Flow Measurement and Instrumentation最新文献

Biogas production measurement is a fundamental aspect of anaerobic digestion research. Lab-scale experiments usually require devices capable of measuring low flowrates and low relative pressures. Despite the existence of multiple commercially available alternatives for such demands, each with particular advantages and limitations, researchers have often resorted to in-house developed systems. This inclination is motivated by cost savings and the potential for of customization to suit specific research needs. Those systems, however, are seldom reproduced beyond laboratory walls due to overly complex designs, limited component availability or unpublished software. In this work, we developed a fully open-source lab-scale biogas flow meter equipped with datalogging capabilities. Our approach relied on inexpensive, widely available electronic modules. The flow meter was calibrated, tested and validated in batch biogas production experiments, alongside a comparative assessment with a proprietary device widely used in the field. The semi-continuous gas meter presented a resolution of 7.45 ± 0.13 mL and maintained a stable pulse volume (under 2 % relative standard deviation) for flowrates ranging from 60 mL h⁻¹ to 1120 mL h⁻¹. The device was not sensitive to the evaporative loss of up to 7 mL of packing liquid, demonstrating its applicability to long-term experiments. The developed system was shown to be robust and reliable and can be easily reproduced, revised and enhanced in laboratories everywhere.

The importance of flow monitoring in the oil industry has expanded due to the global need for fossil fuels. This has led to the emergence of a new subset of the flowmeter market. The goal of this study is to use a Radial Basis Function (RBF) neural network developed through Simulated Annealing (SA) to pick features of the signals generated by gamma-based flowmeters in order to determined volumetric fractions. The volumetric detection system presented in this article consists of a¹³⁷Cs isotope as gamma emitter, two NaI detectors for collecting the photons, and a glass pipe in between them. Monte Carlo N-Particle (MCNP) was used to model the above-mentioned geometry. Fifteen wavelet, frequency, and time characteristics were extracted from the raw data captured by both detectors. First, the SA optimization algorithm was used to identify the suitable attributes. Five useful features were presented as a consequence of this procedure, and they were fed into the RBF network in order to estimate volumetric percentages. This study is innovative in that it combines the RBF neural network with the SA algorithm to pick effective features. The outcome is as follows: (1) Outlining five appropriate characteristics for use in determining percentages of volume. (2) Predicting the volume fraction of materials in two-phase flow with a root mean square error of less than 0.22. (3) By recognizing suitable inputs using the method based on the SA algorithm, the artificial neural network can determine the target output with less computational load.

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

In order to fulfill the present need for downhole lifting pumps in various cutting-edge drilling techniques, a double jet pump is ingeniously devised. The internal three-dimensional characteristics of this pump are utilized to optimize its structural parameters through the flow field simulation method. Extensive evaluations are conducted to assess the alterations in jet performance under different parameters, including area ratios, throat lengths, nozzle exit positions, and nozzle area ratios. The pivotal structural parameters of the double jet pump are optimized as follows: area ratio $m_a=0.22$, throat length $L_h=84.5$ mm, nozzle exit position $L_c=6.76$ mm, and nozzle area ratio $m_b=0.3$. Furthermore, an experimental platform is meticulously erected to validate the principles of the double jet pump and to analyze its jet performance. This ascertains the feasibility of the double jet pump and validates the accuracy of the simulation and analysis results. This device can be effectively employed in the oil and gas production characterized by a high sand content extraction.

In this study, we propose a numerical model aimed at improving the understanding and prediction of flow velocities in natural channels. This model specifically focuses on the challenges presented by anisotropic turbulence and bottom roughness. By incorporating the mixing length concept into an algebraic turbulence model and employing the finite element method with Streamline-Upwind/Petrov–Galerkin (SUPG) stabilization, our model seeks to refine the simulation of axial velocity distribution and secondary motions. Validation was achieved through Acoustic Doppler Current Profiler (ADCP) measurements in three sections of the Canal do Rodeador, showing our model’s predictions of discharge and average velocity to have a deviation of approximately 9.34% from experimental data. The results underline the significance of secondary currents and turbulence anisotropy in shaping channel flow behaviors, offering new insights into the interactions between flow characteristics and channel bed features. This model stands out as a robust tool for hydraulic structure design and hydrokinetic potential evaluation, providing a non-intrusive, cost-effective alternative to traditional methods.

This study introduces a novel methodology for determining the optimal frequency for pressure data acquisition in water distribution systems (WDSs) in response to transient-flow events such as pipe ruptures. By leveraging actual design and operational data, we simulated potential rupture scenarios and their resultant pressure wave patterns through transient-flow analysis. Harmonic series modeling was used to identify significant periods that closely replicate the observed pressure-wave time series. This allowed the derivation of recommended data acquisition intervals tailored to each monitoring site within the network. The proposed approach facilitates comprehensive analysis of all possible hydraulic shock scenarios, which can provide insights into both the existing monitoring infrastructure and strategic planning in the design phase of the WDS. Although the methodology focuses on rupture events, its application can extend to various water-quality indicators and flow rates, albeit with consideration of their typically static behavior.

In this proposed work, water leakage detection in Polyvinyl chloride (PVC) pipes has been investigated using a ring filter connected to log-periodic feedlines (LPF). The proposed filter has a well-structured ring design to adjust the PVC pipes and operates between 0.3 GHz & 0.7 GHz. Using a signal generator & analyzer, attenuation measurements are obtained to analyze the impact of water leakage. PVC pipes with varying diameters (3 inches, 2 inches, 1.5 inches, & 1 inch) are connected with water stoppers to control the water flow during experiments. The leak is contained within a plastic tub to make measurements easier. Results show a good connection between attenuation & water level for all pipe diameters, supporting the LPF-based ring filter’s effectiveness as a leakage detector. An analogous circuit model is created to evaluate the filter’s performance further. At the same time, the resonant parameters, such as Q-factors & resonant frequencies, are retrieved from $S_{21}$ magnitude measurements in the operating frequency range. The proposed methodology is shown to be accurate & reliable, offering significant potential for water leakage detection in PVC pipe systems. By accurately detecting water leakage, this technology enables prompt repairs, reducing water loss & associated costs, enhancing water infrastructure, and promoting sustainable water management.

Leak detection in water pipelines is still an important issue in real-world industrial systems. Thus, a new nondestructive thermal sensor design based on the thermal interrogation method has been developed to detect and estimate the real-time leaks in water plastic pipes without environmental interference. The proposed thermal interrogation method utilizes a combination of heat flux and temperature measurements on the upstream and downstream outside plastic pipe surfaces. These measurements are obtained by using heat flux sensors and thermocouples that are covered by thin-film polyimide electric heaters. The interrogation method approach delivers that at constant input heat flux, the difference in sensor temperature values is dependent on the leakage volume flow rates. The sensor was tested over a range of water leakage volume flow rate from 0.1 m³/h to 1.3 m³/h and a range of water temperature from 21 °C to 25 °C. The water leakage volume flow rates were correlated to the difference in temperature values between the upstream and downstream sensors. The measurement results reveal that this sensor can be used to detect and estimate the real-time water leakage volume flow rate with high accuracy and repeatability for plastic pipes.

When measuring the coolant flow in a nuclear power plant using the elbow flowmeter, the complex fluid-heat coupling environment at the measurement location and other factors will affect the accuracy of the flow measurement, and the uncertainty of the influencing factors on the flow measurement needs to be considered to improve the measurement accuracy. To address this problem, this paper adopts the finite element simulation method to simulate and analyze the flow-heat field of the bend section of the primary circuit of a nuclear power plant and optimizes the Optimal Cross-section selection of the pipeline for flow measurement. Based on the pressure values measured using the traditional method, temperature information is added, and a BP neural network bend pipe flow soft measurement model based on the whale optimization algorithm is established to quantify the effects of temperature and pressure on flow measurement. The experimental results show that compared with the traditional engineering empirical method, the average absolute percentage error measured by the soft measurement method is reduced from 2.57 % to 0.21 %, which realizes the accurate measurement of the coolant flow rate of the elbow pipe.

Through the experimental verification, the accuracy of the cavitation jet feature captured by the coupling numerical calculation with different mesh types and different turbulence models is evaluated. Firstly, we built a high-speed jet experimental platform, and a high-speed camera and a paperless recorder are used to record the pressure, flow rate, and jet cloud oscillation of the jet pump in different states. The oscillation period and length of the jet cloud under different cavitation numbers are analyzed by the gray value method. The numerical results are compared with the experimental results. It is found that after the numerical calculation converges, the states of the jet cloud calculated by K–O and LES coupled with different meshes are periodically oscillating, while K-E does not. However, the maximum jet cloud lengths calculated by K-E and K–O turbulence models under different mesh types are nearly the same. Overall, the oscillation period calculated by the LES model is more in line with the experimental results, but its amplitude and maximum length are more different from the experimental results. In terms of capturing the jet cloud, critical pressure ratio, turbulent state and vapor volume fraction, the accuracy order of numerical calculation results are P–H (polyhedron-hexahedron mesh) ≈ P (polyhedron mesh) > H-T (hexahedral-tetrahedral mesh) > T (tetrahedral mesh), P–H ≈ P > T > H-T, P–H ≈ P > H-T > T and P–H > P > T > H-T, respectively. This research can provide more appropriate solutions for different concerns, as well as guidance for improving the accuracy and efficiency of simulations, and provides a basis for optimizing and developing specialized mesh types and turbulence models.