Two-phase flow velocity field measurement method based on adaptive cross-correlation of zoned electrical parameter distribution

Abstract

The application discloses a two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electric parameter distribution, which comprises the following steps: step 1, multi-electrode capacitance array data acquisition and dielectric constant distribution reconstruction; step 2, feature region identification and division of gas-liquid two-phase dielectric parameter distribution; step 3, time-space cross-correlation flow velocity estimation of dielectric parameter distribution in a local region; and step 4, area weighted fusion of multi-partition flow velocities and gas-liquid two-phase mixed flow velocity estimation. The application performs feature identification and partitioning processing on the dielectric parameter distribution, respectively calculates the flow velocities of each region through adaptive cross-correlation, and then performs weighted fusion, so that the measurement error caused by uneven flow patterns is eliminated, and the measurement precision and robustness of the gas-liquid two-phase mixed flow velocity are improved.

Description

Technical Field

[0001] This invention belongs to the field of multiphase flow parameter detection technology, specifically relating to a two-phase velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution, which is applicable to the accurate measurement of gas-liquid two-phase velocity in cryogenic propellant refueling processes and pipeline transportation of carbon dioxide. Background Technology

[0002] In key industrial sectors such as energy and chemical engineering, aerospace, and refrigeration engineering, gas-liquid two-phase flow is widespread, and the accurate measurement of its flow velocity parameters is directly related to the safety and economy of the production process. Especially in extreme conditions such as pipeline transportation of carbon dioxide during carbon capture, utilization, and storage, and cryogenic propellant refueling for space launches, the medium in the flow channel is often under high pressure, low temperature, or prone to phase change, with complex and variable flow patterns, and significant differences in gas and liquid phase density and viscosity. This causes a sharp increase in measurement error, or even failure, of traditional flowmeters based on the assumption of single-phase flow.

[0003] With its advantages of non-invasiveness, fast response speed, and no radiation, electrophoresis (ECT) technology has become the mainstream means of visually monitoring the interface of two-phase flow. However, the existing ECT flow velocity measurement schemes mainly have the following technical bottlenecks: (1) Traditional ECT flow velocity measurement mostly adopts the two-section cross-correlation method, that is, cross-correlation calculation is performed on the entire reconstructed dielectric constant image. Since the sensitivity field of the ECT sensor has the characteristics of "soft field" with weak edge and strong center, the calculation of the entire image will cause the motion information of high-speed sparse bubbles and low-speed continuous liquid phase to be superimposed, resulting in the broadening of the peak value of the cross-correlation function or the appearance of duplicate peaks, making it difficult to determine the true flow velocity. (2) In the process of carbon dioxide pipeline transportation or cryogenic propellant refueling, the flow pattern is often not ideally uniformly distributed. During cryogenic propellant refueling, a ring flow of liquid film on the wall and gas phase in the center is easily formed, while the density of carbon dioxide changes drastically near the critical point. Existing methods lack the ability to identify flow patterns and cannot perform differentiated velocity measurements for different regions (such as the gas phase core region and the liquid phase wall region), resulting in deviations in the calculation of the volumetric flow weighted average velocity. (3) For media involving phase change or high density difference, even small velocity measurement errors can directly lead to huge deviations in flow accumulation, affecting trade settlement and security monitoring. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a two-phase velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution. This method combines spatial distribution information of dielectric parameters of gas-liquid two-phase flow, identifies the flow pattern structure, and decouples the velocity in partitions to ultimately achieve high-precision measurement of gas-liquid two-phase mixed flow velocity under complex working conditions, thus meeting the application requirements of extreme scenarios such as pipeline transportation of carbon dioxide and cryogenic propellant refueling.

[0005] To achieve the above objectives, the present invention provides the following solution: A two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution includes: Step 1: Data acquisition and dielectric constant distribution reconstruction of multi-electrode capacitor array; Step 2: Identify and divide characteristic regions based on the distribution of dielectric parameters in the gas-liquid two-phase system; Step 3: Based on the identified features, estimate the spatiotemporal cross-correlation velocity of the dielectric parameter distribution in the local region; Step 4: Based on the spatiotemporal cross-correlation velocity, perform area-weighted fusion of multi-zone velocities and estimate the gas-liquid two-phase mixing velocity.

[0006] Preferably, in step 1, a time-division scanning method is used to sequentially excite one electrode and measure the voltage of the remaining electrodes. The original capacitance measurement data reflecting the distribution of dielectric parameters of the gas-liquid two-phase flow in the tube is continuously acquired in real time through a capacitance tomography data acquisition device. By establishing an electrode sensitivity distribution model, the dielectric parameter distribution of the capacitance measurement data is reconstructed using the Tikhonov regularization method.

[0007] As a preferred option, in step 2, different adaptive region division strategies are implemented based on the flow pattern recognition results: (1) Laminar flow: Based on the bimodal characteristics of the dielectric parameter distribution histogram, the gas-liquid two-phase interface is extracted by adaptive threshold segmentation, and the dielectric parameter distribution of the gas-liquid two-phase flow is divided into two independent regions, corresponding to the gas-dominated region and the liquid-dominated region, respectively; (2) Bubble flow: Based on the uniformly dispersed phase distribution characteristics, the cross section is uniformly divided into (3) Circular flow: Based on the gradient change of dielectric constant, the liquid film boundary is detected and the cross section is divided into two parts: the gas phase inner ring and the liquid film outer ring.

[0008] Preferably, in step 3, the temporal motion information of each independent characteristic subdomain of the gas-liquid two-phase flow dielectric parameter distribution is extracted. Specifically, multiple frames of dielectric constant distribution data are continuously collected in the time series. Based on each characteristic subdomain identified in step 2, adaptive cross-correlation velocity measurement raw data are constructed respectively. The adaptive cross-correlation calculation method is used to estimate the velocity of each characteristic subdomain.

[0009] Preferably, in step 4, the discrete zone flow velocities are mapped to a mixed flow velocity characterizing the overall transport capacity of the pipeline; wherein, the area proportion of each zone, which was calibrated in step 2, is retrieved. This is used as the fusion weight coefficient; based on the fact that the contribution of different phases in gas-liquid two-phase flow to volumetric flow rate is proportional to their cross-sectional area, an area-weighted fusion algorithm is adopted to calculate the partitioned flow velocities in step 3. Weighted calculations are performed to ultimately estimate the mixing velocity of the gas-liquid two-phase flow.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. To address the velocity ambiguity and flow pattern sensitivity issues caused by the overall cross-correlation of traditional dielectric parameter distribution, an adaptive cross-correlation velocity measurement scheme based on partitioned dielectric parameter distribution is proposed. This scheme effectively suppresses the interference of soft field effects and complex flow patterns on velocity inversion, and improves the measurement accuracy and robustness of gas-liquid two-phase mixed flow velocity under non-uniform flow patterns.

[0011] 2. To address the unsteady and highly dynamic fluctuation characteristics of gas-liquid two-phase flow intensity in cryogenic propellant refueling and pipeline transportation of carbon dioxide, an adaptive cross-correlation velocimetry algorithm is proposed. By dynamically adjusting the sampling window, it achieves accurate interception of upstream and downstream signals with high overlap and adaptive tracking of transit time, effectively suppressing the calculation error caused by drastic changes in flow velocity, and significantly improving the dynamic response capability and accuracy of cross-correlation velocimetry. Attached Figure Description

[0012] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a two-phase velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution, according to an embodiment of the present invention. Figure 2 Flowchart for adaptive cross-correlation flow velocity calculation based on dielectric parameter distribution. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] Example 1 like Figure 1 , 2 As shown, this invention provides a two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution, comprising: Step 1: Data Acquisition and Dielectric Constant Distribution Reconstruction of Multi-Electrode Capacitor Array Two sets of metal electrodes are evenly arranged circumferentially on the outer wall of the pipe to be tested to form a sensor array, with the distance between the two sets of electrode arrays being [missing information]. An AC excitation voltage signal is applied. The system employs a time-division scanning method, sequentially exciting one electrode while measuring the voltage of the remaining electrodes. Through a capacitance tomography data acquisition device, raw capacitance measurement data reflecting the dielectric parameter distribution of the gas-liquid two-phase flow within the tube is continuously acquired in real time. Given the soft-field characteristics of capacitance tomography, an electrode sensitivity distribution model is established, and the dielectric parameter distribution is reconstructed from the capacitance measurement data using the Tikhonov regularization method.

[0017] MERGEFORMAT (1) in, Represents boundary measurement values. This indicates the distribution of electrical parameters within the field. This represents the mapping from the medium within the field to the boundary response, i.e., the sensitivity matrix. It is the identity matrix. This is the regularization parameter. This step not only completes the conversion from weak capacitance signals to digital quantities, but also transforms the invisible manifold structure into a visualized grayscale distribution map of dielectric constant through image reconstruction algorithms, providing a high-fidelity spatial distribution data foundation for subsequent physical feature-based partitioning and flow velocity calculation.

[0018] Step 2: Identification and division of characteristic regions of dielectric parameter distribution in gas-liquid two-phase systems First, based on the dielectric parameter distribution of the gas-liquid two-phase flow obtained above, the flow pattern is identified by image recognition method, mainly divided into laminar flow, annular flow and bubbly flow. For other flow patterns, the dielectric parameter distribution of their two-dimensional cross sections can be attributed to the spatial distribution of the above three flow patterns.

[0019] Secondly, the reconstructed grayscale image of the dielectric constant of the gas-liquid two-phase flow was preprocessed, and the median filtering algorithm was used to suppress the "soft field" effect and artifacts caused by measurement noise. Based on the above flow pattern recognition results, different adaptive region division strategies were implemented: (1) Laminar flow: Based on the bimodal characteristics of the dielectric parameter distribution histogram (reflecting the significant dielectric difference between the gas phase and the liquid phase), the gas-liquid two-phase interface was extracted by adaptive threshold segmentation, and the dielectric parameter distribution of the gas-liquid two-phase flow was divided into two independent regions, corresponding to the gas phase-dominated region and the liquid phase-dominated region, respectively; (2) Bubble flow: In view of its diffuse and uniform phase distribution characteristics, the cross section was uniformly divided into (3) Circular flow: Based on the change of dielectric constant gradient, the liquid film boundary is detected and the cross section is divided into two parts: the gas phase inner ring and the liquid film outer ring.

[0020] Finally, the area ratio and cumulative dielectric constant of each characteristic subdomain of the gas-liquid two-phase flow dielectric parameter distribution are calculated. This step realizes an intelligent mapping from the continuous physical field of the gas-liquid two-phase flow dielectric parameter distribution to discrete characteristic regions, providing an accurate data foundation for subsequent partitioned cross-correlation calculations.

[0021] Step 3: Adaptive cross-correlation velocity estimation based on dielectric parameter distribution within a local region This step focuses on extracting temporal motion information within each independent characteristic subdomain of the dielectric parameter distribution in a gas-liquid two-phase flow. Multiple frames of dielectric constant distribution data are continuously acquired over a time series. Based on each characteristic subdomain (e.g., gas phase or liquid phase) identified in step 2, adaptive cross-correlation velocity measurement raw data are constructed. The following adaptive cross-correlation calculation method is used to estimate the velocity in each characteristic subdomain. First, the sampling times of the upstream and downstream cross-sectional signals are... The sampling length is Cross-correlation function of two columns of data for, MERGEFORMAT (2) Then it is normalized. MERGEFORMAT (3) in, and The value of the autocorrelation function at zero, transit time It can be calculated using the following formula.

[0022] MERGEFORMAT (4) The initial transit time is obtained using the algorithm described above. This serves as a rough estimate of the actual transit time. A more accurate value is searched within its neighborhood using the following steps. The sampling time of the downstream signal in the next calculation will be... start, MERGEFORMAT (5) Through the next round of cross-correlation calculation, if In the search range If memory is at its peak, the remaining transit time can be obtained. Then the new crossing time It can be represented as MERGEFORMAT (6) Conversely, the new transit time .

[0023] If the operation continues, then use As the new initial transit time value, i.e. MERGEFORMAT (7) If the calculation stops, the flow velocity within the characteristic subdomain of the dielectric parameter distribution of the gas-liquid two-phase flow is: MERGEFORMAT (8) The above process is performed on all characteristic subdomains in the dielectric parameter distribution of the gas-liquid two-phase flow to realize the flow velocity calculation of the entire flow field of the dielectric parameter distribution of the gas-liquid two-phase flow.

[0024] Step 4: Area-weighted fusion of multi-zone velocities and estimation of gas-liquid two-phase mixing velocity After calculating the local velocity of each independent characteristic subdomain, the discrete partitioned velocities are mapped to a mixed velocity characterizing the overall transport capacity of the pipeline. First, the system retrieves the area percentage of each partition as defined in step 2. This is used as the fusion weighting coefficient. Given that the contribution of different phase states to volumetric flow rate in gas-liquid two-phase flow is proportional to their cross-sectional area, an area-weighted fusion algorithm is adopted to calculate the partitioned flow velocities in step 3. Weighted calculations are performed to ultimately estimate the mixing velocity of the gas-liquid two-phase flow. MERGEFORMAT (8) in, This indicates the mixing velocity of the gas-liquid two-phase flow. This indicates the number of partitions in the distribution of dielectric parameters across the cross section.

[0025] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for measuring two-phase flow velocity fields based on adaptive cross-correlation of partitioned electrical parameter distribution, characterized in that, include: Step 1: Data acquisition and dielectric constant distribution reconstruction of multi-electrode capacitor array; Step 2: Identify and divide characteristic regions based on the distribution of dielectric parameters in the gas-liquid two-phase system; Step 3: Based on the identified features, estimate the spatiotemporal cross-correlation velocity of the dielectric parameter distribution in the local region; Step 4: Based on the spatiotemporal cross-correlation velocity, perform area-weighted fusion of multi-zone velocities and estimate the gas-liquid two-phase mixing velocity.

2. The two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution as described in claim 1, characterized in that, In step 1, a time-division scanning method is used to sequentially excite one electrode and measure the voltage of the remaining electrodes. The original capacitance measurement data reflecting the distribution of dielectric parameters of the gas-liquid two-phase flow in the tube is continuously acquired in real time through a capacitance tomography data acquisition device. By establishing an electrode sensitivity distribution model, the dielectric parameter distribution of the capacitance measurement data is reconstructed using the Tikhonov regularization method.

3. The two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution as described in claim 2, characterized in that, In step 2, different adaptive region segmentation strategies are executed based on the manifold recognition results: Laminar flow: Based on the bimodal characteristics of the dielectric parameter distribution histogram, the gas-liquid two-phase interface is extracted by adaptive threshold segmentation. The dielectric parameter distribution of the gas-liquid two-phase flow is divided into two independent regions, corresponding to the gas-dominant region and the liquid-dominant region, respectively. Bubble flow: Based on the characteristics of a uniformly dispersed phase distribution, the cross section is uniformly divided into... A regular grid area; Circular flow: Based on the detection of liquid film boundary by the gradient change of dielectric constant, the cross section is divided into two parts: the gas phase inner ring and the liquid film outer ring.

4. The two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution as described in claim 3, characterized in that, In step 3, time-series motion information is extracted based on the distribution of dielectric parameters of gas-liquid two-phase flow within each independent characteristic subdomain. This involves continuously collecting multiple frames of dielectric constant distribution data over a time series, constructing adaptive cross-correlation velocity measurement raw data for each characteristic subdomain identified in step 2, and using an adaptive cross-correlation calculation method to estimate the velocity of each characteristic subdomain.

5. The two-phase flow velocity field measurement method based on adaptive cross-correlation of partitioned electrical parameter distribution as described in claim 4, characterized in that, In step 4, the discrete zone flow velocities are mapped to a mixed flow velocity characterizing the overall transport capacity of the pipeline; wherein, the area proportion of each zone, which was calibrated in step 2, is retrieved. This is used as the fusion weight coefficient; based on the fact that the contribution of different phases in gas-liquid two-phase flow to volumetric flow rate is proportional to their cross-sectional area, an area-weighted fusion algorithm is adopted to calculate the partitioned flow velocities in step 3. Weighted calculations are performed to ultimately estimate the mixing velocity of the gas-liquid two-phase flow.

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