Similarity of the asymmetric swirl generator and a double bend in the near-field range

An asymmetric swirl generator (ASG) is considered to replace the current swirl generator in the upcoming 2020 revision of the standard for heat meters EN 1434. While recent studies have shown its superiority with respect to a reproducible representation of the double bend out-of-plane (DB) flow disturbance in the far field, there are still open questions regarding the similarity in the near-field range and the determination of an optimum testing distance. In this paper, we examine the performance of an ASG in the potential testing range and investigate an increase of the segmental orifice plate. Laser Doppler anemometry (LDA) measurements downstream of the ASGs and a DB carried out at Reynolds numbers ($Re$) of $5\times {10}^4$ and $5\times {10}^5$ are evaluated based on a comparison of the flow patterns and a quantitative analysis by means of performance indicators. The results indicate, that the original version of the ASG does not reach the level of asymmetry and swirl provided by the DB in the near-field range. An increase of the orifice height yields higher swirl and asymmetry, hence, a better similarity of the flow characteristics. A maximum degree of resemblance with the crescent-shaped velocity patterns of the DB is found seven diameters downstream of the modified ASG. In contrast, a testing distance of two diameters or less does not represent the DB, as the early flow development bears the traces of the disturber's geometry. The results may be considered for the implementation of the test procedure in EN 1434 and the next revision of the standards for water meters.     

Implementation of unscented transform to estimate the uncertainty of a liquid flow standard system

First-order partial derivatives of a mathematical model are an essential part of evaluating the measurement uncertainty of a liquid flow standard system according to the Guide to the expression of uncertainty in measurement (GUM). Although the GUM provides a straight-forward method to evaluate the measurement uncertainty of volume flow rate, the first-order partial derivatives can be complicated. The mathematical model of volume flow rate in a liquid flow standard system has a cross-correlation between liquid density and buoyancy correction factor. This cross-correlation can make derivation of the first-order partial derivatives difficult. Monte Carlo simulation can be used as an alternative method to circumvent the difficulty in partial derivation. However, the Monte Carlo simulation requires large computational resources for a correct simulation because it considers the completeness issue whether an ideal or a real operator conducts an experiment to evaluate the measurement uncertainty. Thus, the Monte Carlo simulation needs a large number of samples to ensure that the uncertainty evaluation is as close to the GUM as possible. Unscented transform can alleviate this problem because unscented transform can be regarded as a Monte Carlo simulation with an infinite number of samples. This idea means that unscented transform considers the uncertainty evaluation with respect to the ideal operator. Thus, unscented transform can evaluate the measurement uncertainty the same as the uncertainty that the GUM provides.     

Evaluating the application of data-driven intelligent methods to estimate discharge over triangular arced labyrinth weir

The use of artificial intelligence based (AI-based) methods has been known as a promising approach for solving engineering problems in order to model systems with high complexity and uncertainty such as the hydrodynamic behavior of flow passing the hydraulic structures. Considering the importance of weirs in regulating the water level and discharge controlling in water transfer channels and dams, it seems that the application of these methods can be considered as a useful tool for the estimation of discharge capacity. The present study examines the precision and use of six data-driven models including Bayesian neural network (BNN), multiple linear regression (MLR), multi-layer perceptron neural network (MLPNN), gene expression programming (GEP), least square support vector machine (LSSVM), and Chi-squared automatic interaction detector (CHAID) for the estimation of discharge passing triangular arced labyrinth weirs compared to two proposed experimental relations. To this end, 212 laboratory test results were used and statistical parameters of coefficient of determination (${\text{R}}^2$), root-mean-square error (RMSE), mean absolute error (MAE), and bias were employed as the criteria for the comparison of the models' performance. Results showed a good agreement between the observed and estimated values using the AI-based models. Among these models, the MLPNN managed to estimate the discharge passing the weir with the highest precision (RMSE = 0.00385 m³/s, R² = 0.999, and Bias<$\left|0.0001\right|$).     

Experimental study of discharge formulas for rectangular sharp-crested weirs under free flow condition

Laboratory experiments were carried out to investigate the discharge characteristics of rectangular sharp-crested weirs under free flow condition. The performances of available discharge formulas have been evaluated by using the experimental data sets of present and previous studies. Error statistics of our experimental data indicate that the recent stage-discharge relationships show satisfactory performances. Discharge formula in terms of weir Reynolds number proposed by Vatankhah gives the highest accuracy among the existing slit weir equations, with $E_{\pm 4}=100.00\text{\%}$ (i.e. percent error less than or equal to $\pm 4$) and a mean absolute error ${\left|E\right|}_m=0.88\text{\%}$. The full-range discharge equation presented by Bijankhan and Mahdavi Mazdeh shows the highest accuracy among the relationships in terms of weir contraction ratio, with $E_{\pm 4}=100.00\text{\%}$, ${\left|E\right|}_m=0.91\text{\%}$ for slit weirs and, $E_{\pm 4}=94.64\text{\%}$, ${\left|E\right|}_m=1.60\text{\%}$ for partially contracted weirs, respectively. The weir velocity formulae suggested by Gharahjeh et al. exhibit the relatively better performance, with $E_{\pm 4}=98.41\text{\%}$, ${\left|E\right|}_m=1.34\text{\%}$ for slit weirs and, $E_{\pm 4}=91.07\text{\%}$, ${\left|E\right|}_m=1.91\text{\%}$ for contracted weirs, respectively. Statistical results of this study confirm the weir velocity approach presented by Aydin et al. and show that, the weir velocity is a predominant quantity for rectangular sharp-crested weirs, unique characteristics of the weir velocity curves make it more suitable for expressing the discharges. Moreover, it is important to point out that the performance of weir velocity formulae can be further improved.     

Improved frictional modeling for the pressure-time method

The pressure-time method is classified as a primary method for measuring discharge in hydraulic machinery. The uncertainty in the discharge determined using the pressure-time method is typically around $\pm 1.5$ %; however, despite dating back almost one hundred years in time, there still exists potential to reduce this uncertainty. In this paper, an improvement of the pressure-time method is suggested by implementing a novel formulation to model the frictional losses arising in the evaluation procedure. By analyzing previously obtained data from CFD, laboratory and full-scale pressure-time measurements it is shown that the new friction model improves the accuracy of the flow rate calculation by approximately 0.1–0.2% points, compared to currently utilized friction models. Despite being a small absolute improvement, the new friction model presents an important development of the pressure-time method because the relative improvement is significant.     

Model analysis for differential pressure two-phase flow rate meter in intermittent flow

Multiphase flow rate metering is a challenging problem, specially for flow patterns other than wet-gas. This paper brings forward a new comparative analysis of three differential pressure calibration models suited for liquid dominated two-phase flows, in a total of seven model configurations. First, the models are compared theoretically and classified in terms of the type of input data required. Then, experimental data of over 300 horizontal air–water experiments, for $1$” and $2$” pipe diameters, supports quantitative analyses of the prediction accuracies and sensitivity of the superficial velocities of gas and liquid to measurement errors in the model input variables. Finally, a method for assessing the decoupled measurement errors for the void fraction and gas velocity is shown, as these variables are typically subject to higher uncertainties. It results that, though the void fraction is shown to be systematically under evaluated in more than 10%, the total mass flow rate is estimated through the Paz et al. (2010) model with an overall root mean squared deviation (RMSD) of 5.75% for the $2$” data. Also, the use of gas velocity measurements, even if subject to considerable errors, decreased the RMSD for the gas superficial velocity by more than half for the $1$” data.     

Velocity measurements in developing narrow open-channel flows with high free-stream turbulence: Acoustic Doppler Velocimetry (ADV) vs Laser Doppler Anemometry (LDA)

A developing narrow open-channel flow has been investigated using Acoustic Doppler Velocimetry (ADV) and Laser Doppler Anemometry (LDA). The objectives were to first characterize the flow environment with the LDA system alone, then quantify the intrusion effect of the ADV sensor immersion, and finally compare ADV-LDA measurements. The main features of the flow have been described. The turbulence levels measured in the outer flow region are high and almost isotropic due to the specificities of the flow (3D, narrow, developing). This contributes to a flattening of the mean streamwise velocity profile in this region. The intrusion effect of the ADV sensor is found to be dependent on Froude number ($Fr=U_0/\sqrt{gH}$ with $U_0$ the discharge velocity, $H$ the flow depth, and $g$ the gravity acceleration). Vertical flow below the sensor is amplified while the streamwise component of the flow is enhanced for “low” Fr ($\le 0.6$) and reduced for “high” Fr ($\ge 1.1$). On the other hand, turbulence quantities are not affected by the sensor presence. Compared to the LDA, the ADV is shown to underestimate the mean flow and turbulence intensities, while not affecting Reynolds shear stress measurements. The underestimation of the turbulence intensities can be attributed to the lower sampling rate and larger sampling volume of the ADV, but the underestimations of the mean velocities are more likely linked to a constant bias that the ADV seems to have or to some type of ADV-intrinsic noise. Some implications for practical application are discussed.     

Comparison of GUM and Monte Carlo methods for evaluating measurement uncertainty of perspiration measurement systems

Measurement uncertainty is an important parameter to express measurement results including means and reliability. The uncertainty analysis of the biomedical measurement system needs to be established. A perspiration measurement system composed of several sensors was developed. We aim to estimate the measurement uncertainty of this system with several uncertainty sources, including airflow rate, air density, and inlet and outlet absolute humidity. Measurement uncertainty was evaluated and compared by the Guide to the expression of the uncertainty in measurement (GUM) method and Monte Carlo simulation. The standard uncertainty for the perspiration measurement system was 6.81 × 10⁻⁶ kg/s and the uncertainty percentage <10%. The major source of the uncertainty was airflow rate, and inlet and outlet absolute humidity. The Monte Carlo simulation could be executed easily with available spreadsheet software programs of the Microsoft Excel. GUM and Monte Carlo simulation did not differ in measurement uncertainty with precision to two decimal places. However, the sensitivity coefficient derived by GUM provided useful information to improve measurement performance, which was not evaluated with the Monte Carlo simulation method.     

A pre-verifiable calibration model of wall shear stress thermal sensor driven by constant current

This paper presents a pre-verifiable calibration model, which can accurately characterize the measurement behavior of the flexible wall shear stress thermal sensor in constant current drive mode. In this calibration model, the fitting parameter $a$ can be pre-verified with $I{R}_a$, which provides a method to verify the accuracy of calibration instruments and data. In repetitive experiments, the average related normalized standard deviation ${\epsilon }_{\tau }$ is 1.26%, which indicates that our calibrations are reliable. And the relative deviations $\eta$ between $a$ and $I{R}_a$ are below 1.15% in all experiments, which indicates that the calibration model can pre-verify the reliability of calibration instruments and data among different sensors and different drive currents.     

Simultaneous velocity profile and temperature profile measurements in microfluidics

Simultaneous non-intrusive temperature and velocity measurements in flows are of high technological interest, e. g. to study the heat transfer in microfluidic environments. However, a measurement system that offers a low velocity uncertainty and micrometre spatial resolution as well as highly accurate temperature measurements in a single device has not been demonstrated so far. In this work, this problem is solved by combining a Laser Doppler Velocity Profile Sensor (LDV-PS) with Laser-Induced Fluorescence (LIF). Seeding particles are employed, that contain the fluorescent dyes uranine and rhodamine B. The multiple dye approach eliminates the influence of the droplet size. Relative velocity uncertainties of down to 0.4% and a temperature uncertainty of down to 0.24 ${}^{\circ }$C with a spatial resolution of $10\mathrm{\mu }\mathrm{m}$ are achieved in a demonstration air flow experiment. The method has the potential to be optimised for different temperature ranges and uncertainty requirements, making it applicable on a wide range of thermal flows like fuel cells or microbioreactors. A better understanding of heat exchange processes can improve the energy efficiency of microfluidic devices.