Method and system for testing performance parameters of hoses for transferring petroleum

Abstract

The present invention relates to the technical field of hose performance testing, and in particular to a method and system for testing the performance parameters of a hose for transferring petroleum, the method comprising: collecting corrosion characteristic data through a flexible electrochemical sensor membrane; simulating the crack propagation path and rate using a dynamic physical field model and crack detection data; identifying leakage points and evaluating leakage characteristics through particle motion analysis; integrating corrosion, crack, fluid anomaly and leakage data in combination with a multimodal collaborative evaluation model, and comprehensively analyzing the hose's pressure resistance, sealing performance and corrosion resistance; and dynamically calculating the hose's remaining service life based on a remaining life prediction algorithm. The present invention effectively solves the problems of insufficient crack detection sensitivity, low corrosion modeling accuracy and incomplete comprehensive performance evaluation in the prior art, significantly improves the accuracy and reliability of performance parameter testing of oil transfer hoses, and provides a scientific basis for hose operation safety.

Description

Technical Field

[0001] The invention relates to the technical field of hose performance testing, and in particular to a method and system for testing performance parameters of a hose for transferring petroleum. Background Art

[0002] Testing the performance parameters of oil transfer hoses is a key step to ensure that the hoses remain safe and reliable in long-term use. As an important component for transporting oil, the hoses are subjected to environmental influences such as high temperature, high pressure, and complex fluids for a long time, and may suffer from corrosion, cracks, and leakage. In response to these problems, accurately and efficiently evaluating the hoses' pressure resistance, sealing performance, and corrosion resistance, and timely discovering potential defects are important links in ensuring the safe operation of transfer pipelines.

[0003] The existing technology (Chinese invention patent, publication number: CN117848864B, title: A method and system for testing the performance parameters of gas hose fittings) mainly uses ultrasonic, infrared imaging and laser modeling methods to test the pressure bearing capacity, sealing and acid-base corrosion loss of the hose. This method has the following defects:

[0004] When the crack is small, the difference in the ultrasonic penetration image is difficult to observe directly, resulting in the risk of misjudgment of the test results; when using infrared images to detect air tightness, when the leakage is extremely small, the hot air generated by the leakage cannot be effectively captured, making it difficult to accurately identify sealing problems; the laser modeling method is affected by the measurement point density and equipment accuracy, and may miss small-scale corrosion losses, resulting in test results that are inconsistent with actual conditions. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a method and system for testing the performance parameters of a hose for transferring oil. The present invention collects the corrosion, crack and leakage related data of the hose through a flexible sensor, combines the dynamic physical field model and the multi-modal collaborative evaluation model, comprehensively analyzes the pressure resistance, sealing performance and corrosion resistance of the hose, and calculates the remaining life of the hose based on the life prediction algorithm. The present invention realizes a comprehensive evaluation of the hose performance and a dynamic early warning of the failure risk, significantly improving the safety and operational reliability of the oil transfer hose.

[0006] A method for testing performance parameters of a hose for transferring petroleum, comprising the following steps:

[0007] The external environment data and internal physical property data of the hose are collected through sensors, and the collected data are processed to generate compensated environmental data, which are input into a multi-physics field modeling system to construct a dynamic physical field model of the hose;

[0008] Detecting the optical signal changes of the cracks inside the hose through the sensor, extracting the geometric characteristic data of the cracks; simulating the crack propagation path and rate based on the geometric characteristic data and the dynamic physical field model, and generating crack propagation data; predicting the impact of the cracks on the behavior of the fluid inside the hose in combination with the crack propagation data, and generating fluid abnormality data;

[0009] Add particles with response characteristics inside the hose, stimulate the movement of the particles through an external device, and collect the movement behavior data of the particles; locate the leakage point of the hose and generate leakage characteristic data by analyzing the abnormal characteristics in the movement behavior of the particles; establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the impact of the leakage on the fluid distribution;

[0010] The corrosion behavior of the hose is monitored by a sensor device to generate corrosion characteristic data; the corrosion characteristic data is combined with the dynamic physical field model to simulate the corrosion expansion of the hose; the corrosion expansion data, fluid anomaly data and leakage characteristic data are combined to evaluate the comprehensive performance of the hose and predict its remaining life.

[0011] Preferably, the collecting of external environmental data and internal physical property data of the hose by sensors includes collecting external pressure, external temperature and external stress data by a flexible MEMS sensor array arranged on the outer wall of the hose, and collecting internal pressure distribution and internal deformation characteristic data by a micro-vibration acoustic wave sensor arranged inside the hose.

[0012] Preferably, the collected data is processed to generate compensated environmental data, including using a noise suppression algorithm to remove noise from external pressure data, external temperature data and external stress data, and dynamically correcting internal pressure distribution data and internal deformation characteristic data; and further using a nonlinear compensation algorithm to correct the measurement deviation of the sensor to generate corrected and compensated external environmental data and internal physical characteristic data.

[0013] Preferably, the optical signal changes of cracks inside the hose are detected by sensors, including using a quantum fiber optic sensor arranged on the inner wall of the hose to collect light wave interference intensity changes and light wave phase shift characteristic data caused by the cracks in real time, and inputting the light wave interference intensity change data and light wave phase shift characteristic data into a crack characteristic extraction algorithm to calculate the crack depth data, crack length data and crack spatial position data.

[0014] Preferably, based on the geometric characteristic data and the dynamic physical field model, the crack propagation path and rate are simulated, including using a nonlinear fracture mechanics analysis algorithm combined with a dynamic physical field model to calculate the crack propagation path and propagation rate under different internal pressures, external stresses and oil flow pressures, and generating crack propagation data.

[0015] Preferably, the adding of particles with response characteristics inside the hose and collecting the movement behavior data of the particles includes uniformly introducing magnetic nanoparticles into the petroleum, wherein the particle size of the magnetic nanoparticles ranges from 10 nanometers to 50 nanometers, generating an alternating electromagnetic field through a ring-shaped electromagnetic detector arranged outside the hose to stimulate the movement of the magnetic nanoparticles, and recording the movement trajectory data and movement speed data of the magnetic nanoparticles in real time.

[0016] Preferably, the method of locating the leakage point of the hose by analyzing the abnormal characteristics in the particle movement behavior includes using a dynamic clustering algorithm to perform cluster analysis on the flow velocity drop areas appearing in the movement trajectory data and movement velocity data of the magnetic nanoparticles, and combining the abnormal distribution characteristics of the particle movement trajectory to determine the specific location and leakage rate of the leakage point.

[0017] Preferably, the monitoring of the corrosion behavior of the hose by means of a sensor device includes collecting corrosion product concentration data and electrochemical potential change data using a flexible electrochemical sensor membrane arranged on the inner and outer walls of the hose, and combining the corrosion product concentration data and electrochemical potential change data to extract corrosion rate data, maximum corrosion depth data and corrosion area data.

[0018] Preferably, the comprehensive performance evaluation of the hose includes inputting the corrosion rate data, maximum corrosion depth data and corrosion area data together with the crack extension data, fluid anomaly data and leakage characteristic data into a multimodal collaborative evaluation model, comprehensively analyzing the pressure resistance, sealing performance and corrosion resistance of the hose, and calculating the remaining service life of the hose based on a remaining life prediction algorithm.

[0019] A system for testing performance parameters of a hose for transferring oil, used for implementing the method for testing performance parameters of a hose for transferring oil, comprising:

[0020] A sensing module, used to collect external environment data and internal physical property data of the hose, the sensing module includes a flexible MEMS sensor arranged on the outer wall of the hose, used to collect external pressure, external temperature and external stress data, and a micro-vibration acoustic wave sensor arranged inside the hose, used to collect internal pressure distribution and internal deformation characteristic data;

[0021] A data processing module, used for performing noise removal and nonlinear compensation processing on the data collected by the sensor module to generate compensated environmental data;

[0022] A modeling module, used for receiving the compensated environmental data and constructing a dynamic physical field model of the hose;

[0023] A crack detection module, used to detect geometric characteristic data of cracks inside the hose through changes in light wave interference intensity and light wave phase shift, and simulate crack propagation path and rate based on the geometric characteristic data and a dynamic physical field model to generate crack propagation data;

[0024] A fluid anomaly analysis module, used to predict the impact of the crack on the behavior of the fluid inside the hose based on the crack propagation data, and generate fluid anomaly data;

[0025] A particle excitation and monitoring module is used to introduce magnetic nanoparticles with response characteristics into the hose, excite the magnetic nanoparticles to move through an external annular electromagnetic detector and collect movement behavior data;

[0026] A leakage analysis module, used to locate the leakage point of the hose by analyzing the abnormal characteristics in the movement behavior of the magnetic nanoparticles, generate leakage characteristic data, and establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the dynamic impact of the leakage on the fluid distribution;

[0027] A corrosion monitoring module, for monitoring the corrosion behavior of the hose through a flexible electrochemical sensor membrane, generating corrosion characteristic data, and combining the corrosion characteristic data with a dynamic physical field model to simulate corrosion expansion;

[0028] The comprehensive performance evaluation module is used to evaluate the comprehensive performance of the hose by combining corrosion extension data, fluid anomaly data and leakage characteristic data, and predict the remaining service life of the hose based on the remaining service life prediction algorithm.

[0029] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0030] The present invention realizes accurate simulation of crack propagation path and rate through a dynamic physical field coupling model, effectively overcoming the problem of insufficient crack detection sensitivity in the prior art;

[0031] The present invention realizes high-precision extraction of corrosion rate, corrosion depth and corrosion area through real-time monitoring of flexible electrochemical sensor membrane, greatly improving the comprehensiveness of corrosion behavior assessment;

[0032] The present invention realizes the comprehensive analysis of pressure resistance, sealing performance and corrosion resistance through a multi-modal collaborative evaluation model, which significantly improves the scientificity and accuracy of the comprehensive performance evaluation;

[0033] The present invention realizes dynamic prediction of the service life of the hose through a remaining service life prediction algorithm, thus providing a reliable basis for operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention;

[0035] Figure 2 It is a schematic diagram of the model analysis and evaluation process in the present invention;

[0036] Figure 3 This is a schematic diagram of the life prediction process in the present invention;

[0037] Figure 4 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0038] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure.

[0039] like Figure 1 As shown, a method for testing performance parameters of a hose for transferring petroleum includes the following steps:

[0040] The external environment data and internal physical property data of the hose are collected through sensors, and the collected data are processed to generate compensated environmental data, which are input into a multi-physics field modeling system to construct a dynamic physical field model of the hose;

[0041] The flexible MEMS sensor array is evenly arranged on the outer wall of the hose to collect external pressure, external temperature and external stress data. The flexible MEMS sensor is based on the principle of micro-electromechanical systems. Its sensitive units can sense physical quantities (such as pressure and temperature) and convert them into electrical signals. The sensor arrangement spacing is 10 cm to ensure that the spatial resolution of data acquisition is high enough while covering the stress concentration areas that may exist on the hose surface.

[0042] Micro-vibration acoustic wave sensors are placed inside the hose to detect internal pressure distribution and deformation characteristics using the propagation characteristics of acoustic waves (such as velocity change and frequency attenuation). The acoustic wave sensors are spaced 5 cm apart and can capture high-resolution dynamic characteristic data.

[0043] The processed and compensated environmental data is input into the multi-physics modeling system to build a dynamic physical field model of the hose. This model integrates external environmental data and internal physical property data through finite element analysis (FEM) and boundary condition iterative calculation to generate a global dynamic model including stress distribution, deformation characteristics and pressure gradient.

[0044] Preferably, the collecting of external environmental data and internal physical property data of the hose by sensors includes collecting external pressure, external temperature and external stress data by a flexible MEMS sensor array arranged on the outer wall of the hose, and collecting internal pressure distribution and internal deformation characteristic data by a micro-vibration acoustic wave sensor arranged inside the hose.

[0045] In the present invention, the hose is subjected to complex external environment and internal physical loads, including external pressure, temperature and stress, as well as internal pressure distribution and deformation characteristics. In order to comprehensively monitor the state of the hose, a flexible MEMS sensor array and a micro-vibration acoustic wave sensor are arranged on the outer wall and inner wall of the hose, respectively, to collect external environmental data and internal physical property data. These sensors use different principles and acquisition mechanisms to achieve high-precision data recording under dynamic operating conditions.

[0046] Flexible MEMS sensors are based on micro-electromechanical systems (MEMS) technology and can convert the pressure, temperature and stress of the external environment into measurable electrical signals. Due to the flexibility of its material, the flexible sensor can fit tightly to the outer wall of the hose without affecting the normal operation of the hose, and can maintain good data stability under high-pressure environments. The flexible MEMS sensor deforms under the action of external pressure through the sensitive diaphragm, and the internal capacitance value changes accordingly, thereby calculating the external pressure value. The spacing of the arrangement is 10 cm, ensuring uniformity of spatial coverage.

[0047] Temperature acquisition: Temperature measurement is achieved through semiconductor thermistors, and the sensor's resistance response to temperature changes is converted into a measurable voltage signal. Stress acquisition: The outer wall stress is measured through the strain change of the sensitive material, which is suitable for monitoring the deformation of the hose caused by external forces.

[0048] Micro-vibration acoustic wave sensors are arranged inside the hose at a spacing of 5 cm. They use the speed change and attenuation characteristics of acoustic waves when propagating in the medium to measure the pressure distribution and deformation characteristics inside the hose in real time. The propagation speed of acoustic waves slows down and the frequency shifts in areas where pressure increases or deformation intensifies. These changes are captured by the sensor and converted into pressure distribution data and deformation characteristic data.

[0049] Pressure distribution measurement: When the sound wave propagates inside the hose, its speed is inversely proportional to the pressure of the medium. By recording the time difference of the sound wave, the local pressure distribution is calculated. Deformation characteristic measurement: In the deformation area, the amplitude and frequency of the sound wave signal will change. By analyzing these characteristics, the bending or deformation position of the hose can be captured.

[0050] All sensors are connected to the central processing unit (CPU) through the data acquisition module, and the collected data are classified according to the sensor type and recorded synchronously. The pressure, temperature and stress data collected by the external sensors and the pressure distribution and deformation data collected by the internal sensors form a complete data set for subsequent processing and analysis.

[0051] Flexible MEMS sensors and micro-vibration acoustic wave sensors can collect external environment and internal physical property data with high sensitivity, covering a wide range and high collection accuracy, providing high-quality raw data for subsequent analysis. The data acquisition module supports real-time monitoring of external environmental changes and internal load characteristics of the hose during the dynamic oil transfer process, and can capture emergencies (such as stress concentration or pressure mutation). The flexible design and tight arrangement of the sensor will not affect the normal operation of the hose, and is suitable for complex working conditions such as high pressure and high temperature, ensuring the operability and applicability of the solution.

[0052] Example 1: A 10-meter-long hose is used to transport oil at a pressure of 5 MPa, and the ambient temperature fluctuates between 10°C and 50°C. A flexible MEMS sensor array is arranged every 10 cm on the outer wall of the hose to collect external pressure, external temperature and external stress data in real time; a micro-vibration acoustic wave sensor is arranged every 5 cm on the inner wall to monitor the internal pressure distribution and deformation characteristics.

[0053] The external pressure is stable at 5 MPa, and the local outer wall stress concentration area reaches 15 MPa, which may pose a fatigue risk; the internal pressure distribution shows that the pressure in a local area rises to 7 MPa, accompanied by an increase in deformation.

[0054] Application effect: It provides a preliminary basis for judging the possible failure points of the hose outer wall and identifies potential crack areas that may be caused by abnormal internal pressure.

[0055] Example 2: In a transfer hose operating in a low-temperature environment (-20°C to 0°C), the temperature changes recorded by the flexible MEMS sensor are consistent with the external stress concentration area, and the inner wall micro-vibration acoustic wave sensor detects that the internal deformation characteristics are associated with the external environmental temperature difference. Through data analysis, it is found that the hose has a sudden stress change in the temperature difference change area.

[0056] When the external temperature is -20°C, the acoustic wave signal in the deformation area of ​​the inner wall of the hose shows high-frequency attenuation; the external stress is highly matched with the internal deformation area.

[0057] Application effect: It provides complete data support for the dynamic performance of hoses in low-temperature environments and provides reliable input for the accuracy of multi-physics field modeling systems.

[0058] Preferably, the collected data is processed to generate compensated environmental data, including using a noise suppression algorithm to remove noise from external pressure data, external temperature data and external stress data, and dynamically correcting internal pressure distribution data and internal deformation characteristic data; and further using a nonlinear compensation algorithm to correct the measurement deviation of the sensor to generate corrected and compensated external environmental data and internal physical characteristic data.

[0059] In the present invention, the collected raw data is often affected by various factors such as sensor accuracy limitations and environmental interference, and may contain noise or deviation. In order to ensure the accuracy of subsequent analysis and modeling, it is necessary to suppress noise and dynamically correct the collected external pressure data, external temperature data, external stress data, internal pressure distribution data, and internal deformation characteristic data through data processing algorithms, and perform nonlinear compensation processing on the system deviation of the sensor.

[0060] The noise in the data may come from external interference (such as mechanical vibration, ambient temperature difference, etc.), which needs to be removed by noise suppression algorithm. Wavelet denoising algorithm is used to process external pressure, external temperature and external stress data. Its core is to decompose the signal into different frequency scales, remove high-frequency noise, and retain the low-frequency part of the effective signal.

[0061] Internal pressure distribution data and internal deformation characteristic data may have measurement errors due to dynamic environments (such as oil flow, pressure fluctuations). Through the dynamic correction algorithm, the real-time measured data is compared with the historical characteristic data, the weight of the collection point is dynamically updated, and non-physical abnormal signals are eliminated. Dynamic correction is based on time-weighted mean:

[0062]

[0063] in, It is the corrected data; is the current collection data; It is historical characteristic data; is the time weighting factor.

[0064] The system deviation of the sensor is mainly manifested as measurement errors caused by changes in ambient temperature, external stress, etc. Nonlinear compensation algorithms are used to correct these deviations. Nonlinear compensation constructs a dynamic correction model by defining compensation coefficients (such as temperature coefficients and stress coefficients). The compensation formula is:

[0065]

[0066] in, is the measured value after compensation; is the original measurement; is the temperature change; is the stress change; , are the compensation coefficients for temperature and stress respectively.

[0067] The compensated external environment data and internal physical property data can be directly input into the multi-physics field modeling system to ensure the accuracy of subsequent analysis results.

[0068] Through the wavelet denoising algorithm, random high-frequency noise in external pressure, temperature and stress data can be effectively removed to ensure signal stability. The dynamic correction algorithm can continuously optimize data quality in complex dynamic environments and reduce the interference of sudden anomalies on data analysis. The nonlinear compensation algorithm effectively eliminates measurement errors caused by temperature and stress changes, providing high-precision input data for subsequent modeling and analysis. The consistency of the processed data is significantly enhanced, the logical correlation between different sensor data is clearer, and the reliability of the overall data is improved.

[0069] Example 1: In a hose for transporting oil, the external temperature data collected by the flexible MEMS sensor fluctuates between 40°C and 70°C, while the external pressure and stress data are disturbed by high-frequency mechanical vibration. Processing steps: Apply wavelet denoising algorithm to the external temperature, pressure and stress data to remove high-frequency noise caused by mechanical vibration; perform nonlinear compensation for the measurement deviation caused by temperature change, and set the temperature compensation coefficient to 0.005. After denoising, the data error is reduced to 0.2%, and the nonlinear compensation reduces the deviation of the pressure measurement value to 1%.

[0070] Example 2: During the transportation process, the internal pressure distribution fluctuates due to the unevenness of the oil flow, and the internal deformation characteristic data shows an abnormal peak. Processing steps: Apply a dynamic correction algorithm to compare the real-time collected data with the historical pressure distribution characteristics, and set the weight factor to 0.7; after correction, the abnormal peak disappears, and the deformation data and pressure data show a linear correlation. The corrected internal data can accurately reflect the actual state of the pressure distribution and deformation area.

[0071] Detecting the optical signal changes of the cracks inside the hose through the sensor, extracting the geometric characteristic data of the cracks; simulating the crack propagation path and rate based on the geometric characteristic data and the dynamic physical field model, and generating crack propagation data; predicting the impact of the cracks on the behavior of the fluid inside the hose in combination with the crack propagation data, and generating fluid abnormality data;

[0072] In the present invention, crack formation and expansion are the main causes of hose failure. The geometric characteristics of the crack (such as depth, length, and spatial position) directly affect the structural integrity of the hose, while the dynamic expansion path and rate of the crack determine the specific impact on the fluid behavior inside the hose. Therefore, crack detection based on optical signals, crack expansion modeling, and fluid anomaly prediction are the core steps to ensure hose performance evaluation.

[0073] Crack detection relies on a quantum fiber sensor placed on the inner wall of the hose, which records the geometric characteristics of the crack location through the interference and phase shift changes of light waves. When a crack forms or expands, the stress concentration in the crack area causes the deformation of the optical fiber, which in turn changes the interference intensity and phase shift characteristics of the light waves. Crack geometric characteristic extraction includes the following steps:

[0074] (1) Light wave signal acquisition: The quantum fiber sensor collects the light wave interference intensity change and phase shift characteristic data in the crack area in real time.

[0075] (2) Signal feature extraction: The signal features are analyzed by algorithms, and the depth, length, and spatial position of the crack are extracted from the signal as crack geometric characteristic data. The length and depth of the crack are calculated by fitting the light wave attenuation characteristic curve, and the spatial position is determined by the physical coordinate system of the optical fiber layout.

[0076] Based on the geometric characteristic data of the crack and the dynamic physical field model (including the stress distribution and pressure gradient of the hose), a crack growth model is constructed to simulate the crack growth path and rate under the combined action of external stress and internal pressure. The crack growth model combines the following factors:

[0077] (1) Stress intensity factor: When a crack propagates under a pressure gradient, the stress concentration at the crack tip is the key to determining the propagation rate. (2) Multi-physics field coupling: Through the dynamic physical field model, the external stress distribution is superimposed on the internal pressure gradient to form the boundary conditions for dynamic crack propagation. (3) Propagation path calculation: Using nonlinear fracture mechanics algorithms, the crack propagation path is dynamically calculated to generate crack propagation data.

[0078] Crack propagation changes the internal structure of the hose, thus affecting the pressure distribution, velocity change and turbulent behavior of the petroleum fluid. Based on the crack propagation data, predict the abnormal behavior of the fluid inside the hose: Fluid-crack coupling modeling combines crack propagation data with dynamic physical field models, and analyzes the specific impact of cracks on fluid behavior through fluid dynamics simulation. Abnormal area identification predicts the turbulent area, pressure drop area and abnormal change of velocity distribution that may be formed near the crack, and generates fluid abnormality data.

[0079] Preferably, the optical signal changes of cracks inside the hose are detected by sensors, including using a quantum fiber optic sensor arranged on the inner wall of the hose to collect light wave interference intensity changes and light wave phase shift characteristic data caused by the cracks in real time, and inputting the light wave interference intensity change data and light wave phase shift characteristic data into a crack characteristic extraction algorithm to calculate the crack depth data, crack length data and crack spatial position data.

[0080] In the performance test of the hose for transporting oil, the geometric characteristics of the crack (depth, length, spatial position) have an important impact on the structural integrity and operational safety of the hose. The quantum fiber optic sensor is based on the detection of light wave interference and phase shift changes, and can sense the dynamic changes of the cracks on the inner wall of the hose in real time at high resolution. The present invention uses a quantum fiber optic sensor arranged on the inner wall of the hose, analyzes the optical signal changes through the crack characteristic extraction algorithm, and calculates the specific geometric parameters of the crack.

[0081] Quantum fiber optic sensors detect cracks by changing the propagation characteristics of light waves in optical fibers. Cracks inside the hose will cause optical fiber deformation, which will in turn lead to attenuation of interference intensity and phase shift of light wave propagation. The specific implementation is as follows:

[0082] When a crack is generated or extended, the stress concentration at the crack tip causes local stretching or compression of the optical fiber surface, causing the length of the light wave propagation path to change, thereby changing the light wave interference intensity. The attenuation of the interference intensity is proportional to the depth of the crack. The deformation of the crack position causes a nonlinear shift in the light wave phase, and the change in the phase shift can reflect the length and spatial distribution of the crack.

[0083] The collected light wave interference intensity change data and light wave phase shift characteristic data are input into the crack characteristic extraction algorithm. The specific steps include:

[0084] De-noising the light wave signal, filtering out high-frequency noise caused by external vibration or environmental interference, and retaining the effective signal; fitting the crack depth data according to the interference intensity attenuation characteristic curve, and the relationship between the crack depth and light wave attenuation is determined by a specific model based on the elastic modulus and stress intensity factor of the optical fiber; length and spatial position calculation: calculate the crack length and spatial position through the relationship between the phase offset change and the spatial coordinates of the optical fiber sensor arrangement. The spatial distribution density of the sensor directly determines the resolution of the calculation.

[0085] Quantum fiber optic sensors have extremely high sensitivity and can detect the formation and expansion of cracks in real time at micron-level resolution. Through comprehensive analysis of interference intensity changes and phase shifts, the depth, length and spatial position of the crack can be accurately calculated, providing a reliable basis for crack expansion modeling and performance evaluation. The sensor can monitor crack characteristics in real time in the dynamic environment of hose operation, providing conditions for timely detection of potential faults.

[0086] Example 1: In a hose with an internal pressure of 6 MPa, quantum fiber optic sensors are evenly arranged on the inner wall, collecting light wave signals every 1 cm. The external environment pressure is 10 MPa and the temperature is 40°C.

[0087] Test results: The sensor detected a 15% drop in interference intensity at the 8 cm position, with a phase shift of 0.8 rad; the crack feature extraction algorithm analysis results showed that the crack depth was 1.5 mm, the length was 4 mm, and the spatial position was (8 cm, 3 cm). The test results provide high-precision geometric feature data for subsequent crack propagation path modeling; the data is updated in real time, and the dynamic characteristics of the crack are accurately monitored.

[0088] Example 2: Under the action of complex external stress gradient (maximum stress of 12 MPa), the quantum fiber optic sensor records light wave signals, the initial position of the crack is 5 cm, and the external temperature difference is -10°C to 50°C.

[0089] Test results: The interference intensity dropped by 20% in the crack propagation area, and the phase shift changed by 1.2 rad; the crack characteristic extraction algorithm concluded that the crack depth was 2 mm, the length was 6 mm, and the crack extended from the 5th cm position to the 7th cm. The geometric characteristics of the crack propagation trend under high temperature stress environment were provided; high-quality data was input for fluid anomaly prediction modeling.

[0090] Preferably, based on the geometric characteristic data and the dynamic physical field model, the crack propagation path and rate are simulated, including using a nonlinear fracture mechanics analysis algorithm combined with a dynamic physical field model to calculate the crack propagation path and propagation rate under different internal pressures, external stresses and oil flow pressures, and generating crack propagation data.

[0091] In the present invention, the crack propagation path and rate directly affect the structural integrity and operational safety of the hose. The crack propagation simulation combines the crack geometric characteristic data (including depth, length and spatial position) with the dynamic physical field model (including external stress distribution, internal pressure gradient and oil flow pressure), based on the nonlinear fracture mechanics analysis algorithm, dynamically calculates the crack propagation behavior under the action of comprehensive loads, and generates crack propagation data.

[0092] The core principle of crack growth is based on the stress intensity factor (SIF) in fracture mechanics. ) and the stress field distribution at the crack tip. When the stress intensity factor reaches the fracture toughness threshold of the material ( ), the crack will propagate along the maximum stress path. The nonlinear fracture mechanics analysis algorithm predicts the dynamic propagation behavior of the crack by iteratively calculating the stress field at the crack tip. The main steps of the algorithm include:

[0093] (1) Stress field calculation: calculate the stress intensity factor at the crack tip in the dynamic physical field model, and dynamically update the stress field distribution according to the elastic modulus and load conditions of the hose material. (2) Determination of the extension direction: determine the crack extension path according to the maximum stress direction at the crack tip (usually extending along the direction of maximum normal stress). (3) Extension rate calculation: calculate the crack extension rate through the energy release rate at the crack tip and the material toughness parameter, and then predict the crack extension time under different load conditions.

[0094] The dynamic physics model provides boundary conditions for crack propagation by integrating external stress, internal pressure and oil flow pressure. The model combines finite element analysis (FEM) to dynamically simulate the stress distribution and pressure gradient of the hose under the action of multiple physical fields:

[0095] External stress, the stress concentration area caused by environmental load provides an external driving force for crack extension. Internal pressure, the radial pressure of the oil fluid on the inner wall of the hose under high pressure conditions, is a stress intensity factor that affects crack extension. Oil flow pressure, the shear stress caused by oil flow and the disturbance of fluid pressure fluctuation on the crack extension path.

[0096] Through the nonlinear fracture mechanics analysis algorithm, the crack propagation path can be accurately predicted according to the dynamic load conditions, avoiding the path deviation problem of traditional methods. The crack propagation rate is affected by the coupling of multiple loads. The model achieves high-precision propagation rate estimation through dynamic calculation of energy release rate. The stress and pressure field distribution conditions provided by the dynamic physical field model can comprehensively evaluate the impact of multiple loads on crack propagation, providing more convincing data support for the performance evaluation of hoses.

[0097] Example 1: In a hose with an internal pressure of 6 MPa, the initial crack length is 5 mm, the depth is 2 mm, and the external environmental load is 12 MPa. The crack propagation path and rate are simulated using a dynamic physical field model and a nonlinear fracture mechanics analysis algorithm.

[0098] Simulation results: The cracks extend radially in the external stress concentration area. After 24 hours, the crack length increases to 10 mm and the depth expands to 4 mm. The expansion path is consistent with the direction of the pressure gradient, and the crack stops expanding in the area with lower pressure. Through the accurate calculation of the expansion rate, the possible failure area of ​​the hose under long-term high pressure conditions is predicted, providing a basis for subsequent maintenance.

[0099] Example 2: In a hose with an external pressure fluctuation range of 10-15 MPa and an oil flow pressure gradient of 0.5 MPa / m, the initial crack length is 3 mm, the depth is 1 mm, and the location is in the middle of the hose.

[0100] Simulation results: The crack propagation path extends radially in the direction of maximum stress. The crack length increases to 15 mm and the depth reaches 3 mm within 72 hours. Fluid pressure fluctuations cause the crack tip to accelerate in stages, and the peak rate appears in the area with higher flow rate. The simulation results show that the failure risk of the hose increases in the area with drastic flow rate changes, and further monitoring of this area is needed.

[0101] Add particles with response characteristics inside the hose, stimulate the movement of the particles through an external device, and collect the movement behavior data of the particles; locate the leakage point of the hose and generate leakage characteristic data by analyzing the abnormal characteristics in the movement behavior of the particles; establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the impact of the leakage on the fluid distribution;

[0102] In the present invention, the location of the leakage point and the impact assessment are one of the core tasks. By introducing particles with response characteristics (such as magnetic nanoparticles) inside the hose and using an external device (such as a ring electromagnetic detector) to generate an electromagnetic field to stimulate the movement of these particles, the movement behavior of the particles inside the hose can be monitored in real time. The movement trajectory and speed of the particles will be abnormal near the leakage point, such as a decrease in flow rate or a reversal of direction. By analyzing these abnormal characteristics, the leakage point can be accurately located and its impact on the fluid distribution can be further evaluated.

[0103] The introduced particles are magnetic nanoparticles with a particle size range of 10-50 nanometers. They have good electromagnetic response characteristics and fluid compatibility and do not affect the physical and chemical properties of oil. The externally arranged annular electromagnetic detector generates a stable alternating electromagnetic field to stimulate the particles to produce responsive movement, while capturing the particle's motion trajectory data and speed changes in real time.

[0104] The particle movement trajectory and velocity data are collected through the annular electromagnetic detector to form a complete movement behavior data set. Under normal circumstances, the particle movement trajectory and flow velocity distribution are uniform. Near the leak point, the particle movement behavior changes significantly, for example:

[0105] Flow velocity drop: The velocity of particles in the leak area drops significantly, indicating that the fluid leak causes a local decrease in flow velocity. Trajectory disturbance: The particle trajectory deviates from the normal streamline, indicating turbulence. Direction reversal: Some particles may change their direction of movement due to leakage backflow.

[0106] The motion behavior data is analyzed through a dynamic clustering algorithm, and the abnormal feature area is divided into a leakage point range, and leakage feature data is further generated, including the location of the leakage point, leakage rate and leakage range.

[0107] The leakage impact model combines leakage characteristic data with fluid anomaly data, and evaluates the dynamic impact of leakage on the overall fluid distribution through fluid dynamics simulation, including pressure gradient changes, velocity distribution adjustments, and the formation of turbulent areas. The main functions of the model include: predicting the range of local pressure reduction caused by the leakage point; evaluating the velocity changes of the fluid around the leakage point, identifying areas that may affect the transfer efficiency; and identifying potential risks that may cause larger-scale anomalies by simulating the fluid backflow and turbulence range of the leakage point.

[0108] Example 1: In a hose with an internal pressure of 8 MPa, magnetic nanoparticles (particle size 20 nanometers) are introduced and the particle movement is stimulated by an external annular electromagnetic detector. The external environmental pressure is 10 MPa, and the detection targets are the location of the leak point and the leak rate. The annular electromagnetic detector detects a sudden drop in the particle velocity at the 15th meter position, from the normal flow rate of 0.5 m / s to 0.2 m / s. The dynamic clustering algorithm analysis results show that the trajectory of the particles at this position is disordered, and the leak point is located at the 15th meter position.

[0109] Evaluation results: The leakage rate was calculated to be 20 ml per second; the leakage impact model predicted a 15% pressure drop within a 10-meter radius around the leak.

[0110] Example 2: Magnetic nanoparticles (50 nanometers in diameter) are introduced into the oil pipeline. The external annular electromagnetic detector detects abnormal flow velocity at multiple locations. The system needs to evaluate the impact of multi-point leakage on the overall fluid distribution. The system detects abnormal flow velocity at the 10th and 30th meters, respectively, with trajectory disturbance ranges of 2 meters and 1 meter, respectively.

[0111] The leakage impact model predicts that the 10-meter leakage point will cause a 10% decrease in pressure within the 15-meter range downstream, and the turbulence range will extend to 20 meters; the 30-meter leakage point will have a small impact on the flow velocity distribution, but will increase sensitivity to pressure fluctuations. It is recommended to urgently repair the 10-meter position and monitor the fluid pressure at the 30-meter position.

[0112] Preferably, the adding of particles with response characteristics inside the hose and collecting the movement behavior data of the particles includes uniformly introducing magnetic nanoparticles into the petroleum, wherein the particle size of the magnetic nanoparticles ranges from 10 nanometers to 50 nanometers, generating an alternating electromagnetic field through a ring-shaped electromagnetic detector arranged outside the hose to stimulate the movement of the magnetic nanoparticles, and recording the movement trajectory data and movement speed data of the magnetic nanoparticles in real time.

[0113] In the present invention, real-time monitoring of internal fluid behavior is crucial for locating leakage points and analyzing abnormal fluid distribution. The present invention introduces magnetic nanoparticles (with a particle size range of 10 nanometers to 50 nanometers) with responsive characteristics evenly into the petroleum, and uses an externally arranged annular electromagnetic detector to generate an alternating electromagnetic field to excite particle movement. By monitoring the particle movement trajectory and speed changes, the dynamic behavior of the fluid in the hose is analyzed. Magnetic nanoparticles can accurately reflect the microscopic characteristics and changes of fluid flow due to their high sensitivity to electromagnetic fields and good dispersion in fluids.

[0114] Magnetic nanoparticles are made of materials with high magnetic permeability (such as ferrites, magnetic alloys) and have the following properties:

[0115] Fluid compatibility: The surface of the particles is coated with chemically inert materials (such as silica) to ensure that they are evenly dispersed in the oil and do not affect the fluid properties. Electromagnetic responsiveness: Under the stimulation of an alternating electromagnetic field, the particles can produce a significant motion response, and their trajectory and speed are highly correlated with the fluid properties.

[0116] Ring-shaped electromagnetic detectors are evenly arranged along the outer wall of the hose, with a detection unit arranged every 10 cm. By generating an alternating electromagnetic field, the detectors can stimulate the movement of magnetic nanoparticles in the oil and collect the movement trajectory and speed data of the particles in real time. The frequency and intensity of the electromagnetic field can be adjusted to adapt to different conveying pressures and fluid viscosities.

[0117] Driven by an alternating electromagnetic field, particles move along the fluid flow path in the oil, and the detector captures the three-dimensional motion trajectory of the particles through magnetic response signals. The detector calculates the movement speed based on the position change of the particles within a fixed time interval and provides fluid flow velocity distribution information. The collected trajectory and velocity data not only reflect the overall flow characteristics of the fluid in the hose, but also reveal anomalies in local areas, such as velocity drop and turbulence formation.

[0118] The high sensitivity of magnetic nanoparticles combined with the precise detection capability of the annular electromagnetic detector can record the dynamic behavior of the fluid in the hose in real time, with data accuracy reaching sub-millimeter level. Through the abnormal changes in particle motion trajectory and speed, potential abnormal areas in the fluid can be identified, providing basic data for subsequent leakage point location and fluid abnormality assessment. Magnetic nanoparticles can still maintain good dispersion and responsiveness in high temperature, high pressure and complex fluid environments, and are suitable for hose performance testing under different working conditions.

[0119] Example 1: In a petroleum delivery hose with an internal pressure of 8 MPa, magnetic nanoparticles with a particle size of 20 nanometers are uniformly introduced, and external annular electromagnetic detectors are arranged at a spacing of 10 cm. The detector generates an alternating electromagnetic field with a frequency of 50 Hz to excite the particle movement. The trajectory data shows that the particles move along the fluid flow path, and the motion trajectory is locally turbulent at the 5-meter position. The velocity data shows that the flow velocity at this position drops from 0.6 m / s to 0.3 m / s.

[0120] Analysis results: Local turbulence and decreased flow velocity indicate that there may be leakage or fluid anomalies at that location. The particle motion behavior analysis locates the leakage point and provides accurate input for subsequent leakage assessment.

[0121] Example 2: In a hose with a petroleum fluid pressure gradient of 0.5 MPa / m and high fluid viscosity, magnetic nanoparticles with a particle size of 50 nanometers were introduced, and a circular electromagnetic detector generated an electromagnetic field with a frequency of 80 Hz. The particle motion trajectory showed a significant directional deviation between the 10th and 12th meters, and the velocity data fluctuated in this interval, with a maximum flow rate drop of 30%.

[0122] Analysis results: Dynamic trajectory deviation indicates that the fluid may flow back in this area due to leakage, and the regional pressure drops significantly. The dynamic monitoring results provide high-precision data support for the detection and analysis of multi-point leakage.

[0123] Preferably, the method of locating the leakage point of the hose by analyzing the abnormal characteristics in the particle movement behavior includes using a dynamic clustering algorithm to perform cluster analysis on the flow velocity drop areas appearing in the movement trajectory data and movement velocity data of the magnetic nanoparticles, and combining the abnormal distribution characteristics of the particle movement trajectory to determine the specific location and leakage rate of the leakage point.

[0124] In the present invention, the leakage point usually causes a significant change in the local fluid behavior, such as a sudden drop in flow rate, a turbulent trajectory or a backflow phenomenon. By introducing magnetic nanoparticles into the oil and collecting the particle's motion trajectory and velocity data, the dynamic distribution characteristics of the fluid inside the hose can be reflected. The dynamic clustering algorithm is an analysis tool based on data feature classification, which can extract abnormal areas from a large amount of collected data, and combine the distribution characteristics of the particle's motion trajectory to accurately locate the position of the leakage point and the leakage rate.

[0125] Flow velocity drop characteristics: The presence of a leak will cause the momentum of the fluid in the leak area to weaken, which is manifested as a significant decrease in the particle movement speed. The range and amplitude of this flow velocity drop are directly related to the leak rate. Trajectory turbulence characteristics: Fluid turbulence or backflow near the leak will cause the particle trajectory to deviate from the normal streamline. The spatial distribution characteristics of the trajectory are related to the pressure distribution in the leak area.

[0126] The dynamic clustering algorithm clusters the area with a sudden drop in flow velocity into an abnormal area through real-time analysis of particle motion behavior data (including motion trajectory and velocity data), and further determines the specific location and leakage rate of the leakage point based on the spatial distribution characteristics of the particle trajectory.

[0127] The dynamic clustering algorithm receives the particle trajectory data and velocity data collected by the annular electromagnetic detector. The trajectory data is stored in the form of three-dimensional coordinates, and the velocity data is a scalar value.

[0128] Calculate the deviation of the particle movement speed in different areas from the normal flow speed, and divide the particles with a sudden drop in flow speed into a cluster. By calculating the spatial density of the trajectory, mark the abnormal trajectory area. The point with the largest sudden drop in flow speed and the most concentrated trajectory disorder in the clustering result is the leakage point location. According to the number of particles and speed changes in the area of ​​sudden drop in flow speed, combined with the spatial distribution of the leakage point, estimate the leakage rate.

[0129] The dynamic clustering algorithm can quickly extract abnormal areas from the particle motion behavior data and accurately locate the leak point with a positioning error of less than 5 cm. By combining the analysis of the sudden drop in flow velocity and the trajectory disturbance, the leak rate can be accurately calculated to provide key data for leak repair. The dynamic clustering algorithm is suitable for real-time data processing and can respond quickly in complex environments to ensure the timeliness and reliability of the test.

[0130] Example 1: Magnetic nanoparticles with a particle size of 20 nanometers were introduced into the hose, the external pressure was 10 MPa, and the internal pressure was 6 MPa. The circular electromagnetic detectors were arranged at a spacing of 10 cm to collect the movement trajectory and velocity data of the particles. The dynamic clustering algorithm analyzed the movement velocity data and found that the particle velocity at the 12th meter position dropped from 0.8 m / s to 0.3 m / s. The trajectory data showed obvious disorder characteristics at the 12th meter position, and the trajectory deviated from the normal streamline and was concentrated.

[0131] Analysis results: Clustering results determined that the leak point was at the 12th meter; based on the magnitude of the sudden drop in flow velocity and the number of particles in the abnormal area, the leak rate was calculated to be 30 ml per second. The precise location of the leak point provided a timely basis for leak repair.

[0132] Example 2: Magnetic nanoparticles with a particle size of 50 nanometers were introduced into a hose with a petroleum fluid pressure gradient of 0.5 MPa / m. The annular electromagnetic detector collected particle motion data at multiple locations. The dynamic clustering algorithm detected the flow velocity drop areas at the 8th and 20th meters; the particle velocity at the 8th meter position dropped from 0.6 m / s to 0.2 m / s, and the trajectory turbulence range was 1.5 meters; the particle velocity at the 20th meter position dropped from 0.7 m / s to 0.4 m / s, and the trajectory turbulence range was 1 meter.

[0133] Analysis results: The leakage rate at the 8th meter position is 50 ml per second; the leakage rate at the 20th meter position is 20 ml per second. Accurately locate multiple leaks and provide reliable data support for hose maintenance under complex working conditions.

[0134] The corrosion behavior of the hose is monitored by a sensor device to generate corrosion characteristic data; the corrosion characteristic data is combined with the dynamic physical field model to simulate the corrosion expansion of the hose; the corrosion expansion data, fluid anomaly data and leakage characteristic data are combined to evaluate the comprehensive performance of the hose and predict its remaining life.

[0135] like Figure 2 As shown, in the present invention, corrosion behavior is an important factor affecting the long-term operating reliability of the hose. Corrosion characteristics (such as corrosion rate, corrosion depth, and corrosion area) can reflect the durability changes of the hose in a complex environment. The present invention collects the corrosion behavior data of the inner and outer walls of the hose in real time through a flexible electrochemical sensor membrane, simulates the corrosion expansion in combination with a dynamic physical field model, and comprehensively analyzes the corrosion expansion data with fluid abnormality data and leakage characteristic data to evaluate the comprehensive performance of the hose and predict its remaining life.

[0136] Flexible electrochemical sensing membranes are attached to the inner and outer walls of the hose to detect the concentration of corrosion products and changes in electrochemical potential in real time: Corrosion product concentration monitoring: The electrochemical sensing membrane is highly sensitive to changes in the concentration of metal ions (such as iron ions) produced by corrosion, and captures the concentration of corrosion products through chemical adsorption and changes in current signals. Electrochemical potential monitoring: The sensing membrane records the changes in the corrosion potential of the hose material in real time, and infers the corrosion rate in combination with environmental conditions (such as temperature and humidity).

[0137] Corrosion characteristic data include: corrosion rate, the loss of material thickness per unit time; maximum corrosion depth, the deepest loss area caused by corrosion; corrosion area, the total surface area affected by corrosion.

[0138] The dynamic simulation of corrosion expansion is based on corrosion characteristic data and dynamic physical field models. The dynamic physical field model integrates the external environmental stress and internal fluid pressure of the hose to provide boundary conditions for corrosion expansion. Expansion path: Corrosion usually expands along the stress concentration area. The dynamic physical field model calculates the stress gradient and predicts the preferred expansion direction of corrosion. Expansion rate: The corrosion rate is related to the chemical composition of the fluid (such as water and oxidant concentration). The expansion rate is simulated by coupling the corrosion characteristic data and the physical field data. Impact range: Combined with multi-scale corrosion modeling technology, the time-dependent impact range of corrosion is predicted.

[0139] By integrating corrosion expansion data with fluid anomaly data and leakage characteristic data, a multimodal collaborative evaluation model is established to analyze the comprehensive performance of the hose: pressure resistance. Corrosion and leakage will reduce the pressure resistance of the hose. The multimodal evaluation model combines parameters such as corrosion depth and leakage location to quantify the decline in pressure resistance. Sealing performance. Through leakage characteristic data, the changes in the sealing performance of the hose under corrosion and fluid anomaly conditions are evaluated. Corrosion resistance performance. According to the scope of influence of corrosion expansion, the corrosion resistance life of the hose in the current environment is predicted.

[0140] The remaining life prediction is based on the corrosion expansion rate and the current performance attenuation. The remaining service time is dynamically calculated through the time step method, providing a decision-making basis for operation and maintenance.

[0141] Preferably, the monitoring of the corrosion behavior of the hose by means of a sensor device includes collecting corrosion product concentration data and electrochemical potential change data using a flexible electrochemical sensor membrane arranged on the inner and outer walls of the hose, and combining the corrosion product concentration data and electrochemical potential change data to extract corrosion rate data, maximum corrosion depth data and corrosion area data.

[0142] Corrosion behavior is a key factor affecting the structural integrity and sealing performance of petroleum transfer hoses during long-term operation. The present invention uses flexible electrochemical sensing membranes arranged on the inner and outer walls of the hose to monitor the concentration of corrosion products and changes in electrochemical potential in real time, thereby extracting key data such as corrosion rate, maximum corrosion depth and corrosion area, and comprehensively reflecting the corrosion status of the hose.

[0143] Flexible electrochemical sensor membrane is a sensing material based on the principle of electrochemical reaction, which has good flexibility and durability. The membrane is embedded with highly sensitive electrochemical detection units, including ion selective electrodes and potential monitoring elements. Its core working mechanism includes:

[0144] Corrosion product concentration monitoring: When the hose material corrodes, metal ions (such as Fe²⁺, Fe³⁺) are released into the medium. The flexible electrochemical sensing membrane captures these metal ions through chemical adsorption and generates an electrical signal proportional to the ion concentration.

[0145] Monitoring of electrochemical potential changes: The corrosion reaction of the hose material will cause changes in the local electrochemical potential. The sensing membrane records the potential changes in real time, reflecting the corrosion rate and electrochemical conditions.

[0146] The collected corrosion product concentration data and electrochemical potential change data are used to extract the following corrosion characteristic data through data processing algorithms: corrosion rate, which is calculated by the generation rate of metal ions and the surface area of ​​the material, and can directly reflect the loss of hose wall thickness per unit time. Maximum corrosion depth, based on the distribution of electrochemical potential changes, combined with the material properties of the hose and the law of corrosion expansion, the deepest loss depth in the corrosion area is extracted. Corrosion area, using the distribution of corrosion product concentration data, the spatial range of the corrosion area is identified, and the total area affected by corrosion is calculated.

[0147] The high sensitivity of the flexible electrochemical sensor membrane ensures real-time monitoring of the concentration of corrosion products and changes in electrochemical potential, and the data acquisition has high time resolution and high accuracy. The three core indicators of corrosion rate, maximum corrosion depth and corrosion area comprehensively describe the corrosion state of the hose, providing reliable input for subsequent corrosion extension modeling and life prediction. The flexible electrochemical sensor membrane is suitable for complex environments with high pressure, high temperature and strong chemical corrosion, and can operate stably for a long time.

[0148] Example 1: In a high-temperature environment with an internal pressure of 8 MPa and a temperature of 60°C in the hose, a group of flexible electrochemical sensor membranes are arranged every 15 cm. The sensor membrane collects the concentration of corrosion products and the change of electrochemical potential in real time. At the 10th meter position, the sensor membrane detected that the concentration of corrosion products increased to 5 ppm and the electrochemical potential change was 150 mV. The data processing results showed that the corrosion rate was 0.03 mm / year, the maximum corrosion depth was 0.8 mm, and the corrosion area was 12 cm². The corrosion rate and corrosion depth provide a real-time assessment of the corrosion risk at this location; the corrosion area shows the affected range, providing high-precision data for subsequent corrosion expansion modeling.

[0149] Example 2: In a chemical corrosion environment with 5% water and high concentration of oxidants in petroleum, the flexible electrochemical sensor membrane is arranged at a spacing of 10 cm. The corrosion product concentration at the 20th meter position reaches 10 ppm, and the electrochemical potential change is 200 mV. Data analysis shows that the corrosion rate at this position is 0.08 mm / year, the maximum corrosion depth is 1.5 mm, and the corrosion area is 25 cm². The monitoring data clarifies the risk level of high corrosion areas and provides a scientific basis for hose life prediction and maintenance planning in complex environments.

[0150] Preferably, Figure 3 As shown, the comprehensive performance evaluation of the hose includes inputting the corrosion rate data, maximum corrosion depth data and corrosion area data together with the crack extension data, fluid anomaly data and leakage characteristic data into a multimodal collaborative evaluation model, and comprehensively analyzing the pressure resistance, sealing performance and corrosion resistance of the hose, and calculating the remaining service life of the hose based on the remaining life prediction algorithm.

[0151] The comprehensive performance evaluation of hoses is a multi-factor dynamic analysis task, the purpose of which is to comprehensively evaluate the current state of the hose and predict its remaining life by integrating data from multiple performance parameters. The present invention adopts a multi-modal collaborative evaluation model to integrate data related to corrosion, cracks, fluid anomalies and leakage, and analyze the hose's pressure resistance, sealing performance and corrosion resistance. Finally, combined with the remaining life prediction algorithm, the remaining service life of the hose is dynamically calculated based on the trend of performance degradation.

[0152] The multimodal collaborative evaluation model is an analytical framework that integrates multidimensional data to quantify the comprehensive performance of hoses.

[0153] Input data: Corrosion-related data, including corrosion rate, maximum corrosion depth and corrosion area, reflect the durability changes of hose materials. Crack-related data, including crack propagation path and propagation rate, reveal the weakening of hose structural integrity. Fluid and leakage data, fluid anomaly data provides information on internal pressure and flow velocity distribution, and leakage characteristic data reflects the local impact of leakage points on overall performance.

[0154] The model assigns priorities based on the weight of each set of data on performance. For example, corrosion depth has a greater weight on corrosion resistance, while crack growth rate has a greater impact on compressive resistance. Multimodal data association analysis is used to evaluate the interaction between parameters, such as the linkage between fluid anomalies caused by leakage and crack growth.

[0155] The compression capacity calculation combines the corrosion depth and crack growth rate to evaluate the change in the strength of the hose under internal pressure. ) is used as the core indicator to measure the failure risk of the hose under the influence of multiple factors.

[0156] The sealing performance is mainly affected by leakage characteristic data and fluid abnormality data. The changes in fluid pressure near the leakage point and the degree of weakening of the sealing effect are analyzed.

[0157] The corrosion resistance is directly based on the change trend of corrosion rate and corrosion area, predicting the degree of degradation of hose material properties.

[0158] The remaining life prediction algorithm combines the performance decay trend and dynamically iterates the remaining service life of the hose based on the time step method: set safe operation thresholds for pressure resistance, sealing performance, and corrosion resistance. Based on the decay rate of the current performance parameters, predict the remaining operating time of the hose before reaching the threshold. Weight the results of multiple performance life predictions to calculate the overall remaining life.

[0159] The multimodal collaborative evaluation model integrates multiple data sources and can comprehensively quantify the performance of the hose from a multi-dimensional perspective, providing a scientific basis for operational decisions. The remaining life prediction algorithm dynamically combines the performance attenuation trend to provide accurate life evaluation results and avoid the errors of traditional single-dimensional data prediction. The model can update input data in real time to adapt to the dynamic changes of hose performance as the operating environment changes.

[0160] The core idea of ​​remaining life prediction is to calculate the remaining time before the hose reaches its performance limit based on the dynamic attenuation trend of the current performance status (such as pressure resistance, sealing performance, and corrosion resistance) combined with the threshold conditions. Suppose the comprehensive performance status of the hose is expressed as The decay law over time can be obtained by empirical model or data fitting. The comprehensive performance reaches the failure threshold Time is the estimated life of the hose, and the current time is , then the remaining life Calculated as:

[0161]

[0162] in, It can be derived through the following steps.

[0163] The remaining life prediction algorithm derivation process includes:

[0164] (1) Performance status model, comprehensive performance status is the hose's current performance over time Based on the multimodal collaborative evaluation model, it can be expressed as:

[0165]

[0166] in, For stress resistance; For sealing performance; For corrosion resistance; , , is the weighting factor, which assigns weights according to the degree of impact of performance on hose operation.

[0167] (2) Performance decay model, performance changes over time The decay can be expressed as an exponential or linear model. The following uses a common exponential decay model:

[0168]

[0169] in, is the initial performance value; is the performance degradation rate, which is affected by the corrosion rate, crack growth rate and leakage characteristics.

[0170] The comprehensive performance status is:

[0171]

[0172] (3) Failure threshold It is the minimum acceptable value for the overall performance of the hose. It has been determined through experimental data or operating experience. Performance degradation to At the time point, the following equation is satisfied:

[0173]

[0174] (4) Solve for the remaining life, and the current time Substitute in, solve :

[0175]

[0176] in, is the effective decay rate of the comprehensive performance, defined as:

[0177]

[0178] Finally, the remaining life is calculated as:

[0179]

[0180] Input parameters:

[0181] , ;

[0182] , ;

[0183] , ;

[0184] , ,

[0185] ; Current time .

[0186] Calculation process:

[0187] Initial value of comprehensive performance:

[0188] Effective decay rate:

[0189] Expiration time: 8.0 years

[0190] Remaining life:

[0191] Result: The remaining life of the hose is 5.0 years.

[0192] Example 1: In a hose with an internal pressure of 10 MPa and an external temperature of 50°C, the corrosion rate is 0.04 mm / year, the maximum corrosion depth is 1.2 mm, and the crack growth rate is 2 mm / month. The leakage characteristic data shows that the leakage rate at the 20-meter position is 50 ml per second.

[0193] Performance evaluation: Pressure resistance, corrosion depth and crack growth rate lead to a 20% decrease in pressure resistance; Sealing performance, changes in leakage rate and pressure distribution show a 15% decrease in sealing performance; Corrosion resistance, corrosion area expanded to 30 cm², and the corrosion resistance of the material decreased by 10%.

[0194] Life prediction: The remaining life of pressure resistance is predicted to be 3 years; the remaining life of sealing performance is predicted to be 2 years; the remaining life of corrosion resistance is predicted to be 4 years; the comprehensive life prediction result is 2.5 years.

[0195] Example 2: In an environment with a fluid pressure gradient of 0.8 MPa / m and a high oxidant concentration, the corrosion rate is 0.1 mm / year, the crack growth rate is 3 mm / month, and the leakage rate is 30 ml / second.

[0196] Performance evaluation: Compression resistance: The combined effect of corrosion and cracks causes the compression resistance to decrease by 25%; Sealing performance: The abnormal flow velocity range near the leakage point extends to 2 meters, and the sealing performance decreases by 20%; Corrosion resistance: The corrosion area expands to 50 cm², and the corrosion resistance decreases by 15%.

[0197] Life prediction: The comprehensive performance evaluation model predicts a remaining life of 1.8 years.

[0198] like Figure 4 As shown, a system for testing performance parameters of a hose for transferring oil is used to implement the method for testing performance parameters of a hose for transferring oil, comprising:

[0199] The sensing module is used to collect the external environment data and internal physical property data of the hose. The sensing module includes a flexible MEMS sensor arranged on the outer wall of the hose, which is used to collect external pressure, external temperature and external stress data, and a micro-vibration acoustic wave sensor arranged inside the hose, which is used to collect internal pressure distribution and internal deformation characteristic data; the flexible MEMS sensor and the micro-vibration acoustic wave sensor are arranged on the outer wall and inside of the hose, which are used to collect external pressure, temperature, stress and internal pressure distribution and deformation characteristic data. This module realizes real-time parameter acquisition under multiple environmental conditions.

[0200] The data processing module is used to remove noise and perform nonlinear compensation on the data collected by the sensor module to generate compensated environmental data; remove noise and perform nonlinear compensation on the data collected by the sensor module to improve the accuracy of the data and generate compensated environmental data that can be used for subsequent modeling.

[0201] The modeling module is used to receive the compensated environmental data and construct a dynamic physical field model of the hose; the dynamic physical field model is constructed based on the compensated environmental data to reflect the global pressure, stress and deformation distribution of the hose.

[0202] The crack detection module is used to detect the geometric characteristic data of cracks inside the hose through changes in light wave interference intensity and light wave phase shift, and based on the geometric characteristic data and the dynamic physical field model, simulate the crack propagation path and rate to generate crack propagation data; collect light wave interference intensity change and phase shift data through quantum optical fiber sensors, combine with dynamic physical field models, identify the geometric characteristics of cracks and simulate their propagation path and rate.

[0203] The fluid anomaly analysis module is used to predict the impact of the crack on the behavior of the fluid inside the hose based on the crack propagation data and generate fluid anomaly data; and to predict the impact of the crack on the internal fluid behavior (such as flow rate and pressure distribution) using the crack propagation data and generate fluid anomaly data.

[0204] The particle excitation and monitoring module is used to introduce magnetic nanoparticles with response characteristics into the inside of the hose, stimulate the movement of the magnetic nanoparticles through an external annular electromagnetic detector and collect their movement behavior data; introduce magnetic nanoparticles, stimulate their movement in the fluid, and reflect the hose leakage point and leakage characteristics through movement trajectory and speed data.

[0205] The leakage analysis module is used to locate the leakage point of the hose by analyzing the abnormal characteristics in the movement behavior of the magnetic nanoparticles, generate leakage characteristic data, and establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the dynamic impact of the leakage on the fluid distribution; based on the abnormal characteristics of the particle movement, locate the leakage point and establish a leakage impact model to evaluate the dynamic impact of the leakage on the fluid distribution.

[0206] The corrosion monitoring module is used to monitor the corrosion behavior of the hose through a flexible electrochemical sensor membrane, generate corrosion characteristic data, and simulate corrosion expansion by combining the corrosion characteristic data with a dynamic physical field model; collect corrosion behavior data through a flexible electrochemical sensor membrane, and simulate corrosion expansion by combining a dynamic physical field model.

[0207] The comprehensive performance evaluation module is used to evaluate the comprehensive performance of the hose by combining corrosion extension data, fluid anomaly data and leakage characteristic data, and predict the remaining service life of the hose based on the remaining service life prediction algorithm. The module integrates corrosion, crack, fluid anomaly and leakage data to evaluate the pressure resistance, sealing performance and corrosion resistance of the hose, and calculates the remaining service life of the hose based on the remaining service life prediction algorithm.

[0208] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for testing performance parameters of a hose for transferring petroleum, characterized in that: The following steps are involved: The external environment data and internal physical property data of the hose are collected through sensors, and the collected data are processed to generate compensated environmental data, which are input into a multi-physics field modeling system to construct a dynamic physical field model of the hose; Detecting the optical signal changes of the cracks inside the hose through the sensor, extracting the geometric characteristic data of the cracks; simulating the crack propagation path and rate based on the geometric characteristic data and the dynamic physical field model, and generating crack propagation data; predicting the impact of the cracks on the behavior of the fluid inside the hose in combination with the crack propagation data, and generating fluid abnormality data; Add particles with response characteristics inside the hose, stimulate the movement of the particles through an external device, and collect the movement behavior data of the particles; locate the leakage point of the hose and generate leakage characteristic data by analyzing the abnormal characteristics in the movement behavior of the particles; establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the impact of the leakage on the fluid distribution; The corrosion behavior of the hose is monitored by a sensor device to generate corrosion characteristic data; the corrosion characteristic data is combined with the dynamic physical field model to simulate the corrosion expansion of the hose; the corrosion expansion data, fluid anomaly data and leakage characteristic data are combined to evaluate the comprehensive performance of the hose and predict its remaining life.

2. The method for testing performance parameters of a hose for transferring petroleum according to claim 1, characterized in that: The method of collecting the external environment data and internal physical property data of the hose through sensors includes collecting external pressure, external temperature and external stress data through a flexible MEMS sensor array arranged on the outer wall of the hose, and collecting internal pressure distribution and internal deformation characteristic data through a micro-vibration acoustic wave sensor arranged inside the hose.

3. The method for testing performance parameters of a hose for transferring petroleum according to claim 2, characterized in that: The collected data is processed to generate compensated environmental data, including using a noise suppression algorithm to remove noise from external pressure data, external temperature data and external stress data, and dynamically correcting internal pressure distribution data and internal deformation characteristic data; and further using a nonlinear compensation algorithm to correct the measurement deviation of the sensor to generate corrected and compensated external environmental data and internal physical characteristic data.

4. The method for testing performance parameters of a hose for transferring petroleum according to claim 1, characterized in that: The method of detecting the optical signal changes of cracks inside the hose by using a sensor includes using a quantum optical fiber sensor arranged on the inner wall of the hose to collect light wave interference intensity changes and light wave phase shift characteristic data caused by the cracks in real time, and inputting the light wave interference intensity change data and light wave phase shift characteristic data into a crack characteristic extraction algorithm to calculate the crack depth data, crack length data and crack spatial position data.

5. The method for testing performance parameters of a hose for transferring petroleum according to claim 1, characterized in that: Based on the geometric characteristic data and the dynamic physical field model, the crack propagation path and rate are simulated, including using a nonlinear fracture mechanics analysis algorithm combined with a dynamic physical field model to calculate the crack propagation path and propagation rate under different internal pressures, external stresses and oil flow pressures, and generating crack propagation data.

6. The method for testing performance parameters of a hose for transferring petroleum according to claim 1, characterized in that: The method of adding particles with response characteristics into the inside of the hose and collecting the movement behavior data of the particles includes uniformly introducing magnetic nanoparticles into the petroleum, wherein the particle size of the magnetic nanoparticles ranges from 10 nanometers to 50 nanometers, generating an alternating electromagnetic field through a ring-shaped electromagnetic detector arranged outside the hose to stimulate the movement of the magnetic nanoparticles, and recording the movement trajectory data and movement speed data of the magnetic nanoparticles in real time.

7. The method for testing performance parameters of a hose for transferring petroleum according to claim 6, characterized in that: The method of locating the leakage point of the hose by analyzing the abnormal characteristics in the particle movement behavior includes using a dynamic clustering algorithm to perform cluster analysis on the flow velocity drop areas appearing in the movement trajectory data and movement speed data of the magnetic nanoparticles, and combining the abnormal distribution characteristics of the particle movement trajectory to determine the specific location and leakage rate of the leakage point.

8. The method for testing performance parameters of a hose for transferring petroleum according to claim 1, characterized in that: The monitoring of the corrosion behavior of the hose by the sensor device includes collecting corrosion product concentration data and electrochemical potential change data using a flexible electrochemical sensor membrane arranged on the inner and outer walls of the hose, and combining the corrosion product concentration data and electrochemical potential change data to extract corrosion rate data, maximum corrosion depth data and corrosion area data.

9. The method for testing performance parameters of a hose for transferring petroleum according to claim 8, characterized in that: The comprehensive performance evaluation of the hose includes inputting the corrosion rate data, maximum corrosion depth data and corrosion area data together with crack extension data, fluid anomaly data and leakage characteristic data into a multimodal collaborative evaluation model, comprehensively analyzing the pressure resistance, sealing performance and corrosion resistance of the hose, and calculating the remaining service life of the hose based on a remaining life prediction algorithm.

10. A system for testing performance parameters of a hose for transferring oil, used for implementing the method for testing performance parameters of a hose for transferring oil as claimed in any one of claims 1 to 9, characterized in that: include: A sensing module, used to collect external environment data and internal physical property data of the hose, the sensing module includes a flexible MEMS sensor arranged on the outer wall of the hose, used to collect external pressure, external temperature and external stress data, and a micro-vibration acoustic wave sensor arranged inside the hose, used to collect internal pressure distribution and internal deformation characteristic data; A data processing module, used for performing noise removal and nonlinear compensation processing on the data collected by the sensor module to generate compensated environmental data; A modeling module, used for receiving the compensated environmental data and constructing a dynamic physical field model of the hose; A crack detection module, used to detect geometric characteristic data of cracks inside the hose through changes in light wave interference intensity and light wave phase shift, and simulate crack propagation path and rate based on the geometric characteristic data and a dynamic physical field model to generate crack propagation data; A fluid anomaly analysis module, used to predict the impact of the crack on the behavior of the fluid inside the hose based on the crack propagation data, and generate fluid anomaly data; A particle excitation and monitoring module is used to introduce magnetic nanoparticles with response characteristics into the hose, excite the magnetic nanoparticles to move through an external annular electromagnetic detector and collect movement behavior data; A leakage analysis module, used to locate the leakage point of the hose by analyzing the abnormal characteristics in the movement behavior of the magnetic nanoparticles, generate leakage characteristic data, and establish a leakage impact model based on the leakage characteristic data and the fluid abnormal data to evaluate the dynamic impact of the leakage on the fluid distribution; A corrosion monitoring module, for monitoring the corrosion behavior of the hose through a flexible electrochemical sensor membrane, generating corrosion characteristic data, and combining the corrosion characteristic data with a dynamic physical field model to simulate corrosion expansion; The comprehensive performance evaluation module is used to evaluate the comprehensive performance of the hose by combining corrosion extension data, fluid anomaly data and leakage characteristic data, and predict the remaining service life of the hose based on the remaining service life prediction algorithm.

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