactory. Flowmeters directly measuring mass flow rate have been studied and developed recently, such as Coriolis and thermal flowmeters. Unfortunately they still have some limits in practical applications. A new method in which mass flow rate can be directly measured based on the vortex shedding principle is presented in this paper. As a vortex flowmeter, von Kàrmàn vortex shedding is generated by a bluff body (vortex shedder), leading to a pressure drop and pressure fluctuation. A single differential pressure sensor is employed to detect the pressure difference between upstream and downstream sides of the vortex shedder. Both vortex shedding frequency and pressure drop are contained from the output signal of the differential pressure sensor, so that the mass flow rate can be obtained from the pressure signal. Numerical simulation has been done to analyze the characteristics of the fluid field and design the measurement device. The Computational Fluid Dynamics (CFD) codes Fluent were used in the numerical simulation. Experiments were carried out with water and gas, and the results show that this method is feasible and effective to measure the mass flow rate. This method has also robustness to disturbances such as pipe vibration and fluid turbulence.