Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R² value of 0.97, RMSE of 0.74 m³/h, MAE of 0.52 m³/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a broad measurement range. Nevertheless, due to the low measurement accuracy under micro gas flow caused by nonlinear errors and a relatively complex structure, traditional laminar flow measurement devices exhibit limitations in micro gas flow measurement scenarios. This study proposes a novel micro gas flow measurement method based on a single capillary laminar flow element, which simplifies the structure and enhances applicability in the field of micro gas flow. Through structural optimization with precise control of the capillary length–diameter ratios and theoretical error correction based on computational analysis, nonlinear errors were effectively reduced while improving the measurement accuracy in the field of micro gas flow. The proposed methodology was systematically validated through computational fluid dynamics simulations (ANSYS Fluent 2021 R1) and experimental investigations using a dedicated test platform. The experimental results show that the relative error of the measurement system within the full measurement range is less than ±0.6% (1–10 cm³/min; cm³/min means cubic centimeter per minute), and its accuracy is superior to 1% of reading (1% Rd) or 1.5% of reading (1.5% Rd) of conventional laminar flowmeters. The fitting curve of the flow rate versus the pressure difference derived from the measurement results maintains an excellent linear correlation (R² > 0.99), thus confirming that this method has practical application value in the field of micro gas flow measurement.

This paper proposes a method for metering natural gas energy based on the correlation between density and sound velocity. The technique integrates physical property correlation models with measured parameters, such as temperature, pressure, sound velocity, and density, to accurately predict the compression factor and the ideal volume calorific value of natural gas under operating conditions. The volume flow is corrected using the compression factor, which enables precise metering of natural gas energy through the adjusted volume flow and calorific value. To develop a high-precision physical property correlation model, a natural gas dataset comprising 10,000 sample sets is first constructed for model training and testing. Multiple machine learning algorithms are then employed to build predictive models. Analysis of the experimental results led to the development of a model-switching strategy based on the ranges of input features, which substantially enhanced prediction accuracy. For the compression factor model, the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were 0.00118, 0.0030, 0.14%, and 0.9987, respectively. The corresponding indicators for the calorific value model were 0.1583, 0.331, 0.44%, and 0.9736. The proposed method is finally validated using a natural gas real-flow test bench. The results demonstrated maximum prediction errors of 0.061% and 1.19% for the compression factor and calorific value, respectively, while the maximum relative energy error across four gas samples was 1.21%. These results indicate that the method can effectively achieve accurate natural gas energy metering in practical operating conditions.

The laminar flow meter (LFM) boasts several advantages such as no moving parts, a wide range ratio, high measurement accuracy, quick dynamic response, etc., and is a promising technology for micro gas flow measurement. In order to explore the influence of different curvature radii on curved surface gap LFM, three curved structures with different curvature radii were designed. The computational fluid dynamics method is applied to simulate the flow feature of three structures. The simulated velocity cloud and pressure distribution show that the larger the curvature radius, the more stable the flow of gas medium. The relationship between differential pressure and volume flow was obtained through the test within a flow range of 0~540 sccm. Regression analysis revealed that the volume flow measured by the curved surface LFM had a high linear relationship with the differential pressure. Experimental findings indicate that differential pressure of the structure with a curvature radius of 2 mm was greater than that of other two structures (curvature radius of 6 mm and 3 mm) at the same point. This indicates that adding the number of surfaces can effectively increase the pressure loss, so as to obtain a larger range ratio, but will increase the measurement error.

A fluidic flowmeter with new structure is designed in this work on the basis of the traditional ones in order to obtain wide measurement range. This fluidic flowmeter has the diffuser wall with polygonal line and it has the performance of adaptability and stability. The CFD software FLUENT is employed to the numerical simulations of the fluidic flowmeter. The linear-relationship of oscillation frequency flowrate is obtained in the wide measurement range through the analysis of the data. The simulation results show that the oscillation of this fluidic flowmeter is stable and reliable. The formula of the flowrate and the oscillation frequency is obtained. The lower limit of the measurement and the pressure loss of the oscillator are also analyzed. Some useful theoretical information can be provided for the design of fluidic flowmeter in this work.

A new method for the flowrate measurement of the gas-liquid two-phase flow is proposed. A multi-circle flowmeter is designed based on the measurement principle of the conventional loop flowmeter. Four differential pressure signals are obtained from the multi-circle sensor at different positions. Multi-parameters of the two-phase flow are obtained. The total flowrate measurement models of the two-phase flow are established using the average values of the differential pressure signals. Four measurement models are compared and the optimal ones are selected to calculate the density and the volume fraction of the mixture. The measurement of the liquid flowrate and the gas flowrate is realized associated with the volume fraction. Experimental results show that the measurement accuracy is satisfactory