Pipeline flow-induced noise testing system and method

Abstract

The invention provides a method for testing flow-induced noise of a pipeline. The method comprises the following steps: S1, determining measurement positions at an upstream flow channel and a downstream flow channel of a measured element, carrying out normalization processing on measured signals, and calculating the pressure power spectrum density of the inner wall surface of a pipe; s2, calculating a wall surface pressure fluctuation time domain cross correlation coefficient, and analyzing correlation characteristics between wall surface pressure pulsation; s3, spectrum intrinsic orthogonal decomposition is carried out on the pressure pulsation field, and space-time decoupling is achieved through time domain-frequency domain conversion; s4, extracting a dominant flow-induced noise mode, constructing an empirical frequency-wave number spectrum, and decomposing fluctuating energy distribution into contributions of different frequency and wave number combinations; s5, analyzing the decoupled flow-induced noise characteristic result, and identifying the amplitude-frequency characteristic of the flow-induced noise; according to the invention, real-time measurement of pressure pulsation in the pipeline can be realized, decoupling analysis of flow field pulsation and sound pressure pulsation can be carried out on the measured signal, and characteristics and a generation mechanism of pipeline flow-induced noise can be identified.

Description

Technical Field

[0001] The present invention relates to the field of testing technology, and in particular to a pipeline flow-induced noise testing system and method. Background Art

[0002] In engineering applications and daily life, all kinds of pipelines are everywhere. From water supply systems to energy transmission, pipelines undertake many important functions. However, the noise generated by the flow of fluid in the pipeline often has adverse effects on the working environment and human physical and mental health. The sources of pipeline noise are very complex, including the pulsation of the fluid flow itself, the interaction of the fluid when it flows through the components in the pipe, etc. As people pay more attention to the impact of noise on health, how to effectively test and control pipeline noise has become an urgent problem to be solved. Existing pipeline noise testing methods are mostly limited to a specific frequency range, and have not yet provided effective testing methods for high-frequency noise in complex flow environments. Therefore, the development of a new pipeline noise testing system and method is not only of great significance to improving the comfort of the working environment, but also can provide the necessary data support for a better understanding of the flow characteristics of fluids in pipelines, thereby providing a theoretical basis for noise control and pipeline design optimization.

[0003] Among the existing noise testing methods, there are "sound intensity method", "microphone array method", "sound pressure measurement method", etc. These methods mainly infer the noise source indirectly by measuring sound pressure or sound intensity. However, in pipeline systems with high-frequency noise and complex noise sources, these methods are usually unable to accurately capture the full picture of the noise.

[0004] This paper proposes a new test system and method, which uses a 10-microphone dynamic pressure sensor to measure the pressure pulsation signal, and distinguishes the different characteristics of the pressure pulsation signal from the noise source through advanced decoupling analysis technology. This method can more accurately capture the spatial distribution and frequency characteristics of the pipeline noise source, and provide more accurate data support for the identification and control of the noise source. Summary of the invention

[0005] The present invention proposes a pipeline flow-induced noise testing system and method, that is, a novel pipeline flow-induced noise testing system and method based on high-frequency dynamic pressure signals, which can realize real-time measurement of pressure pulsation in the pipeline, and can also realize decoupling analysis of flow field pulsation and acoustic pressure pulsation of the measured signal, thereby identifying the characteristics and generation mechanism of pipeline flow-induced noise.

[0006] The present invention adopts the following technical solutions.

[0007] A pipeline flow-induced noise testing method comprises the following steps: Step S1, determining the measurement positions of the high-frequency dynamic pressure signals of several microphones at the upstream and downstream flow channels of the measured element, normalizing the in-pipe pulsating pressure signals measured at these positions with the in-pipe flow velocity collected by the Pitot tube, calculating the in-pipe wall pressure power spectrum density, and exploring the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise; Step S2, calculating the time domain correlation coefficient of the wall pressure fluctuation, analyzing the correlation characteristics between the wall pressure pulsations, and intuitively displaying the correlation of the physical quantity at each position; Step S3, performing spectral intrinsic orthogonal decomposition on the pressure pulsation field, realizing time-space decoupling through time domain-frequency domain conversion, thereby optimally identifying the flow-induced noise distribution characteristics; Step S4, perform principal component analysis on the signal, extract the dominant flow-induced noise mode, perform two-dimensional Fourier transform, construct an empirical frequency-wavenumber spectrum, and decompose the energy distribution of the fluctuation into contributions of different frequency and wavenumber combinations; Step S5: Analyze the decoupled flow-induced noise characteristic results to identify the amplitude-frequency characteristics of the flow-induced noise.

[0008] The test system comprises an air source system, a data acquisition system, a data processing system and an acoustic system; the air source system comprises a silent fan (2), a frequency converter, a flexible hose (3), a sound insulation pad (4) and a pitot tube (5) located at the upstream flow channel; the data acquisition system comprises a measured element (7) located between the upstream flow channel and the downstream flow channel, and also comprises an upstream microphone array (6) and a downstream microphone array (8) for measuring a microphone high-frequency dynamic pressure signal, and each microphone dynamic pressure sensor comprises a microphone pointing toward the flow channel; The measured element is a replaceable pipe; The data processing system comprises a workstation (1) for running a data processing algorithm; and the acoustic system comprises an exponential diffuser (9) and an anechoic chamber (10) which are sequentially connected at a downstream flow channel.

[0009] The method for using the test system comprises the following steps: Step 1: Calculate the shear number of waves in the pipeline according to the inner diameter of the measured element, the kinematic viscosity of the fluid medium and the frequency of the measured element, and determine the installation spacing of each microphone according to the calculation results; Step 2: Start the equipment and make the silent fan work in the suction mode; Step 3: Calibrate the microphone of the test process microphone dynamic pressure sensor in the test using a standard microphone; Step 4: In the range of interest, obtain the amplitude-frequency and phase-frequency characteristic curves and transfer functions, verify whether the autopower spectrum of the signal to be calibrated is consistent with the standard signal, and then reconstruct the data of the experimental signal; Step 5: If the autopower spectrum of the signal to be calibrated does not match the standard signal, repeat step 4; Step 6: Install the measured element and use a Pitot tube to collect the flow velocity information in the tube; Step 7: Obtain the pulsating pressure signal in the pipe, respectively, under the conditions that the component to be tested is not installed and the component to be tested is installed, collect the high-frequency dynamic pressure signals provided by ten high-frequency dynamic pressure sensors and calculate their decibel values; Step 8: Calculate the power spectrum density of the inner wall pressure to explore the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise; Step 9: Calculate the time domain correlation coefficient of the wall pressure fluctuations and analyze the correlation characteristics between the wall pressure pulsations; Step 10: Perform spectral intrinsic orthogonal decomposition on the pressure pulsation field, realize time-space decoupling through time domain-frequency domain conversion, and decompose to obtain the optimal flow field mode with both time orthogonality and space orthogonality; Step 11: Perform principal component analysis on the pressure pulsation field to extract the dominant flow-induced noise mode; perform two-dimensional Fourier transform on the pressure field reconstructed by the other mode decomposition to construct the empirical frequency-wavenumber spectrum; Step 12: Analyze the decoupled flow-induced noise characteristic results, identify the frequency and amplitude-frequency characteristics of the flow-induced noise, and analyze its generation mechanism.

[0010] In step 1, the installation distance between the microphones With pipe diameter , Noise frequency Speed ​​of sound The relationship should satisfy: Formula 1.

[0011] In step nine, the wall pressure pulsation mutual interference coefficient is defined as Formula 2; In the formula, is the reference point position, is the reference point spacing, represents the cross-power spectrum between two points, which is defined as: Formula 3; In the formula, is the cross-correlation function of the pressure pulsation between two points.

[0012] In step 10, the method for implementing spectral eigenorthogonal decomposition includes the following steps: Step S10: Given a set of transient field sequences, first subtract the time-averaged value from each transient field , and get the pulsating field ( = 1, 2, …, ), refactored to: Formula 4; Step S20: The energy of the transient field collected at each moment can be expressed as the inner product of the available space: Formula 5; in, is a positive definite Hermitian matrix representing the weight of each component, Ω is the decomposed spatial domain, represents the complex conjugate; Step S30: Decompose the instantaneous energy and seek the mode with the optimal inner product in both time and space: Formula 6; Step S40: Divide the transient field set into There are some overlapping measurement series in each segment. An instantaneous field, Formula 7; Step S50: Perform a time-domain weighted discrete Fourier transform on each sequence , get the data matrix after Fourier transformation: Formula 8; in, Representative The frequency at Fourier realization, in The matrix of all Fourier realizations at frequencies is: Formula 9; Step S60: Modality And the corresponding energy value Through the CSD matrix The eigenvectors and eigenvalues ​​are obtained; Formula 10; Step S70: Obtain the coefficients of the SPOD mode in Fourier transform , which is a column matrix , corresponding to The SPOD mode at the frequency is: Formula 11; in, is the SPOD modal energy arranged from high to low, that is ,and This is the modal characteristic of the noise.

[0013] In step 11, a two-dimensional Fourier transform is performed on the pressure field reconstructed after modal decomposition to construct an empirical frequency-wavenumber spectrum, which can be expressed as Formula 12; in, represents the angular frequency, and are the window functions in the space domain and time domain respectively; the calculation formula of the wavenumber-frequency spectrum is: Formula 13.

[0014] This spectrum (empirical frequency-wavenumber spectrum) decomposes the energy distribution of the fluctuation into contributions from different frequency and wavenumber combinations. After Fourier transformation in time and space, the reconstructed pressure field shows clear energy transmission ridges. The slope of these ridges represents the transmission speed of the energy component. For modes identified as fluid kinetic energy, this part of the reconstruction result will dissipate rapidly with the flow in the pipe. However, acoustic energy will propagate rapidly along the pipe and throughout the entire pipe system. This enables the identification and characterization of flow-induced noise components.

[0015] The upstream microphone array includes five microphone dynamic pressure sensors PT1, PT2, PT3, PT4, and PT5 which are equidistantly arranged upstream of the flow channel, and the downstream microphone array includes five microphone dynamic pressure sensors PT6, PT7, PT8, PT9, and PT10 which are equidistantly arranged downstream of the flow channel; In step 1, the microphone dynamic pressure sensor is installed in a pinhole-type manner, that is, a small hole with a diameter of 1 mm is drilled on the pipe wall for pipe wall pressure measurement to reduce the influence of the small hole on the pipe wall flow; a wedge-shaped cavity is set between the microphone and the small hole to fully receive the pulsating pressure information transmitted by the small hole; According to this connection method, the resonant frequency of the signal transmission cavity can be calculated according to Formula 1, which is about 5.5kHz, meeting the experimental measurement requirements: Formula A1; In step three, the standard microphone is used to calibrate the experimental microphone in turn. The calibration principle is based on the microphone interchange technology of the standard microphone and the microphone to be calibrated. The acoustic coupling cavity microphone calibration device is used. Its main body is composed of a long cylinder with an inner diameter of 85 mm. The wall is filled with sponge sound insulation material with high sound absorption performance. A speaker is installed at one end of the device, and the standard microphone and the microphone to be tested are installed at the other end. During calibration, a signal generator first emits white noise, and then a plane wave signal is emitted by a loudspeaker after being amplified by a power amplifier. The two microphones measure the signal at the same time. Based on the transfer function of the linear invariant system, the amplitude-frequency and phase-frequency characteristics of the microphone to be tested can be obtained. by and As the timing signals of the standard microphone and the microphone to be calibrated, , and are the auto-power spectra of the two microphones and the cross-power spectrum between them. The cross-power spectrum density function is a complex number, expressed as Formula A2; The transfer function between the standard microphone and the microphone to be calibrated is: Formula A3; The transfer function contains information about the relationship between amplitude and frequency and phase and frequency. The pressure signal data obtained by each microphone to be calibrated in the actual measurement is reconstructed accordingly. According to the discrete Fourier transform, the converted linear array microphone signal output is: Formula A4; in Formula A5; The reconstruction of the acquired signal is obtained by inverse discrete Fourier transform Formula A6; In this way, all 10 test microphones are calibrated to improve the measurement accuracy.

[0016] In step 8, the data collected by the microphone is the transient pressure pulsation value , which is the instantaneous value minus the mean Get, that is: Formula A7; Its Fourier transform is: Formula A8; Its unilateral autopower spectral density is defined as: Formula A9; In the formula, for The complex conjugate of In step nine, the time-domain mutual correlation coefficient of the wall pressure fluctuation is calculated, the correlation characteristics between the wall pressure pulsations are analyzed, and the correlation of the physical quantities at each position is intuitively displayed. The mutual interference function is essentially the dimensionless result of the mutual power spectrum of the pressure pulsation measured by the microphone dynamic pressure sensors PT1, PT2, PT3, PT4, PT5, PT6, PT7, PT8, PT9, and PT10 at each point on the wall. When the coherence function presents a vertical line perpendicular to the frequency axis, it indicates that the signals at each position are highly coherent, and it is measured and judged to exhibit significant flow-induced noise characteristics.

[0017] The measured element is a straight pipe without bends and rigid pipe wall, which is used for noise testing in the frequency range of 0-10kHz.

[0018] The present invention proposes a novel test system and method for measuring pipeline flow-induced noise based on high-frequency dynamic pressure signals of ten microphones, which can not only realize real-time measurement of pressure pulsation in the pipeline, but also realize decoupling analysis of flow field pulsation and sound pressure pulsation of the measured signal, thereby identifying the characteristics and generation mechanism of pipeline flow-induced noise, and providing reference significance for the measurement and analysis of pipeline flow-induced noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: Attached Figure 1 It is a flow chart of a test method for measuring pipeline flow-induced noise based on high-frequency dynamic pressure signals of ten microphones provided in an embodiment of the present invention; Attached Figure 2 It is a schematic diagram of a pipeline flow-induced noise measurement system based on ten microphone high-frequency dynamic pressure signals provided by an embodiment of the present invention; In the figure; 1. Workstation (i.e. control unit including signal acquisition module and signal processing module); 2. Silent fan; 3. Flexible hose; 4. Sound insulation pad; 5. Pitot tube; 6. Upstream microphone array (i.e. microphone dynamic pressure sensors PT1, PT2, PT3, PT4, PT5); 7. Measured element; 8. Downstream microphone array (i.e. microphone dynamic pressure sensors PT6, PT7, PT8, PT9, PT10); 9. Exponential diffuser; 10. Anechoic chamber. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] The terms "include" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. The term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0023] like Figure 1 As shown, a pipeline flow-induced noise testing method comprises the following steps; Step S1, determining the measurement positions of the high-frequency dynamic pressure signals of several microphones at the upstream and downstream flow channels of the measured element, normalizing the in-pipe pulsating pressure signals measured at these positions with the in-pipe flow velocity collected by the Pitot tube, calculating the in-pipe wall pressure power spectrum density, and exploring the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise; Step S2, calculating the time domain correlation coefficient of the wall pressure fluctuation, analyzing the correlation characteristics between the wall pressure pulsations, and intuitively displaying the correlation of the physical quantity at each position; Step S3, performing spectral intrinsic orthogonal decomposition on the pressure pulsation field, realizing time-space decoupling through time domain-frequency domain conversion, thereby optimally identifying the flow-induced noise distribution characteristics; Step S4, perform principal component analysis on the signal, extract the dominant flow-induced noise mode, perform two-dimensional Fourier transform, construct an empirical frequency-wavenumber spectrum, and decompose the energy distribution of the fluctuation into contributions of different frequency and wavenumber combinations; Step S5: Analyze the decoupled flow-induced noise characteristic results to identify the amplitude-frequency characteristics of the flow-induced noise.

[0024] The test system includes an air source system, a data acquisition system, a data processing system and an acoustic system; the air source system includes a silent fan 2, a frequency converter, a flexible hose 3, a sound insulation pad 4 and a pitot tube 5 located at the upstream flow channel; the data acquisition system includes a measured element 7 located between the upstream flow channel and the downstream flow channel, and also includes an upstream microphone array 6 and a downstream microphone array 8 for measuring a microphone high-frequency dynamic pressure signal, and each microphone dynamic pressure sensor includes a microphone pointing to the flow channel; The measured element is a replaceable pipe; The data processing system includes a workstation 1 for running a data processing algorithm; the acoustic system includes an exponential diffuser 9 and an anechoic chamber 10 which are sequentially connected at the downstream flow channel.

[0025] The method for using the test system comprises the following steps: Step 1: Calculate the shear number of waves in the pipeline according to the inner diameter of the measured element, the kinematic viscosity of the fluid medium and the frequency of the measured element, and determine the installation spacing of each microphone according to the calculation results; Step 2: Start the equipment and make the silent fan work in the suction mode; Step 3: Calibrate the microphone of the test process microphone dynamic pressure sensor within the test using a standard microphone (calibrate within the range of interest in the test); Step 4: In the range of interest, obtain the amplitude-frequency and phase-frequency characteristic curves and transfer functions, verify whether the autopower spectrum of the signal to be calibrated is consistent with the standard signal, and then reconstruct the data of the experimental signal; Step 5: If the autopower spectrum of the signal to be calibrated does not match the standard signal, repeat step 4; Step 6: Install the measured element and use a Pitot tube to collect the flow velocity information in the tube; Step 7: Obtain the pulsating pressure signal in the pipe, respectively, under the conditions that the component to be tested is not installed and the component to be tested is installed, collect the high-frequency dynamic pressure signals provided by ten high-frequency dynamic pressure sensors and calculate their decibel values; Step 8: Calculate the power spectrum density of the inner wall pressure to explore the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise; Step 9: Calculate the time domain correlation coefficient of the wall pressure fluctuations and analyze the correlation characteristics between the wall pressure pulsations; Step 10: Perform spectral intrinsic orthogonal decomposition on the pressure pulsation field, realize time-space decoupling through time domain-frequency domain conversion, and decompose to obtain the optimal flow field mode with both time orthogonality and space orthogonality; Step 11: Perform principal component analysis on the pressure pulsation field to extract the dominant flow-induced noise mode; perform two-dimensional Fourier transform on the pressure field reconstructed by the other mode decomposition to construct the empirical frequency-wavenumber spectrum; Step 12: Analyze the decoupled flow-induced noise characteristic results, identify the frequency and amplitude-frequency characteristics of the flow-induced noise, and analyze its generation mechanism.

[0026] In step 1, the installation distance between the microphones With pipe diameter , Noise frequency Speed ​​of sound The relationship should satisfy: Formula 1.

[0027] In step nine, the wall pressure pulsation mutual interference coefficient is defined as Formula 2; In the formula, is the reference point position, is the reference point spacing, represents the cross-power spectrum between two points, which is defined as: Formula 3; wherein, is the cross-correlation function of the pressure pulsation between two points.

[0028] In step ten, the implementation method of spectral proper orthogonal decomposition includes the following steps: Step S10: Given a set of transient field sequences, first subtract the time average value from each transient field to obtain the pulsating field ( = 1, 2, …, ), and reconstruct it as: Equation 4; Step S20: Represent the energy available space inner product of the transient field collected at each moment as: Equation 5; wherein, is a positive definite Hermitian matrix, representing the weight of each component, Ω is the spatial domain of decomposition, represents the complex conjugate; Step S30: Decompose the instantaneous energy and seek the mode with the optimal spatio-temporal inner product: Equation 6; Step S40: Divide the transient field set into segments of measurement sequences with a certain overlap, and each series contains instantaneous fields, that is Equation 7; Step S50: Perform a discrete Fourier transform with time domain weighting on each sequence to obtain the data matrix after Fourier transform: Equation 8; wherein, represents the th Fourier realization at the th frequency, and the matrix of all Fourier realizations at the th frequency is: Equation 9; Step S60: The mode and the corresponding energy value can be obtained through the eigenvectors and eigenvalues of the CSD matrix ; Equation 10; Step S70: Obtain the coefficients of the SPOD mode in the Fourier transform, which is a column matrix , corresponding to The SPOD mode at the frequency is: Formula 11; in, is the SPOD modal energy arranged from high to low, that is ,and This is the modal characteristic of the noise.

[0029] In step 11, a two-dimensional Fourier transform is performed on the pressure field reconstructed after modal decomposition to construct an empirical frequency-wavenumber spectrum, which can be expressed as Formula 12; in, represents the angular frequency, and are the window functions in the space domain and time domain respectively; the calculation formula of the wavenumber-frequency spectrum is: Formula 13.

[0030] This spectrum (empirical frequency-wavenumber spectrum) decomposes the energy distribution of the fluctuation into contributions from different frequency and wavenumber combinations. After Fourier transformation in time and space, the reconstructed pressure field shows clear energy transmission ridges. The slope of these ridges represents the transmission speed of the energy component. For modes identified as fluid kinetic energy, this part of the reconstruction result will dissipate rapidly with the flow in the pipe. However, acoustic energy will propagate rapidly along the pipe and throughout the entire pipe system. This enables the identification and characterization of flow-induced noise components.

[0031] The upstream microphone array includes five microphone dynamic pressure sensors PT1, PT2, PT3, PT4, and PT5 which are equidistantly arranged upstream of the flow channel, and the downstream microphone array includes five microphone dynamic pressure sensors PT6, PT7, PT8, PT9, and PT10 which are equidistantly arranged downstream of the flow channel; In step 1, the microphone dynamic pressure sensor is installed in a pinhole-type manner, that is, a small hole with a diameter of 1 mm is drilled on the pipe wall for pipe wall pressure measurement to reduce the influence of the small hole on the pipe wall flow; a wedge-shaped cavity is set between the microphone and the small hole to fully receive the pulsating pressure information transmitted by the small hole; According to this connection method, the resonant frequency of the signal transmission cavity can be calculated according to Formula 1, which is about 5.5kHz, meeting the experimental measurement requirements: Formula A1; In step three, the standard microphone is used to calibrate the experimental microphone in turn. The calibration principle is based on the microphone interchange technology of the standard microphone and the microphone to be calibrated. The acoustic coupling cavity microphone calibration device is used. Its main body is composed of a long cylinder with an inner diameter of 85 mm. The wall is filled with sponge sound insulation material with high sound absorption performance. A speaker is installed at one end of the device, and the standard microphone and the microphone to be tested are installed at the other end. During calibration, a signal generator first emits white noise, and then a plane wave signal is emitted by a loudspeaker after being amplified by a power amplifier. The two microphones measure the signal at the same time. Based on the transfer function of the linear invariant system, the amplitude-frequency and phase-frequency characteristics of the microphone to be tested can be obtained. by and As the timing signals of the standard microphone and the microphone to be calibrated, , and are the auto-power spectra of the two microphones and the cross-power spectrum between them. The cross-power spectrum density function is a complex number, expressed as Formula A2; The transfer function between the standard microphone and the microphone to be calibrated is: Formula A3; The transfer function contains information about the relationship between amplitude and frequency and phase and frequency. The pressure signal data obtained by each microphone to be calibrated in the actual measurement is reconstructed accordingly. According to the discrete Fourier transform, the converted linear array microphone signal output is: Formula A4; in Formula A5; The reconstruction of the acquired signal is obtained by inverse discrete Fourier transform Formula A6; In this way, all 10 test microphones are calibrated to improve the measurement accuracy.

[0032] In step 8, the data collected by the microphone is the transient pressure pulsation value , which is the instantaneous value minus the mean Get, that is: Formula A7; Its Fourier transform is: Formula A8; Its unilateral autopower spectral density is defined as: Formula A9; In the formula, for The complex conjugate of In step nine, the time-domain mutual correlation coefficient of the wall pressure fluctuation is calculated, the correlation characteristics between the wall pressure pulsations are analyzed, and the correlation of the physical quantities at each position is intuitively displayed. The mutual interference function is essentially the dimensionless result of the mutual power spectrum of the pressure pulsation measured by the microphone dynamic pressure sensors PT1, PT2, PT3, PT4, PT5, PT6, PT7, PT8, PT9, and PT10 at each point on the wall. When the coherence function presents a vertical line perpendicular to the frequency axis, it indicates that the signals at each position are highly coherent, and it is measured and judged to exhibit significant flow-induced noise characteristics.

[0033] The measured element is a straight pipe without bends and a rigid pipe wall, and is used for noise testing in a frequency range of 0-10kHz.

[0034] Embodiment 1: As shown in the figure, a novel pipeline flow-induced noise test system and method, the test system is driven by a silent fan, and the high-frequency dynamic pressure signals collected by ten dynamic pressure sensors at the pipeline are decoupled to analyze the flow field pulsation and flow-induced noise generated by the measured element. The characteristics are as follows: the ten high-frequency dynamic pressure sensors include five PT1, PT2, PT3, PT4, PT5 arranged equidistantly upstream (from the measured element to the silent fan), and five PT6, PT7, PT8, PT9, PT10 arranged equidistantly downstream (from the exponential diffuser to the measured element); the silent fan works in the suction mode to reduce the influence of background noise; The calculation process of measuring the flow-induced noise of the measured element includes the following steps: Step 1: Calculate the shear number of waves in the pipeline according to the inner diameter of the pipeline, the kinematic viscosity of the fluid medium and the frequency of the measured element, and determine the installation position and spacing of each microphone according to the calculation results; Step 2: Start the equipment, and the silent fan works in suction mode to reduce the impact of background noise; Step 3: Calibrate the experimental microphone. Use a standard microphone to calibrate the experimental microphone within the range of interest.

[0035] Step 4: In the range of interest, the corresponding amplitude-frequency and phase-frequency characteristic curves are obtained, and the amplitude-frequency and phase-frequency transfer functions are obtained to verify whether the power spectrum of the signal to be calibrated is consistent with the standard signal. Then, the data of the signal collected in the experiment is reconstructed, and the reconstructed data is basically consistent with the characteristics of the real pressure pulsation signal.

[0036] Step 5: If the autopower spectrum of the signal to be calibrated does not match the standard signal, repeat step 4 and recalibrate the microphone.

[0037] Step 6: Install the measured element and use a Pitot tube to collect the flow rate information in the tube. The Pitot tube is arranged in the air source section for flow rate measurement. The distance between the Pitot tube and the test section is long enough, and the flow rate is recorded in real time by an air flow collector to ensure that the Reynolds number in the tube under different measured elements is maintained at the target working condition.

[0038] Step 7: Obtain the pulsating pressure signal in the pipe. Collect the high-frequency dynamic pressure signals provided by ten high-frequency dynamic pressure sensors: PT1, PT2, PT3, PT4, PT5, and PT6, PT7, PT8, PT9, PT10, and calculate their decibel values, respectively, without or with the DUT installed. Ensure that after the DUT is installed, the decibel value of the dynamic pressure signal collected by the microphone is more than 10 dB higher than that without the DUT installed.

[0039] Step 8: Based on the high-frequency dynamic pressure signal provided by the dynamic pressure sensor in step 7, calculate the power spectrum density of the inner wall pressure, and explore the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise.

[0040] Step 9: Calculate the time domain correlation coefficient of the wall pressure fluctuation, analyze the correlation characteristics between the wall pressure pulsations, and intuitively display the correlation of the physical quantity at each position. The mutual interference function is essentially the dimensionless result of the cross-power spectrum of the pressure pulsation at each point on the wall (PT1, PT2, PT3, PT4, PT5, PT6, PT7, PT8, PT9, PT10). When the coherence function presents a vertical line perpendicular to the frequency axis, it indicates that the signals at each position are highly coherent, and it can be considered to show significant flow-induced noise characteristics.

[0041] Step 10: Perform spectral intrinsic orthogonal decomposition of the pressure pulsation field, achieve time-space decoupling through time-frequency domain conversion, and decompose to obtain the optimal flow field mode with both time orthogonality and space orthogonality (i.e., space-frequency). At each frequency, spectral intrinsic orthogonal decomposition obtains a set of energy-ordered orthogonal modes, thereby optimally identifying the flow-induced noise distribution characteristics.

[0042] Step 11: Perform principal component analysis on the pressure pulsation field to extract the dominant flow-induced noise mode. By performing a two-dimensional Fourier transform on the pressure field reconstructed by the other mode decomposition, an empirical frequency-wavenumber spectrum is constructed, which decomposes the energy distribution of the fluctuation into contributions from different frequency and wavenumber combinations. After Fourier transform in time and space, the reconstructed pressure field shows clear energy transmission ridges. The slopes of these ridges represent the transmission speed of the energy components. For modes identified as fluid kinetic energy, this part of the reconstruction results will dissipate rapidly with the flow in the pipeline. The acoustic energy of flow-induced noise will propagate rapidly along the pipeline and throughout the entire pipeline system. This achieves feature recognition of noise.

[0043] Step 12: Analyze the decoupled flow-induced noise characteristic results, identify the frequency and amplitude-frequency characteristics of the flow-induced noise, and analyze its generation mechanism.

[0044] In Step 1, the microphone is installed on the pipeline wall for wall pulsation pressure measurement. The traditional microphone installation method is the flush-mounted method, which measures with the pressure measuring diaphragm of the microphone parallel to the inner surface of the pipe wall. Due to the large size of the microphone, this method will undoubtedly affect the spatial resolution of the signal and the effective frequency of the measurement. In addition, this installation method is difficult to align, and there is a hidden danger of affecting the flow and noise characteristics due to the too large hole drilled in the pipeline wall. To avoid the above problems, the pin-hole installation method is adopted, that is, a small hole with a diameter of 1 mm is drilled in the pipe wall for pipe wall pressure measurement to reduce the influence of the small hole on the pipe wall flow. There is a wedge-shaped cavity between the microphone and the small hole to fully receive the pulsation pressure information transmitted by the small hole. According to this connection method, the resonance frequency of the signal transmission cavity can be calculated by Formula 1, about 5.5 kHz, which meets the experimental measurement requirements: In Step 3, the experimental microphone is calibrated sequentially using a standard microphone. The calibration principle is based on the microphone interchange technology between the standard microphone and the microphone to be calibrated. A sound coupling cavity microphone calibration device is used, and its main body consists of a long cylinder with an inner diameter of 85 mm. The inner wall is filled with sponge sound insulation material with high sound absorption performance. One end of the device is equipped with a speaker, and the standard microphone and the microphone to be measured are installed at the other end. During calibration, the signal generator emits white noise, which is amplified by the power amplifier and then emits a plane wave signal by the speaker. The two microphones measure this signal at the same time. According to the transfer function of the linear invariant system, the amplitude-frequency and phase-frequency characteristics of the microphone to be calibrated can be obtained. Taking and y as the time series signals of the standard microphone and the microphone to be calibrated respectively, and taking , and as the auto-power spectra of the two microphones and the cross-power spectrum between them respectively. Among them, the cross-power spectral density function is a complex number and can be expressed as The transfer function between the standard microphone and the microphone to be calibrated is This transfer function contains the information of both the amplitude-frequency and phase-frequency relationships at the same time. For the pressure signal data obtained by each microphone to be calibrated in actual measurement, it can be reconstructed accordingly. According to the discrete Fourier transform, the converted linear array microphone signal output is where The reconstruction of the acquired signal can be obtained by inverse discrete Fourier transform In this way, all 10 test microphones are calibrated to improve the measurement accuracy.

[0045] In step 8, the data collected by the microphone is the transient pressure pulsation value , which is the instantaneous value minus the mean Get, that is: Its Fourier transform is: Its unilateral autopower spectral density is defined as: In the formula, for The complex conjugate of .

[0046] In step nine, the wall pressure pulsation mutual interference coefficient is defined as: In the formula, is the reference point position, is the reference point spacing, represents the cross-power spectrum between two points, which is defined as: In the formula, is the cross-correlation function of the pressure pulsation between two points.

[0047] In step 10, the steps for implementing spectral eigenorthogonal decomposition are as follows: S10: Given a set of transient field sequences, first subtract the time-averaged value from each transient field , and get the pulsating field ( = 1, 2, …, ), refactored to: S20: The energy of the transient field collected at each moment can be expressed as the inner product of the available space: in, is a positive definite Hermitian matrix representing the weight of each component, Ω is the decomposed spatial domain, represents the complex conjugate.

[0048] S30: Decompose the instantaneous energy and seek the mode with the optimal inner product in both time and space: The eigenvalue problem is solved in the frequency domain, which can be achieved using the two-point space-time correlation matrix of the Fourier transform.

[0049] S40: Divide the transient field set into There are some overlapping measurement series in each segment. An instantaneous field, Under the ergodic hypothesis, each sequence can be regarded as an independent representation of the noise field. The purpose of data segmentation is to increase the number of data sets.

[0050] S50: Time-domain weighted discrete Fourier transform of each sequence , get the data matrix after Fourier transformation: in, Representative The frequency at Fourier realization, in The matrix of all Fourier realizations at frequencies is: S60: Modal And the corresponding energy value Through the CSD matrix The eigenvectors and eigenvalues ​​are obtained.

[0051] S70: Obtain the coefficients of the SPOD mode in the Fourier transform , which is a column matrix , corresponding to The SPOD mode at the frequency is: in, is the SPOD modal energy arranged from high to low, that is ,and This is the modal characteristic of the noise.

[0052] In step 11, an empirical frequency-wavenumber spectrum is constructed by performing a two-dimensional Fourier transform on the pressure field reconstructed by modal decomposition. in, represents the angular frequency, and are the window functions in the spatial domain and the time domain respectively. Then, the wavenumber-frequency spectrum is calculated as: The spectrum decomposes the energy distribution of the fluctuation into contributions from different frequency and wave number combinations. After Fourier transformation in time and space, the reconstructed pressure field shows clear energy transmission ridges. The slope of these ridges represents the transmission speed of the energy component. For modes identified as fluid kinetic energy, this part of the reconstruction result will dissipate rapidly with the flow in the pipe. However, acoustic energy will propagate rapidly along the pipe and throughout the entire pipe system. This enables the identification and characterization of flow-induced noise components.

[0053] Embodiment 2: like Figure 2 As shown, in this example: The silent fan 2 provides a stable airflow for the pipeline. The noise and vibration of the fan are extremely low, and the impact on the experiment can be almost ignored. At the same time, the speed of the fan can be controlled by a frequency converter, so as to control the airflow conditions in the test section to obtain a specified flow rate. The flexible hose 3 isolates the weak vibrations generated by the fan that may still exist; the sound insulation pad 4 further eliminates the background noise caused by the fan and the environment; the Pitot tube 5 measures the flow velocity in the pipeline and is installed in the gas source section. The distance between the Pitot tube and the test section is arranged long enough to ensure that the flow is fully developed in the test section; the measured element 7 can be replaced to measure the pipeline flow noise caused by different elements; the exponential diffuser 9 eliminates standing waves in the working section and minimizes the reflection of sound waves at both ends of the pipeline; the anechoic chamber 10 reduces background noise and prevents noise reflection from generating unnecessary standing waves and noise.

[0054] The microphone dynamic pressure sensors PT1, PT2, PT3, PT4, PT5 at the upstream of the air duct and the microphone dynamic pressure sensors PT6, PT7, PT8, PT9, PT10 at the downstream of the air duct measure the flow-induced noise generated by the measured element in the pipeline.

[0055] The pulsating pressure signal in the pipe obtained in step 7 is normalized by the flow velocity in the pipe collected by the pitot tube in step 6; then, according to the high-frequency dynamic pressure signal provided by the dynamic pressure sensor in step 7, the power spectrum density of the wall pressure in the pipe is calculated to explore the dominant characteristics of the flow structure, wall pressure pulsation and far-field noise; then, according to step 9, the time-domain mutual correlation coefficient of the wall pressure fluctuation is calculated to analyze the correlation characteristics between the wall pressure pulsation, and intuitively display the correlation of physical quantities at various positions; according to step 10, the pressure pulsation field is subjected to spectral intrinsic orthogonal decomposition, and time-space decoupling is achieved through time-frequency domain conversion, and the optimal flow field mode with both time orthogonality and space orthogonality is decomposed. At each frequency, the spectral intrinsic orthogonal decomposition obtains a set of energy-ordered orthogonal modes, thereby optimally identifying the flow-induced noise distribution characteristics; according to step 11, the pressure pulsation field is subjected to principal component analysis to extract the dominant flow-induced noise mode. By performing a two-dimensional Fourier transform on the pressure field reconstructed by the other mode decomposition, an empirical frequency-wavenumber spectrum is constructed, which decomposes the energy distribution of the fluctuation into the contributions of different frequency and wavenumber combinations. Finally, according to step 12, the characteristic results of the decoupled flow-induced noise are analyzed to identify the frequency and amplitude-frequency characteristics of the flow-induced noise and analyze its generation mechanism.

[0056] The above is only a preferred embodiment of the present invention, which is only used to illustrate the method of the present invention rather than to limit it. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A pipeline flow-induced noise testing method, characterized in that: The steps include: Step S1, determining the measurement positions of the high-frequency dynamic pressure signals of several microphones, normalizing the measured pulsating pressure signals in the pipe, and calculating the power spectrum density of the pressure on the inner wall of the pipe; Step S2, calculating the time domain correlation coefficient of the wall pressure fluctuations, and analyzing the correlation characteristics between the wall pressure pulsations; Step S3, performing spectral intrinsic orthogonal decomposition on the pressure pulsation field, and realizing time-space decoupling through time domain-frequency domain conversion; Step S4, extracting the dominant flow-induced noise mode, constructing an empirical frequency-wavenumber spectrum, and decomposing the energy distribution of the fluctuation into contributions of different frequency and wavenumber combinations; Step S5: Analyze the decoupled flow-induced noise characteristic results to identify the amplitude-frequency characteristics of the flow-induced noise.

2. A pipeline flow-induced noise testing system, which performs testing based on the pipeline flow-induced noise testing method according to claim 1, characterized in that: The test system comprises an air source system, a data acquisition system, a data processing system and an acoustic system; the air source system comprises a silent fan (2), a frequency converter, a flexible hose (3), a sound insulation pad (4) and a pitot tube (5) located at the upstream flow channel; the data acquisition system comprises a measured element (7) located between the upstream flow channel and the downstream flow channel, and also comprises an upstream microphone array (6) and a downstream microphone array (8) for measuring a microphone high-frequency dynamic pressure signal, and each microphone dynamic pressure sensor comprises a microphone pointing toward the flow channel; The measured element is a replaceable pipe; The data processing system comprises a workstation (1) for running a data processing algorithm; and the acoustic system comprises an exponential diffuser (9) and an anechoic chamber (10) which are sequentially connected at a downstream flow channel.

3. A pipeline flow-induced noise testing system according to claim 2, characterized in that: The method for using the test system comprises the following steps: Step 1: Calculate the shear number of waves in the pipeline according to the inner diameter of the measured element, the kinematic viscosity of the fluid medium and the frequency of the measured element, and determine the installation spacing of each microphone according to the calculation results; Step 2: Start the equipment and make the silent fan work in the suction mode; Step 3: Calibrate the microphone of the test process microphone dynamic pressure sensor in the test using a standard microphone; Step 4: In the range of interest, obtain the amplitude-frequency and phase-frequency characteristic curves and transfer functions, verify whether the autopower spectrum of the signal to be calibrated is consistent with the standard signal, and then reconstruct the data of the experimental signal; Step 5: If the autopower spectrum of the signal to be calibrated does not match the standard signal, repeat step 4; Step 6: Install the measured element and use a Pitot tube to collect the flow velocity information in the tube; Step 7: Obtain the pulsating pressure signal in the pipe, respectively, under the condition that the component to be tested is not installed and the component to be tested is installed, collect the high-frequency dynamic pressure signals provided by several high-frequency dynamic pressure sensors and calculate their decibel values; Step 8: Calculate the power spectrum density of the inner wall pressure; Step 9: Calculate the time domain correlation coefficient of the wall pressure fluctuations and analyze the correlation characteristics between the wall pressure pulsations; Step 10: Perform spectral intrinsic orthogonal decomposition on the pressure pulsation field, realize time-space decoupling through time domain-frequency domain conversion, and decompose to obtain the optimal flow field mode with both time orthogonality and space orthogonality; Step 11: Perform principal component analysis on the pressure pulsation field to extract the dominant flow-induced noise mode; The empirical frequency-wavenumber spectrum is constructed by performing a two-dimensional Fourier transform on the pressure field reconstructed by the other mode decomposition; Step 12: Analyze the decoupled flow-induced noise characteristic results to identify the frequency and amplitude-frequency characteristics of the flow-induced noise.

4. A pipeline flow-induced noise testing system according to claim 3, characterized in that: In step 1, the installation distance between the microphones With pipe diameter , Noise frequency Speed ​​of sound The relationship should satisfy: Formula 1.

5. A pipeline flow-induced noise testing system according to claim 3, characterized in that: In step nine, the wall pressure pulsation mutual interference coefficient is defined as Formula 2; In the formula, is the reference point position, is the reference point spacing, represents the cross-power spectrum between two points, which is defined as: Formula 3: In the formula, is the cross-correlation function of the pressure pulsation between two points.

6. A pipeline flow-induced noise testing system according to claim 3, characterized in that: In step 10, the method for implementing spectral eigenorthogonal decomposition includes the following steps: Step S10: Given a set of transient field sequences, first subtract the time-averaged value from each transient field , and get the pulsating field ( = 1, 2, …, ), refactored to: Formula 4: Step S20: The energy of the transient field collected at each moment can be expressed as the inner product of the available space: Formula 5: in, is a positive definite Hermitian matrix representing the weight of each component, Ω is the decomposed spatial domain, represents the complex conjugate; Step S30: Decompose the instantaneous energy and seek the mode with the optimal inner product in both time and space: Formula 6: Step S40: Divide the transient field set into There are some overlapping measurement series in each segment. An instantaneous field, Formula 7: Step S50: Perform a time-domain weighted discrete Fourier transform on each sequence , get the data matrix after Fourier transformation: Formula 8: in, Representative The frequency at Fourier realization, in The matrix of all Fourier realizations at frequencies is: Formula 9: Step S60: Modality And the corresponding energy value Through the CSD matrix The eigenvectors and eigenvalues ​​are obtained; Formula 10: Step S70: Obtain the coefficients of the SPOD mode in Fourier transform , which is a column matrix , corresponding to The SPOD mode at the frequency is: Formula 11: in, is the SPOD modal energy arranged from high to low, that is ,and This is the modal characteristic of the noise.

7. A pipeline flow-induced noise testing system according to claim 3, characterized in that: In step 11, a two-dimensional Fourier transform is performed on the pressure field reconstructed after modal decomposition to construct an empirical frequency-wavenumber spectrum, which can be expressed as Formula 12: in, represents the angular frequency, and are the window functions in the space domain and time domain respectively; the calculation formula of the wavenumber-frequency spectrum is: Formula 13.

8. A pipeline flow-induced noise testing system according to claim 3, characterized in that: The upstream microphone array includes five microphone dynamic pressure sensors PT1, PT2, PT3, PT4, and PT5 which are equidistantly arranged upstream of the flow channel, and the downstream microphone array includes five microphone dynamic pressure sensors PT6, PT7, PT8, PT9, and PT10 which are equidistantly arranged downstream of the flow channel; In step 1, the microphone dynamic pressure sensor is installed in a pinhole-type manner, that is, a small hole with a diameter of 1 mm is drilled on the pipe wall for pipe wall pressure measurement to reduce the influence of the small hole on the pipe wall flow; a wedge-shaped cavity is set between the microphone and the small hole to fully receive the pulsating pressure information transmitted by the small hole; According to the microphone array connection method, the resonant frequency of the signal transmission cavity is calculated according to Formula 1, which is about 5.5kHz, meeting the experimental measurement requirements: Formula A1; In step three, the standard microphone is used to calibrate the experimental microphone in turn. The calibration principle is based on the microphone interchange technology of the standard microphone and the microphone to be calibrated. The acoustic coupling cavity microphone calibration device is used. Its main body is composed of a long cylinder with an inner diameter of 85 mm. The wall is filled with sponge sound insulation material with high sound absorption performance. A speaker is installed at one end of the device, and the standard microphone and the microphone to be tested are installed at the other end. During calibration, a signal generator first emits white noise, and then a plane wave signal is emitted by a loudspeaker after being amplified by a power amplifier. The two microphones measure the signal at the same time. Based on the transfer function of the linear invariant system, the amplitude-frequency and phase-frequency characteristics of the microphone to be tested can be obtained. by and As the timing signals of the standard microphone and the microphone to be calibrated, , and As the auto-power spectrum of the two microphones and the cross-power spectrum between them; Among them, the cross power spectral density function is a complex number, expressed as Formula A2; The transfer function between the standard microphone and the microphone to be calibrated is: Official A3; The transfer function contains information about the relationship between amplitude and frequency and phase and frequency. The pressure signal data obtained by each microphone to be calibrated in the actual measurement is reconstructed accordingly. According to the discrete Fourier transform, the converted linear array microphone signal output is: Official A4; in Formula A5; The reconstruction of the acquired signal is obtained by inverse discrete Fourier transform Formula A6; In this way, all 10 test microphones are calibrated to improve the measurement accuracy.

9. A pipeline flow-induced noise testing system according to claim 8, characterized in that: In step 8, the data collected by the microphone is the transient pressure pulsation value , which is the instantaneous value minus the mean Get, that is: Formula A7; Its Fourier transform is: Formula A8; Its unilateral autopower spectral density is defined as: Formula A9; In the formula, for The complex conjugate of In step nine, the time-domain mutual correlation coefficient of the wall pressure fluctuation is calculated, the correlation characteristics between the wall pressure pulsations are analyzed, and the correlation of the physical quantities at each position is intuitively displayed. The mutual interference function is essentially the dimensionless result of the mutual power spectrum of the pressure pulsation measured by the microphone dynamic pressure sensors PT1, PT2, PT3, PT4, PT5, PT6, PT7, PT8, PT9, and PT10 at each point on the wall. When the coherence function presents a vertical line perpendicular to the frequency axis, it indicates that the signals at each position are highly coherent, and it is measured and judged to exhibit significant flow-induced noise characteristics.

10. A pipeline flow-induced noise testing system according to claim 8, characterized in that: The measured element is a straight pipe without bends and a rigid pipe wall, and is used for noise testing in a frequency range of 0-10kHz.

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