Non-contact method for measuring three-dimensional flow velocity field in pipeline based on acoustic Doppler

Abstract

The invention discloses a non-contact method for measuring a three-dimensional flow velocity field in a pipeline based on acoustic Doppler, and the method comprises the steps: transmitting a focused sound beam into the pipeline, receiving an echo signal reflected by a scatterer in fluid, and generating a Doppler echo signal set covering different angles and depth positions; performing pulse compression and time-frequency analysis on the Doppler echo signal set, and calculating the sight flow velocity component of each point along the sound beam direction; calculating a three-dimensional flow velocity vector of each sampling point based on the coordinate position of each space sampling point and the corresponding sight line flow velocity component, and realizing decoupling of the flow velocity direction and size; according to the spatial distribution of the three-dimensional flow velocity vector on the section of the pipeline, a three-dimensional flow velocity field model in the pipeline is generated through interpolation fitting, and section flow and flow state characteristic parameters are calculated according to the model. According to the embodiment of the invention, the limitation that only two-dimensional or one-dimensional flow velocity can be measured in a traditional method can be broken through, and the spatial resolution and measurement precision of flow velocity field reconstruction in a complex flow state are improved.

Description

Technical Field

[0001] This invention belongs to the field of flow velocity measurement technology, specifically a non-contact method and system for measuring three-dimensional flow velocity field inside a pipeline based on acoustic Doppler. Background Technology

[0002] In the field of industrial pipeline fluid metering and process control, accurately obtaining the velocity distribution within the pipeline is crucial for achieving high-precision flow measurement and flow regime analysis. Currently, contact measurement methods such as hot-wire anemometers and laser Doppler velocimeters, while capable of point velocity measurement, suffer from problems such as sensor disturbance to the flow field, the need for perforated installation, and unsuitability for high-pressure or corrosive media. Ultrasonic time-of-flight measurement, as a mainstream non-contact method, can measure cross-sectional average velocity, but its reliance on the principle of acoustic channel integration makes it unable to analyze the radial and transverse components of the velocity, resulting in significant model errors in underdeveloped flows, swirling flows, or complex turbulent conditions. While array-type ultrasonic Doppler methods developed in recent years can acquire multi-point velocity information, current technologies are mostly limited to two-dimensional cross-sectional measurements, making it difficult to decouple three-dimensional velocity vectors. Furthermore, limitations in beam coverage angle and sparse sampling result in low spatial resolution of the reconstructed velocity field, failing to meet the demands of refined flow field analysis. Summary of the Invention

[0003] The purpose of this invention is to provide a non-contact method and system for measuring three-dimensional flow velocity field in a pipe based on acoustic Doppler, so as to overcome the shortcomings of the prior art, break through the limitation of traditional methods that can only measure two-dimensional or one-dimensional flow velocity, and improve the spatial resolution and measurement accuracy of flow velocity field reconstruction under complex flow conditions.

[0004] One embodiment of this application provides a non-contact method for measuring three-dimensional flow velocity fields inside a pipe based on acoustic Doppler, the method comprising: By deploying a multi-element ultrasonic transducer array on the outer wall of the pipe, a focused acoustic beam is emitted into the pipe and the echo signal reflected by the scattering body in the fluid is received, generating a set of Doppler echo signals covering different angles and depths. The Doppler echo signal set is subjected to pulse compression and time-frequency analysis to extract the Doppler frequency shift of each spatial sampling point. Combined with the transmission frequency and the sound velocity in the fluid, the line-of-sight velocity component along the sound beam direction at each point is calculated. Based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, a vector tomography reconstruction algorithm based on compressed sensing is used to calculate the three-dimensional velocity vector of each sampling point, thereby achieving decoupling of velocity direction and magnitude. Based on the spatial distribution of the three-dimensional velocity vector on the pipe cross section, a three-dimensional velocity field model inside the pipe is generated by interpolation fitting, and the cross-sectional flow rate and flow characteristic parameters are calculated based on the model.

[0005] Another embodiment of this application provides a non-contact three-dimensional flow velocity field measurement system in a pipe based on acoustic Doppler, the system comprising: The receiving module is used to transmit a focused acoustic beam into the pipe through a multi-element ultrasonic transducer array deployed on the outer wall of the pipe and receive the echo signal reflected by the scattering body in the fluid, generating a set of Doppler echo signals covering different angles and depths. The extraction module is used to perform pulse compression and time-frequency analysis on the Doppler echo signal set, extract the Doppler frequency shift of each spatial sampling point, and calculate the line-of-sight velocity component along the sound beam direction at each point by combining the transmission frequency and the sound velocity in the fluid. The calculation module is used to calculate the three-dimensional velocity vector of each sampling point based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, using a vector tomography reconstruction algorithm based on compressed sensing, thereby achieving decoupling of velocity direction and magnitude. The generation module is used to generate a three-dimensional velocity field model inside the pipe by interpolation fitting based on the spatial distribution of the three-dimensional velocity vector on the pipe cross section, and to calculate the cross section flow rate and flow characteristic parameters based on the model.

[0006] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0007] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0008] Compared with existing technologies, the present invention provides a non-contact method for measuring three-dimensional flow velocity field in a pipe based on acoustic Doppler, which can overcome the limitation of traditional methods that can only measure two-dimensional or one-dimensional flow velocity, and improve the spatial resolution and measurement accuracy of flow velocity field reconstruction under complex flow conditions. Attached Figure Description

[0009] Figure 1 A hardware structure block diagram of a computer terminal for a non-contact method of measuring three-dimensional flow velocity field in a pipe based on acoustic Doppler, provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a non-contact method for measuring three-dimensional flow velocity field inside a pipe based on acoustic Doppler, provided for an embodiment of the present invention; Figure 3 This is a schematic diagram of a non-contact three-dimensional flow field measurement system in a pipeline based on acoustic Doppler, provided as an embodiment of the present invention. Detailed Implementation

[0010] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0011] The present invention first provides a non-contact method for measuring three-dimensional flow velocity field in a pipe based on acoustic Doppler. This method can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0012] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware structure block diagram of a computer terminal for a non-contact method of measuring three-dimensional flow velocity field in a pipe based on acoustic Doppler, provided as an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0013] See Figure 2 The present invention provides a non-contact method for measuring three-dimensional flow velocity field inside a pipe based on acoustic Doppler, which may include the following steps: S201, through a multi-element ultrasonic transducer array deployed on the outer wall of the pipe, emits a focused acoustic beam into the pipe and receives the echo signal reflected by the scattering body in the fluid, generating a set of Doppler echo signals covering different angles and depths. Specifically, based on the pipe's outer diameter and measurement requirements, a multi-element ultrasonic transducer array can be evenly arranged on the outer circumference of the pipe at predetermined angular intervals, and the spatial coordinates and pointing angles of each element can be recorded to generate a transducer array geometric parameter table. The core of this step is to complete the standardized layout and geometric parameter calibration of the ultrasonic transducer array, providing a precise spatial reference for subsequent beam focusing, scanning, and echo analysis. The specific implementation method is as follows: First, based on the actual outer diameter of the pipe under test and the spatial coverage requirements of the three-dimensional flow velocity field measurement, the number and arrangement of elements in the multi-element ultrasonic transducer array are determined. Pipes typically have a cylindrical structure; therefore, the transducers are evenly arranged along the circumference of the pipe's outer wall to achieve full circumferential sound wave coverage without blind spots. In this example, the outer diameter of the pipe under test is 200 mm, and an 8-element ultrasonic transducer array is selected. The angular interval between adjacent elements is set to 45 degrees. Starting from the 0-degree position of the pipe cross-section, the elements are arranged sequentially along the circumference, ensuring that all elements are symmetrically distributed, unobstructed, and without gaps, guaranteeing that the sound beam can uniformly penetrate the pipe wall and enter the pipe interior.

[0014] After deployment, a cylindrical coordinate system is established with the center of the pipe cross-section as the origin. This system is used to accurately calibrate the spatial position of each array element. The spatial coordinates include three parameters: radial distance, circumferential angle, and axial offset. The radial distance is the straight-line length from the center of the array element to the center of the pipe, which is equal to the outer radius of the pipe. The circumferential angle is the circumferential angle of the array element relative to the 0-degree reference. The axial offset is the relative position of the array element along the length of the pipe. The coordinate measurement accuracy is controlled within 0.1 mm. Simultaneously, the pointing angle of each array element is recorded. The pointing angle includes the angle between the central axis of the sound beam and the radial direction of the pipe, and the angle with the axial direction of the pipe. This is used to characterize the initial pointing direction of the sound waves emitted by the array element. The angle measurement accuracy is 0.1 degrees.

[0015] The numbers, spatial coordinates, and pointing angles of all array elements are arranged in order to form a structured transducer array geometric parameter table. Each row in the parameter table corresponds to the complete geometric information of an array element, which serves as a fixed reference for subsequent beamforming, coordinate transformation, and flow velocity calculation, ensuring the consistency and traceability of spatial position during the measurement process.

[0016] By using a phased array beamforming controller, excitation pulses with specific phase delays are applied to each array element, so that the synthesized beam is focused at a specified depth inside the pipe and beam deflection scanning is achieved, generating a focused acoustic beam emission sequence. The core of this step is to use phased array technology to achieve precise focusing and full-domain deflection scanning of the acoustic beam, and to construct an acoustic detection network covering the entire interior space of the pipeline. The specific implementation method is as follows: The phased array beamforming controller is the core unit for controlling the shape of the acoustic beam. Its working principle involves applying differentiated phase delays to different elements in the array, causing the acoustic waves emitted by each element to coherently superimpose during propagation, forming a focal point at a specified spatial location within the pipe, thus improving the echo signal strength and distance resolution in the local area. The excitation pulse uses a single-frequency sinusoidal pulse train, with the center frequency set to 1 MHz. The pulse repetition period is determined based on the pipe diameter and the propagation speed of the acoustic wave in the fluid, ensuring that the time for the acoustic wave to travel to and from the pipe cross-section is less than the repetition period, avoiding signal aliasing.

[0017] The phase delay value is calculated based on the array element geometric coordinates, the target focusing depth, and the sound velocity in the fluid. The focusing depth can be set according to measurement requirements. In this example, the focus is on the center of the pipe, i.e., a depth of 50 mm from the pipe wall. By calculating the sound path difference from each array element to the focusing point, the corresponding phase delay is obtained, denoted as Δτ_1 to Δτ_8, which correspond to the excitation delay times of the eight array elements, respectively. After applying the phase delay, the acoustic beam synthesized by the multiple array elements will accurately converge at the target depth, achieving focused detection.

[0018] Based on fixed-depth focusing, the controller dynamically adjusts the phase delay distribution to drive the acoustic beam to deflect and scan along the circumference of the pipe. The scanning angle range covers 0 to 360 degrees, with an angle step size set at 5 degrees to ensure no scanning blind spots. Each deflection angle corresponds to a set of independent phase delay and excitation pulses, which are emitted sequentially in time to form a continuous focused acoustic beam emission sequence. This sequence can achieve full coverage of the pipe interior at different angles and radial depths, providing a foundation for subsequent multi-dimensional echo signal acquisition.

[0019] The transducer array is controlled to receive the echo signal reflected by the scatterer in the fluid. After pre-amplification and anti-aliasing filtering, it is synchronously sampled by a multi-channel analog-to-digital converter to generate the original multi-channel echo data. The core of this step is to receive, condition, and digitally acquire weak echo signals, preserving Doppler frequency shift characteristics to provide high-quality raw data for subsequent signal processing. The specific implementation method is as follows: The fluid inside the pipe contains suspended particles, tiny bubbles, and other scatterers. When the focused acoustic beam irradiates the scatterers, it generates a Doppler frequency shift echo signal that is related to the fluid velocity. This signal is received by the same transducer array. Due to the attenuation of the echo signal through the pipe wall and the loss of fluid propagation, the amplitude is extremely weak. Therefore, it first enters the preamplifier circuit for signal amplification. The amplification gain is set to 40 dB to increase the weak echo signal to a manageable amplitude range while ensuring low-noise amplification and not introducing additional interference.

[0020] The amplified signal enters the anti-aliasing filter circuit. The filter uses a low-pass filter mode with a cutoff frequency set to 2 MHz, higher than the center frequency of the transmitted sound wave. Its function is to filter out high-frequency environmental noise and circuit interference, avoid frequency aliasing during analog-to-digital conversion, and ensure the spectral integrity of the echo signal. After filtering, the signal is sent to a multi-channel analog-to-digital converter for synchronous digital sampling. The converter uses 16-bit sampling precision and a sampling rate set to 10 MHz, much higher than the highest frequency of the signal, satisfying the sampling theorem requirements. All channels are driven by the same clock source to ensure that the sampling time of the echo signal of each array element is completely synchronized.

[0021] After sampling, the data is organized according to channel number, sampling time, and signal amplitude to generate original multi-channel echo data. The data fully preserves the amplitude, time, and phase information of the echo signal, corresponding to the echo characteristics of scatterers at different emission angles and propagation depths, providing raw input for subsequent beamforming and Doppler analysis.

[0022] The original multi-channel echo data is processed by channel delay compensation and beamforming, and the data is organized according to different transmission angles and reception depths to finally generate a set of Doppler echo signals covering different angles and depths.

[0023] The core of this step is to improve the echo signal-to-noise ratio through signal processing, complete the spatial regularization of multi-channel data, and form a standardized set of Doppler echo signals. The specific implementation method is as follows: First, channel delay compensation is performed on the original multi-channel echo data. The compensation amount includes two parts: the hardware transmission delay of the array elements and the sound wave propagation path difference. The compensation delay is marked as Δτ_c, and its value is calculated based on the geometric parameters of the transducer array and the sound wave propagation speed. This eliminates the time deviation between different channels and ensures that the time reference of the echo signals of all channels is unified.

[0024] Subsequently, beamforming is performed to coherently superimpose multi-channel echo signals at the same focusing angle and depth. Through phase alignment and amplitude weighting, random noise is effectively suppressed, and the signal-to-noise ratio of the echo signal is improved. In the example, the signal-to-noise ratio can be improved by more than 15 dB after beamforming, which significantly enhances the recognizability of Doppler frequency shift features.

[0025] After beamforming, the signals are classified and organized according to the deflection angle of the transmitted sound wave and the receiving depth of the echo propagation. The transmission angle is divided into 5-degree intervals, and the receiving depth is divided into 1-millimeter intervals. Each group of signals corresponds to a unique spatial sampling point in the pipe, which includes the angular coordinates, depth coordinates and echo time domain signal of that point.

[0026] By integrating the valid echo signals corresponding to all spatial locations, a set of Doppler echo signals covering different angles and depths within the pipeline is finally generated. This set provides a complete spatial sampling signal foundation for three-dimensional flow field measurement. The signals correspond one-to-one with the spatial coordinates and can be directly used for subsequent pulse compression, time-frequency analysis, and Doppler frequency shift extraction.

[0027] S202, perform pulse compression and time-frequency analysis on the Doppler echo signal set, extract the Doppler frequency shift of each spatial sampling point, and calculate the line-of-sight velocity component along the sound beam direction at each point by combining the transmission frequency and the sound velocity in the fluid. Specifically, each signal in the Doppler echo signal set can be subjected to matched filtering, and a reference signal matching the transmitted signal can be used for convolution to achieve pulse compression to improve range resolution and generate a range-time signal matrix. Each signal in the Doppler echo signal set is subjected to matched filtering, and a reference signal matching the transmitted signal is used for convolution to achieve pulse compression to improve range resolution, thereby generating a range-time signal matrix.

[0028] The core of this step is to improve the distance resolution of the echo signal through matched filtering and pulse compression techniques, compressing the diffuse echo signal into a narrow pulse to accurately correspond to spatial sampling points at different depths within the pipe, thus laying the foundation for subsequent time-frequency analysis. The specific implementation method is as follows: Doppler echo signals contain time-domain signals from different angles and depths. After propagation through the pipe and reflection by the scattering body, these signals exhibit pulse broadening and amplitude attenuation, resulting in low range resolution and an inability to accurately distinguish sampling points at adjacent depths. Therefore, matched filtering is necessary for signal optimization. The core principle of matched filtering is to use a reference signal that perfectly matches the waveform and frequency of the transmitted signal, and convolve it with the echo signal. This leverages the correlation between the signals to enhance the useful echo, suppress noise, and compress the broadened pulse into a narrow pulse, significantly improving range resolution.

[0029] The transmitted signal uses a single-frequency sinusoidal pulse train, with the center transmission frequency f_0 set to 1 MHz, a pulse width of 10 microseconds, and a pulse repetition period of 1 millisecond. The reference signal and the transmitted signal have completely identical waveforms, frequencies, and phases; only the amplitude is normalized (normalized to 1) to ensure maximum extraction of useful echo signals during convolution. During the convolution operation, each echo signal is multiplied point-by-point with the reference signal and then integrated to eliminate noise interference in the echo signal. Simultaneously, the pulse width is compressed to 0.1 microseconds, improving the distance resolution from 1.5 mm to 0.015 mm, enabling precise differentiation of scattering echoes from adjacent depths within the pipe.

[0030] Each echo signal, after matched filtering and pulse compression, yields a pulse signal with concentrated amplitude and narrow width. The peak position of the pulse corresponds to the propagation time of the echo signal, and thus to the sampling depth within the pipe. The pulse amplitude corresponds to the intensity of the echo signal. All processed signals are organized according to transmission angle and reception depth, constructing a two-dimensional matrix—the distance-time signal matrix—with time (corresponding to propagation distance) on the horizontal axis and signal amplitude on the vertical axis. Each row in the matrix corresponds to a fixed transmission angle, and each column corresponds to a fixed time point (i.e., a fixed depth). The matrix elements represent the echo signal amplitude at the corresponding location, comprehensively presenting the distribution of echo signals at different angles and depths, providing structured signal input for subsequent time-frequency analysis.

[0031] A short-time Fourier transform is performed on the range-time signal matrix along the fast time axis to obtain the time-spectrum diagram corresponding to each range unit, generating a range-Doppler spectrum data cube; A short-time Fourier transform is performed on the range-time signal matrix along the fast time axis to obtain the time-spectrum diagram corresponding to each range cell, generating a range-Doppler spectrum data cube.

[0032] The core of this step is to convert the time-domain echo signal into a time-frequency domain signal using short-time Fourier transform, extract the frequency characteristics of each range cell, and capture Doppler frequency shift information to provide a basis for subsequent frequency shift extraction. The specific implementation method is as follows: The distance-time signal matrix contains time and distance information of the time-domain signal, but it cannot directly reflect the Doppler frequency shift characteristics. Since the Doppler frequency shift is a core parameter reflecting fluid velocity, time-frequency analysis is needed to convert the time-domain signal into a time-frequency domain signal. The short-time Fourier transform (SFT) is a commonly used time-frequency analysis method. Its core principle is to divide the time-domain signal into multiple short time windows, perform a Fourier transform on the signal within each window, obtain the frequency distribution corresponding to each window, and thus obtain a two-dimensional correlation between time and frequency. This method can both preserve the time information of the signal and extract frequency features.

[0033] The fast time axis refers to the coordinate axis corresponding to the echo propagation time in the range-time signal matrix, reflecting the time-domain changes of the signal. A short-time Fourier transform is performed along the fast time axis; that is, the time-domain signal of each range unit (fixed depth) is segmented and Fourier transformed according to a time window. A Hanning window is selected for the time window, with a window length of 256 points and a window overlap rate of 50%. This ensures frequency resolution while avoiding signal distortion caused by window segmentation. The frequency resolution is controlled at 39.06 Hz, which can accurately capture minute Doppler shift changes.

[0034] During the Fourier transform, the time-domain signal within each time window is converted into a frequency-domain signal, resulting in a time-spectrum for each distance unit. The horizontal axis of the time-spectrum represents time, and the vertical axis represents frequency. The intensity of the color corresponds to the signal amplitude; a higher amplitude indicates a stronger frequency component. The component whose frequency deviates from the transmission frequency is the frequency signal corresponding to the Doppler shift. The time-spectrums of all distance units are integrated in distance order to construct a three-dimensional data structure, namely a distance-Doppler spectrum data cube. The three dimensions of this cube are distance (corresponding to the depth within the pipe), time, and frequency. Each cube element corresponds to a signal amplitude at a specific distance, time, and frequency, fully presenting the Doppler shift distribution characteristics at different depths and times, providing comprehensive spectral data support for subsequent peak frequency extraction.

[0035] The peak frequency of each spatial sampling point is extracted from the distance-Doppler spectrum data cube as the Doppler frequency shift, and the corresponding angle and distance coordinates of the point are recorded to generate a spatial sampling point Doppler frequency shift dataset. The peak frequency of each spatial sampling point is extracted from the distance-Doppler spectrum data cube as the Doppler frequency shift, and the corresponding angle and distance coordinates of the point are recorded to generate a spatial sampling point Doppler frequency shift dataset.

[0036] The core of this step is to accurately extract the Doppler frequency shift reflecting fluid velocity from the spectral data, correlate it with spatial coordinate information, and form a structured frequency shift dataset. This provides the core input for subsequent line-of-sight velocity component calculations. The specific implementation method is as follows: In the distance-Doppler spectrum data cube, each spatial sampling point corresponds to a fixed emission angle and a fixed pipe depth (distance). In the corresponding spectrum signal, the peak frequency is the Doppler frequency shift of the echo reflected by the scatterer at that sampling point. Because the intensity of the Doppler frequency shift signal is much higher than that of the noise signal, the signal amplitude corresponding to the peak frequency is the largest, which can be accurately extracted through the peak detection algorithm.

[0037] The core logic of the peak detection algorithm is to traverse the spectral data corresponding to each spatial sampling point and find the frequency point with the largest amplitude. This frequency point is the peak frequency, which is also the Doppler frequency shift f_d. During the extraction process, an amplitude threshold is set (30% of the largest amplitude), and noise frequency points with amplitudes below the threshold are removed to avoid false extraction. The extraction accuracy is controlled within 1 Hz to ensure the accuracy of the frequency shift. At the same time, the emission angle (i.e., the deflection angle of the transducer array element) and distance coordinate (i.e., the radial depth inside the pipe) corresponding to the sampling point are recorded, with an angle accuracy of 0.1 degrees and a distance accuracy of 0.01 millimeters.

[0038] In the example, a spatial sampling point has an emission angle of 30.0 degrees and a distance of 50.00 millimeters. After traversing its corresponding spectral data, the extracted peak frequency is 102 Hz, which is the Doppler frequency shift of this sampling point, f_d = 102 Hz. Another sampling point has an emission angle of 45.0 degrees and a distance of 60.00 millimeters, and the extracted peak frequency is -85 Hz. The negative sign indicates that the direction of motion of the scatterer is opposite to the direction of sound beam propagation. The emission angle, distance coordinates, and Doppler frequency shift of all spatial sampling points are integrated in sequence, and each sampling point is labeled with a unique identifier to form a spatial sampling point Doppler frequency shift dataset. Each entry in the dataset contains complete spatial coordinates and frequency shift information, without missing or anomalies, providing accurate parameter input for subsequent flow velocity calculations.

[0039] Based on the Doppler frequency shift formula and the sound velocity in the fluid and the emission frequency, the line-of-sight velocity component along the sound beam direction at each sampling point is calculated, generating a measurement dataset containing coordinate information and the line-of-sight velocity component.

[0040] Based on the Doppler frequency shift formula and the sound velocity in the fluid and the emission frequency, the line-of-sight velocity component along the sound beam direction at each sampling point is calculated, generating a measurement dataset containing coordinate information and the line-of-sight velocity component.

[0041] The core of this step is to utilize the acoustic Doppler principle to convert the extracted Doppler frequency shift into the actual velocity component of the fluid along the sound beam direction, correlate it with spatial coordinates, and form a standardized measurement dataset, providing a foundation for subsequent three-dimensional velocity vector reconstruction. The specific implementation method is as follows: The acoustic Doppler principle states that when a sound wave strikes a moving scatterer, the frequency of the reflected echo will shift. The amount of shift (Doppler shift) has a fixed mathematical relationship with the velocity of the scatterer (i.e., fluid velocity), the speed of sound, and the emission frequency. This relationship is the Doppler shift formula, which is v_r=(c×f_d) / (2×f_0), where v_r is the line-of-sight velocity component, c is the speed of sound in the fluid, f_d is the Doppler shift, and f_0 is the center frequency of the emitted sound wave.

[0042] The meanings and values ​​of the parameters in the formula are as follows: the line-of-sight velocity component v_r is the velocity component of the fluid along the direction of sound beam propagation, in meters per second. A positive value indicates that the direction of fluid movement is the same as the direction of sound beam propagation, and a negative value indicates that the direction is opposite; the sound speed c in the fluid is set according to the type of fluid medium. In the example, the measurement medium is clean water. At 25 degrees Celsius, the sound speed c of clean water is 1500 meters per second, and the measurement accuracy is controlled within 1 meter per second; the Doppler frequency shift f_d is the peak frequency extracted in step three, in Hertz; the transmission frequency f_0 = 1 MHz, which is a fixed value.

[0043] During the calculation, the Doppler frequency shift f_d, fluid sound velocity c, and transmission frequency f_0 of each sampling point are substituted into the formula to calculate the corresponding line-of-sight velocity component v_r one by one, with the calculation accuracy controlled within 0.001 meters per second. In the example, at a certain sampling point, f_d = 102 Hz, substituting into the formula, we get v_r = (1500 × 102) / (2 × 10^6) = 153000 / 2000000 = 0.0765 meters per second. A positive value indicates that the fluid at that point is moving forward along the direction of the sound beam. At another sampling point, f_d = -85 Hz, we calculate v_r = (1500 × (-85)) / (2 × 10^6) = -127500 / 2000000 = -0.06375 meters per second. A negative value indicates that the fluid is moving in the opposite direction of the sound beam.

[0044] After calculation, the spatial coordinates (emission angle, distance) of each sampling point are integrated with the corresponding line-of-sight velocity component v_r, and information such as sampling point identification and measurement time is added to generate a measurement dataset containing coordinate information and line-of-sight velocity components. This dataset has a standardized format, with each entry corresponding to a unique spatial sampling point within the pipe, clearly presenting the spatial position of each point and the velocity characteristics along the sound beam direction. It can be directly used for subsequent vector tomography reconstruction based on compressed sensing to achieve the calculation of three-dimensional velocity vectors.

[0045] S203, based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, adopts a vector tomography reconstruction algorithm based on compressed sensing to solve the three-dimensional velocity vector of each sampling point, thereby achieving decoupling of velocity direction and magnitude; Specifically, a three-dimensional mesh can be established based on the coordinates of spatial sampling points in the measurement dataset, and the line-of-sight velocity component of each sampling point can be associated with the corresponding measurement direction vector to construct a projection measurement matrix; A three-dimensional mesh is established based on the coordinates of spatial sampling points in the measurement dataset. The line-of-sight velocity component of each sampling point is associated with the corresponding measurement direction vector to construct a projection measurement matrix.

[0046] The core of this step is to normalize the discrete measurement data into a three-dimensional spatial mesh of the pipeline, establish a mathematical mapping relationship between the line-of-sight velocity and the three-dimensional velocity vector, and provide a basic constraint model for compressed sensing reconstruction. The specific implementation method is as follows: The measurement dataset includes the circumferential angle, radial depth, axial position coordinates, and corresponding line-of-sight velocity components of all spatial sampling points within the pipeline. First, a cylindrical three-dimensional computational grid is established with the pipeline axis as the center and the pipe wall as the boundary. The grid division takes into account both reconstruction accuracy and computational efficiency, with a radial step size of 5 mm, a circumferential step size of 3 degrees, and an axial step size of 10 mm. All discrete sampling points are mapped to corresponding grid nodes, and nodes not directly matched are assigned through neighbor interpolation, ultimately forming a three-dimensional regular grid covering the entire pipeline flow area.

[0047] The measurement direction vector refers to the unit direction vector of the ultrasonic beam corresponding to each sampling point in three-dimensional space. It is calculated from the transducer array geometric parameters and beam deflection angle. The vector contains three components: radial, circumferential, and axial, denoted as e_r, e_θ, and e_z. This vector represents the spatial projection direction corresponding to the line-of-sight velocity component. The three-dimensional velocity vector consists of three orthogonal components, denoted as v_x, v_y, and v_z. The line-of-sight velocity component is essentially the dot product of the three-dimensional velocity vector and the measurement direction vector.

[0048] Based on the above projection relationship, the measurement direction vectors of all sampling points are matched with the positions of grid nodes to construct a projection measurement matrix H. Each row of the matrix corresponds to the measurement direction vector of a sampling point, and each column corresponds to the velocity component of the three-dimensional grid node. The matrix dimension matches the number of sampling points and the number of grid nodes. In the example, there are 1200 sampling points and 800 grid nodes. The dimension of the projection measurement matrix is ​​1200×2400, and the matrix elements are the projection coefficients of the corresponding directions. A complete linear constraint relationship between the measured values ​​and the three-dimensional velocity vector to be determined is established, providing a core mathematical model for the subsequent reconstruction algorithm.

[0049] By selecting a wavelet basis or gradient sparse basis suitable for the fluid velocity field as the transformation domain, a sparse representation model of the three-dimensional velocity vector is established, and a sparse basis transformation matrix is ​​generated. By selecting a wavelet basis or gradient sparse basis suitable for the fluid velocity field as the transformation domain, a sparse representation model of the three-dimensional velocity vector is established, and a sparse basis transformation matrix is ​​generated.

[0050] The core of this step is to utilize the natural sparsity of the fluid velocity field to construct a sparse representation model, which satisfies the reconstruction prerequisite of the compressed sensing algorithm and reduces the redundancy of the three-dimensional velocity solution. The specific implementation method is as follows: The core premise of compressed sensing reconstruction is that the signal to be determined has sparsity in a certain transform domain, that is, most coefficients are zero or close to zero. The fluid velocity field in the pipe can be efficiently sparsely represented under wavelet basis or gradient sparse basis. In this study, the second-order discrete wavelet basis is selected as the transform basis, and the wavelet basis type is selected as db4 wavelet. This basis function has a high fitting degree for the continuously changing fluid velocity field and the best sparse representation effect. It can also be switched to gradient sparse basis according to the flow complexity, and sparse expression can be achieved by taking advantage of the small spatial gradient of the velocity field.

[0051] The sparse representation model of the three-dimensional velocity vector decomposes the original velocity vector into the product of the sparse basis transformation matrix and the sparse coefficient vector, expressed as v=Ψ×x, where v is the set of three-dimensional velocity vectors to be determined, Ψ is the sparse basis transformation matrix, and x is the sparse coefficient vector. The vast majority of elements in the sparse coefficient vector have magnitudes close to zero, and only a few coefficients correspond to the main structure of the velocity field, thus satisfying the sparsity requirement.

[0052] The dimension of the sparse basis transformation matrix Ψ matches the number of nodes in the 3D grid. Each grid node corresponds to three velocity components. The matrix dimension is 3N×3N, where N is the total number of grid nodes. In the example, N=800, and the matrix dimension is 2400×2400. The matrix elements are composed of the values ​​of wavelet basis functions at the grid nodes. During the generation process, the matrix is ​​orthogonalized to ensure the invertibility and stability of the transformation. This matrix can convert the 3D velocity vector into a coefficient vector in the sparse domain, which greatly reduces the solution variables of the reconstruction algorithm and improves the solution efficiency and anti-interference ability.

[0053] A compressed sensing reconstruction algorithm is used to solve an optimization problem constrained by the projection measurement matrix and the sparse basis transformation matrix. The three-dimensional flow velocity vector is recovered by iterative thresholding or Bayesian methods to generate an initial reconstructed flow velocity vector field. The compressed sensing reconstruction algorithm is used to solve the optimization problem constrained by the projection measurement matrix and the sparse basis transformation matrix. The three-dimensional flow velocity vector is recovered by iterative thresholding or Bayesian method to generate the initial reconstructed flow velocity vector field.

[0054] The core of this step is to achieve accurate estimation of the sparsity coefficients through optimization, thereby recovering the three-dimensional velocity vector of each grid node and completing the initial decoupling of the velocity magnitude and direction. The specific implementation method is as follows: By combining the projection measurement matrix and the sparse basis transformation matrix, a compressed sensing constrained optimization problem is constructed. The objective function is to minimize the L1 norm of the sparse coefficient vector. The constraint condition is that the product of the projection measurement matrix and the three-dimensional velocity vector is equal to the measured line-of-sight velocity component. This optimization problem can effectively balance sparsity and measurement accuracy and avoid solution deviations caused by noise interference.

[0055] The algorithm uses the iterative threshold method, which is computationally stable and converges quickly, making it suitable for real-time reconstruction of pipeline velocity fields. The maximum number of iterations is set to 50, and the iteration convergence threshold is 0.001 m / s. In each iteration, the sparse coefficients are thresholded to remove small amplitude coefficients and enhance the sparsity characteristics. For low signal-to-noise ratio measurement data, the Bayesian reconstruction method can also be switched to introduce the prior probability distribution of sparse coefficients and improve the reconstruction robustness through maximum a posteriori probability estimation.

[0056] After solving, the sparse coefficient vector is multiplied by the sparse basis transformation matrix to recover the three-dimensional velocity vector of each three-dimensional mesh node, which contains three orthogonal components: radial, circumferential, and axial. This achieves preliminary decoupling of the velocity direction and magnitude. The velocity vectors of all nodes are integrated to form the initial reconstructed velocity vector field. In the example, the three-dimensional velocity vector recovered from a certain mesh node is v_x = 0.082 m / s, v_y = -0.031 m / s, and v_z = 0.005 m / s. The vector direction and magnitude fully represent the fluid motion state at that point, and the initial field already has complete three-dimensional flow characteristics, but there are still a few abnormal vectors caused by noise.

[0057] Spatial consistency verification is performed on the initial reconstructed velocity vector field to remove abnormal vectors caused by noise, and the fluid continuity equation is used for correction to finally generate an accurate three-dimensional velocity vector for each sampling point.

[0058] Spatial consistency verification is performed on the initial reconstructed velocity vector field to remove abnormal vectors caused by noise, and the fluid continuity equation is used for correction to finally generate an accurate three-dimensional velocity vector for each sampling point.

[0059] The core of this step is to eliminate reconstruction noise and errors through spatial verification and physical equation constraints, thereby obtaining an accurate three-dimensional velocity vector that conforms to the laws of fluid motion, ensuring the physical rationality of the measurement results. The specific implementation method is as follows: Spatial consistency verification is based on the spatial continuity of the fluid velocity field. The threshold for the difference between the velocity components of adjacent grid nodes is set to 0.05 m / s. All three-dimensional grid nodes are traversed, and the velocity components of the current node are compared with the six neighboring nodes one by one. If the velocity component of a certain node exceeds the difference threshold, it is determined to be an abnormal vector caused by noise. It is directly removed and filled by weighted interpolation of neighboring nodes. The removal process retains the real flow change region and only eliminates random noise interference.

[0060] The correction process employs the continuity equation for incompressible fluids, where the velocity field divergence is zero (∇・v=0). This equation serves as the core physical constraint for fluid motion within the pipe and effectively corrects reconstruction errors. The initial reconstructed velocity vector field is substituted into the continuity equation, and the divergence value at each node is calculated. Nodes with non-zero divergence are corrected using the Lagrange multiplier method, fine-tuning the three-dimensional velocity components to bring the overall divergence close to zero. The correction process preserves the overall distribution characteristics of the velocity field, correcting only physically unreasonable deviations.

[0061] After verification and correction, a precise three-dimensional velocity vector is generated for each spatial sampling point, with the vector accuracy improved to 0.001 meters per second and the direction calculation error less than 2 degrees, which fully meets the requirements for three-dimensional velocity field measurement of pipelines. All precise velocity vectors are organized in the order of grid nodes to form a standardized three-dimensional velocity vector dataset, providing accurate data support for subsequent velocity field modeling and flow calculation.

[0062] S204. Based on the spatial distribution of the three-dimensional velocity vector on the pipe cross-section, a three-dimensional velocity field model inside the pipe is generated by interpolation fitting, and the cross-sectional flow rate and flow characteristic parameters are calculated based on the model.

[0063] Specifically, the three-dimensional velocity vectors of discrete sampling points can be interpolated on the pipe cross section and along the pipe direction. A continuous velocity field distribution on a regular grid can be generated by using radial basis functions or kriging interpolation methods, thus generating a three-dimensional velocity field interpolation model. The three-dimensional velocity vectors of discrete sampling points are interpolated on the pipe cross section and along the pipe direction. A continuous velocity field distribution on a regular grid is generated using radial basis functions or kriging interpolation methods, thus generating a three-dimensional velocity field interpolation model.

[0064] The core of this step is to use interpolation algorithms to fill in the velocity information in unsampled areas within the pipe from discrete three-dimensional velocity vector data, constructing a continuous three-dimensional velocity field model covering the entire pipe. This provides a complete velocity distribution foundation for subsequent flow calculations and flow regime analysis. The specific implementation method is as follows: The three-dimensional velocity vectors of discrete sampling points only cover a portion of the spatial location within the pipe and cannot fully reflect the velocity distribution over the entire pipe. The purpose of interpolation is to utilize the velocity characteristics of adjacent sampling points and use mathematical algorithms to infer the velocity in unsampled areas, thus achieving a continuous velocity field. This study selects the radial basis function interpolation method, which offers high fitting accuracy and strong noise resistance for continuously changing physical quantities like fluid velocity fields. It is well-suited to the spatial distribution characteristics of velocity within the pipe and can also be switched to the Kriging interpolation method based on the sampling point density, further improving interpolation accuracy by utilizing spatial correlation.

[0065] The radial basis function used is a Gaussian radial basis function, expressed as φ(r) = exp(-(εr)^2), where r is the spatial distance between the interpolation point and the sampling point in meters, and ε is a shape parameter used to adjust the smoothness of the interpolation function. Combined with the pipe dimensions, ε is set to 0.1 to ensure that the interpolation result closely matches the measured data while avoiding local distortion caused by overfitting. During the interpolation process, based on the three-dimensional regular mesh generated in step four, each mesh node is used as the interpolation point. The five nearest discrete sampling points around each node are selected, and the weight coefficients of each sampling point are calculated using the radial basis function. The weight coefficients are negatively correlated with the distance from the sampling point to the interpolation point; the closer the distance, the greater the weight. The three-dimensional velocity vector of the interpolation point is then obtained through weighted summation.

[0066] In the example, a certain grid node to be interpolated is located at a radial depth of 55 mm, a circumferential angle of 35 degrees, and an axial position of 100 mm in the pipe. Five discrete sampling points are selected around the node, with their three-dimensional velocity vectors being (0.078, -0.029, 0.004) m / s, (0.083, -0.032, 0.005) m / s, etc. The weighting coefficients obtained by radial basis function calculation are 0.28, 0.25, etc. After weighted summation, the three-dimensional velocity vector of the node to be interpolated is obtained as (0.081, -0.031, 0.0045) m / s. After interpolation of all grid nodes, they are integrated to form a continuous velocity field distribution on a regular grid, i.e., a three-dimensional velocity field interpolation model. This model completely covers the radial, circumferential, and axial directions of the pipe, with a continuous and smooth velocity distribution. The interpolation error is controlled within 0.002 m / s, which can accurately reflect the velocity characteristics of the entire pipe.

[0067] The cross-section of the pipe in the three-dimensional velocity field interpolation model is integrated by surface integral. The normal velocity component is integrated on the cross-section to calculate the volumetric flow rate through the cross-section and generate the cross-sectional flow rate calculation result. The cross-section of the pipe in the three-dimensional velocity field interpolation model is integrated by surface integral. The normal velocity component is integrated on the cross-section to calculate the volumetric flow rate through the cross-section and generate the cross-sectional flow rate calculation result.

[0068] The core of this step is based on a continuous velocity field model, which calculates the volumetric flow rate of the pipe cross-section through area integration, achieving non-contact flow measurement and providing accurate data for pipe flow monitoring. The specific implementation method is as follows: The core principle of volumetric flow rate calculation is that the flow rate through the cross-section of a pipe is equal to the sum of the products of the normal velocity components of all infinitesimal areas on that cross-section and the areas of the infinitesimal elements. That is, the summation is achieved through area integration. The normal velocity component refers to the velocity component perpendicular to the cross-section of the pipe, which is the velocity component v_z along the pipe axis. Since the cross-section of the pipe is perpendicular to the axis, the normal velocity directly determines the rate at which the fluid passes through the cross-section.

[0069] First, a fixed axial position inside the pipe is selected as the measurement section. This section is perpendicular to the pipe axis, and the radius of the section is equal to the inner diameter of the pipe. In this example, the inner diameter of the pipe is 180 mm, and the axial position of the measurement section is 200 mm. This cross-section is divided into several micro-element areas. The micro-element is a ring-shaped micro-element with a radial step of 1 mm and a circumferential step of 3 degrees. The area of ​​each micro-element is dA = 2πr × dr, where r is the radial distance from the micro-element to the center of the pipe, and dr is the radial step. The finer the micro-element division, the higher the accuracy of the flow calculation. In this case, the number of micro-elements is 5400.

[0070] Subsequently, the normal velocity component v_z corresponding to the center of each micro-element is extracted. v_z is multiplied by the corresponding micro-element area dA to obtain the flow rate micro-element dQ = v_z × dA. Then, the flow rate micro-element values ​​of all micro-elements are integrated and summed to obtain the volumetric flow rate Q = ∫∫v_z dA of the entire cross-section. The integration range covers the entire pipe cross-section, from the pipe center (r=0) to the pipe wall (r=90 mm). During the calculation, numerical integration is used to improve accuracy, with integration error controlled within 0.5%. In the example, the volumetric flow rate Q = 0.086 cubic meters per second obtained through integration is equivalent to 309.6 cubic meters per hour. Simultaneously, information such as the measurement section location, calculation time, and micro-element division parameters are recorded to generate the cross-sectional flow rate calculation results, ensuring the traceability of the results.

[0071] The flow characteristic parameters, including cross-sectional average velocity, turbulence intensity, vorticity distribution and Reynolds number, are calculated based on the three-dimensional velocity field interpolation model to generate a set of flow characteristic parameters. The flow characteristic parameters, including cross-sectional average velocity, turbulence intensity, vorticity distribution and Reynolds number, are calculated based on a three-dimensional velocity field interpolation model to generate a set of flow characteristic parameters.

[0072] The core of this step is to extract key parameters reflecting the fluid flow state from the continuous velocity field model, quantify the flow characteristics, and provide a basis for pipeline flow state judgment and anomaly analysis. The specific implementation method is as follows: The cross-sectional average velocity is a core parameter reflecting the overall flow velocity of fluid within a pipe. It is calculated as the ratio of volumetric flow rate to the pipe's cross-sectional area, using the formula v_avg=Q / A, where v_avg is the cross-sectional average velocity in meters per second (m / s), Q is the volumetric flow rate calculated in step two, and A is the pipe's cross-sectional area (A=πR^2), where R is the pipe's inner diameter. In this example, the pipe's inner diameter is 180 mm, so A=π×(0.09)^2≈0.0254 square meters. Combining this with Q=0.086 cubic meters per second, we calculate v_avg=0.086 / 0.0254≈3.386 m / s, with an accuracy controlled within 0.001 m / s.

[0073] Turbulence intensity is a parameter reflecting the degree of fluid velocity fluctuation and characterizing the stability of the flow regime. It is calculated as the ratio of the root mean square (RMS) of the velocity fluctuation to the average velocity of the cross section, with the formula I = √(v'^2) / v_avg, where v' is the velocity fluctuation component, i.e., the difference between the actual velocity and the average velocity at a certain point. In the calculation, 100 uniformly distributed grid nodes are selected on the cross section of the pipe, the axial velocity component of each node is extracted, the velocity fluctuation component of each node is calculated, and then the RMS is calculated. In the example, the RMS of the velocity fluctuation is calculated to be 0.406 m / s, and the turbulence intensity I = 0.406 / 3.386 ≈ 0.12. The larger the value, the more unstable the flow regime and the stronger the turbulence.

[0074] Vorticity distribution is a parameter reflecting the rotational motion of a fluid. Vorticity is the curl of a three-dimensional velocity field, denoted as ω=∇×v, and includes three components: x, y, and z, corresponding to the rotational intensity in the three directions, respectively. The curl is calculated by the difference in velocity components between adjacent grid nodes. In the example, the vorticity components of a certain grid node are ω_x=0.012 rad / s, ω_y=-0.008 rad / s, and ω_z=0.005 rad / s. The larger the vorticity amplitude, the more intense the fluid rotation in that region. The vorticity data of all nodes are integrated to form the vorticity distribution.

[0075] The Reynolds number is a core dimensionless parameter for determining the flow regime (laminar or turbulent), and its formula is Re = v_avg × D / ν, where D is the pipe inner diameter in meters, and ν is the fluid kinematic viscosity in square meters per second. In the example, the fluid is clean water, and at 25 degrees Celsius, ν = 1.004 × 10^-6 square meters per second, D = 0.18 meters, and v_avg = 3.386 meters per second. The calculated Re = 3.386 × 0.18 / (1.004 × 10^-6) ≈ 607000. A Reynolds number greater than 4000 indicates turbulent flow; therefore, the flow regime in this pipe is turbulent. By integrating the cross-sectional average velocity, turbulence intensity, vorticity distribution, Reynolds number, and related calculation parameters, a set of flow regime characteristic parameters is generated, providing a complete quantification of the fluid flow state within the pipe.

[0076] The cross-sectional flow rate calculation results and the flow characteristic parameter set are visualized and rendered to generate a three-dimensional velocity field distribution map and flow analysis report inside the pipeline.

[0077] The cross-sectional flow rate calculation results and the flow characteristic parameter set are visualized and rendered to generate a three-dimensional velocity field distribution map and flow analysis report inside the pipeline.

[0078] The core of this step is to transform abstract numerical data into intuitive graphical and textual reports, clearly presenting the velocity field distribution and flow characteristics within the pipeline, facilitating quick understanding and application of measurement results by staff. The specific implementation method is as follows: The visualization rendering employs 3D rendering technology, based on a 3D velocity field interpolation model, to generate various visualization graphics that intuitively present the spatial distribution characteristics of the velocity field. The velocity vector map uses arrows to represent the 3D velocity direction and magnitude of each grid node. The arrow direction corresponds to the velocity direction, and the arrow length corresponds to the velocity magnitude. Color depth helps distinguish the velocity amplitude, with the velocity range from 0 to 4 meters per second. A gradient color scheme is used, with blue representing low velocity and red representing high velocity. The isovelocity map is used to present the velocity distribution on the pipe cross-section. Nodes with the same velocity are connected to form isovelocity lines, clearly reflecting the radial distribution pattern of the velocity. The vorticity cloud map uses a color gradient to present the vorticity distribution; the darker the color, the greater the vorticity, intuitively showing areas of intense fluid rotation.

[0079] The flow regime analysis report integrates all measurement data and analysis results in text form. The report includes basic measurement information (pipe size, measurement time, fluid medium), cross-sectional flow rate calculation results (volume flow rate, unit conversion, calculation accuracy), flow regime characteristic parameters (cross-sectional average velocity, turbulence intensity, Reynolds number, vorticity distribution characteristics), flow regime judgment conclusion (based on Reynolds number to determine whether it is turbulent or laminar flow, and combining turbulence intensity to explain flow regime stability), and anomaly analysis (if there are abnormal flow velocities, excessive vorticity, etc., analyze possible causes). The example report clearly indicates that the measured pipe inner diameter is 180 mm, the fluid is 25°C clean water, the volumetric flow rate is 0.086 m / s, the cross-sectional average velocity is 3.386 m / s, the Reynolds number is 607000, the flow regime is turbulent, the turbulence intensity is 0.12, the overall flow regime is stable, and slight vortices exist locally.

[0080] The system integrates visualization graphics with flow analysis reports to generate a complete 3D velocity field distribution map and flow analysis report within the pipeline. The graphics are clear and intuitive, and the report data is detailed and the conclusions are clear. It can be directly used for pipeline flow monitoring, flow optimization, fault diagnosis and other scenarios, providing precise technical support for pipeline operation and management.

[0081] Another embodiment of the present invention provides a non-contact three-dimensional flow velocity field measurement system in a pipe based on acoustic Doppler, see [link to relevant documentation]. Figure 3 The system may include: The receiving module 301 is used to transmit a focused acoustic beam into the pipe and receive the echo signal reflected by the scattering body in the fluid through a multi-element ultrasonic transducer array deployed on the outer wall of the pipe, thereby generating a set of Doppler echo signals covering different angles and depths. Extraction module 302 is used to perform pulse compression and time-frequency analysis on the Doppler echo signal set, extract the Doppler frequency shift of each spatial sampling point, and calculate the line-of-sight velocity component of each point along the sound beam direction by combining the transmission frequency and the sound speed in the fluid. The calculation module 303 is used to calculate the three-dimensional velocity vector of each sampling point based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, using a vector tomography reconstruction algorithm based on compressed sensing, thereby achieving decoupling of velocity direction and magnitude. The generation module 304 is used to generate a three-dimensional velocity field model in the pipe by interpolation fitting based on the spatial distribution of the three-dimensional velocity vector on the pipe cross section, and to calculate the cross section flow rate and flow characteristic parameters based on the model.

[0082] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0083] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0084] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0085] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A non-contact method for measuring three-dimensional flow velocity field inside a pipe based on acoustic Doppler, characterized in that, The method includes: By deploying a multi-element ultrasonic transducer array on the outer wall of the pipe, a focused acoustic beam is emitted into the pipe and the echo signal reflected by the scattering body in the fluid is received, generating a set of Doppler echo signals covering different angles and depths. The Doppler echo signal set is subjected to pulse compression and time-frequency analysis to extract the Doppler frequency shift of each spatial sampling point. Combined with the transmission frequency and the sound velocity in the fluid, the line-of-sight velocity component along the sound beam direction at each point is calculated. Based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, a vector tomography reconstruction algorithm based on compressed sensing is used to calculate the three-dimensional velocity vector of each sampling point, thereby achieving decoupling of velocity direction and magnitude. Based on the spatial distribution of the three-dimensional velocity vector on the pipe cross section, a three-dimensional velocity field model inside the pipe is generated by interpolation fitting, and the cross-sectional flow rate and flow characteristic parameters are calculated based on the model.

2. The method according to claim 1, characterized in that, The method involves using a multi-element ultrasonic transducer array deployed on the outer wall of the pipe to emit a focused acoustic beam into the pipe and receive echo signals reflected by scatterers in the fluid, generating a set of Doppler echo signals covering different angles and depths, including: Based on the outer diameter of the pipe and the measurement requirements, the multi-element ultrasonic transducer array is evenly arranged on the outer circumference of the pipe at predetermined angular intervals. The spatial coordinates and pointing angles of each element are recorded to generate a table of geometric parameters of the transducer array. By using a phased array beamforming controller, excitation pulses with specific phase delays are applied to each array element, so that the synthesized beam is focused at a specified depth inside the pipe and beam deflection scanning is achieved, generating a focused acoustic beam emission sequence. The transducer array is controlled to receive the echo signal reflected by the scatterer in the fluid. After pre-amplification and anti-aliasing filtering, it is synchronously sampled by a multi-channel analog-to-digital converter to generate the original multi-channel echo data. The original multi-channel echo data is processed by channel delay compensation and beamforming, and the data is organized according to different transmission angles and reception depths to finally generate a set of Doppler echo signals covering different angles and depths.

3. The method according to claim 2, characterized in that, The process of performing pulse compression and time-frequency analysis on the Doppler echo signal set, extracting the Doppler frequency shift at each spatial sampling point, and calculating the line-of-sight velocity component along the sound beam direction at each point by combining the transmission frequency and the sound velocity in the fluid, includes: Each signal in the Doppler echo signal set is subjected to matched filtering, and a reference signal that matches the transmitted signal is used for convolution to achieve pulse compression to improve range resolution and generate a range-time signal matrix. A short-time Fourier transform is performed on the range-time signal matrix along the fast time axis to obtain the time-spectrum diagram corresponding to each range unit, generating a range-Doppler spectrum data cube; The peak frequency of each spatial sampling point is extracted from the distance-Doppler spectrum data cube as the Doppler frequency shift, and the corresponding angle and distance coordinates of the point are recorded to generate a spatial sampling point Doppler frequency shift dataset. Based on the Doppler frequency shift formula and the sound velocity in the fluid and the emission frequency, the line-of-sight velocity component along the sound beam direction at each sampling point is calculated, generating a measurement dataset containing coordinate information and the line-of-sight velocity component.

4. The method according to claim 3, characterized in that, Based on the coordinate positions of each spatial sampling point and the corresponding line-of-sight velocity components, a vector tomography reconstruction algorithm based on compressed sensing is used to calculate the three-dimensional velocity vector of each sampling point, achieving decoupling of velocity direction and magnitude, including: A three-dimensional mesh is established based on the coordinates of spatial sampling points in the measurement dataset. The line-of-sight velocity component of each sampling point is associated with the corresponding measurement direction vector to construct a projection measurement matrix. By selecting a wavelet basis or gradient sparse basis suitable for the fluid velocity field as the transformation domain, a sparse representation model of the three-dimensional velocity vector is established, and a sparse basis transformation matrix is ​​generated. A compressed sensing reconstruction algorithm is used to solve an optimization problem constrained by the projection measurement matrix and the sparse basis transformation matrix. The three-dimensional flow velocity vector is recovered by iterative thresholding or Bayesian methods to generate an initial reconstructed flow velocity vector field. Spatial consistency verification is performed on the initial reconstructed velocity vector field to remove abnormal vectors caused by noise, and the fluid continuity equation is used for correction to finally generate an accurate three-dimensional velocity vector for each sampling point.

5. The method according to claim 4, characterized in that, The process of generating a three-dimensional velocity field model within the pipe by interpolation fitting based on the spatial distribution of the three-dimensional velocity vector on the pipe cross-section, and calculating the cross-sectional flow rate and flow characteristic parameters based on this model, includes: The three-dimensional velocity vectors of discrete sampling points are interpolated on the pipe cross section and along the pipe direction. A continuous velocity field distribution on a regular grid is generated by using radial basis functions or kriging interpolation methods, thus generating a three-dimensional velocity field interpolation model. The cross-section of the pipe in the three-dimensional velocity field interpolation model is integrated by surface integral. The normal velocity component is integrated on the cross-section to calculate the volumetric flow rate through the cross-section and generate the cross-sectional flow rate calculation result. The flow characteristic parameters, including cross-sectional average velocity, turbulence intensity, vorticity distribution and Reynolds number, are calculated based on the three-dimensional velocity field interpolation model to generate a set of flow characteristic parameters. The cross-sectional flow rate calculation results and the flow characteristic parameter set are visualized and rendered to generate a three-dimensional velocity field distribution map and flow analysis report inside the pipeline.

6. A non-contact three-dimensional flow velocity field measurement system in a pipe based on acoustic Doppler, characterized in that, The system includes: The receiving module is used to transmit a focused acoustic beam into the pipe through a multi-element ultrasonic transducer array deployed on the outer wall of the pipe and receive the echo signal reflected by the scattering body in the fluid, generating a set of Doppler echo signals covering different angles and depths. The extraction module is used to perform pulse compression and time-frequency analysis on the Doppler echo signal set, extract the Doppler frequency shift of each spatial sampling point, and calculate the line-of-sight velocity component along the sound beam direction at each point by combining the transmission frequency and the sound velocity in the fluid. The calculation module is used to calculate the three-dimensional velocity vector of each sampling point based on the coordinate position of each spatial sampling point and the corresponding line-of-sight velocity component, using a vector tomography reconstruction algorithm based on compressed sensing, thereby achieving decoupling of velocity direction and magnitude. The generation module is used to generate a three-dimensional velocity field model inside the pipe by interpolation fitting based on the spatial distribution of the three-dimensional velocity vector on the pipe cross section, and to calculate the cross section flow rate and flow characteristic parameters based on the model.

7. The system according to claim 6, characterized in that, The receiving module is specifically used for: Based on the outer diameter of the pipe and the measurement requirements, the multi-element ultrasonic transducer array is evenly arranged on the outer circumference of the pipe at predetermined angular intervals. The spatial coordinates and pointing angles of each element are recorded to generate a table of geometric parameters of the transducer array. By using a phased array beamforming controller, excitation pulses with specific phase delays are applied to each array element, so that the synthesized beam is focused at a specified depth inside the pipe and beam deflection scanning is achieved, generating a focused acoustic beam emission sequence. The transducer array is controlled to receive the echo signal reflected by the scatterer in the fluid. After pre-amplification and anti-aliasing filtering, it is synchronously sampled by a multi-channel analog-to-digital converter to generate the original multi-channel echo data. The original multi-channel echo data is processed by channel delay compensation and beamforming, and the data is organized according to different transmission angles and reception depths to finally generate a set of Doppler echo signals covering different angles and depths.

8. The system according to claim 7, characterized in that, The extraction module is specifically used for: Each signal in the Doppler echo signal set is subjected to matched filtering, and a reference signal that matches the transmitted signal is used for convolution to achieve pulse compression to improve range resolution and generate a range-time signal matrix. A short-time Fourier transform is performed on the range-time signal matrix along the fast time axis to obtain the time-spectrum diagram corresponding to each range unit, generating a range-Doppler spectrum data cube; The peak frequency of each spatial sampling point is extracted from the distance-Doppler spectrum data cube as the Doppler frequency shift, and the corresponding angle and distance coordinates of the point are recorded to generate a spatial sampling point Doppler frequency shift dataset. Based on the Doppler frequency shift formula and the sound velocity in the fluid and the emission frequency, the line-of-sight velocity component along the sound beam direction at each sampling point is calculated, generating a measurement dataset containing coordinate information and the line-of-sight velocity component.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-5 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-5.

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