A thin-walled nickel alloy pipe hydrostatic testing system

Abstract

This invention relates to the field of hydrostatic testing technology for nickel alloy pipes, and discloses a hydrostatic testing system for thin-walled nickel alloy pipes. The system includes establishing a closed hydrostatic chamber using an annular sealing clamp under axial loading of the thin-walled nickel alloy pipe, and then collecting strain distribution data on the pipe surface using a distributed fiber optic sensor array. Based on the collected strain distribution data, a three-dimensional stress field reconstruction model is constructed to calculate the equivalent plastic deformation threshold of the pipe under different water pressure gradients. The slope parameters of the hydrostatic loading curve are adjusted accordingly, and a dynamic hydrostatic loading command is generated. A high-frequency pressure pulse generator executes the command, simultaneously triggering an X-ray diffractometer to collect lattice distortion data of the pipe. This data is input into a material micro-damage evolution model, outputting a spatial distribution map of micro-defects. Finally, the material constitutive parameters of the three-dimensional stress field reconstruction model are corrected based on the map, forming a closed-loop feedback control, achieving multi-dimensional data acquisition and precise control of the hydrostatic test of the thin-walled nickel alloy pipe.

Description

Technical Field

[0001] This invention relates to the field of hydrostatic testing technology for nickel alloy pipes, specifically a hydrostatic testing system for thin-walled nickel alloy pipes. Background Technology

[0002] Thin-walled nickel alloy tubes, with their excellent high-temperature resistance, corrosion resistance, and high strength, are widely used in high-end industrial fields such as aerospace, nuclear power, and petrochemicals. They are often used as critical components for transporting high-temperature and high-pressure fluids or bearing complex loads. Because these applications place extremely high demands on the structural integrity and pressure resistance of thin-walled nickel alloy tubes, hydrostatic testing, as an important means of detecting their pressure resistance and potential defects, is indispensable in the production and application of thin-walled nickel alloy tubes.

[0003] However, existing hydrostatic testing technologies for thin-walled nickel alloy tubes have many shortcomings, making it difficult to meet the demands of high-end fields for testing accuracy and data integrity. Regarding sealing, traditional hydrostatic tests often employ simple end-face seals or O-ring seals. When the thin-walled nickel alloy tube is under axial loading, the tube body is prone to slight deformation due to stress, leading to loose sealing and pressure leakage. To maintain stable test pressure, frequent pressure replenishment is required, which not only affects testing efficiency but also causes pressure fluctuations, distorting the collected test data and failing to accurately reflect the tube's true pressure resistance.

[0004] In the strain acquisition stage, existing technologies mostly rely on resistance strain gauges to measure strain at single or a few points. Because the strain distribution on the surface of thin-walled nickel alloy tubes is significantly non-uniform under water pressure, especially in areas such as weld seams and areas with minor wall thickness deviations where stress concentration easily occurs, strain data from only a few discrete points cannot comprehensively capture the strain distribution characteristics of the tube surface. This results in insufficient data support for the subsequent stress field model, making it difficult to accurately reflect the stress state inside the tube, and consequently affecting the assessment of plastic deformation and damage risk.

[0005] In hydraulic loading control, traditional tests often employ constant slope loading or stepped loading modes, with loading parameters preset before the test. These parameters cannot be adjusted based on the actual deformation of the pipe during the test. Since different batches and specifications of thin-walled nickel alloy pipes exhibit varying plastic deformation characteristics, a fixed loading mode may lead to a mismatch between the loading rate and the pipe's deformation response: if the loading rate is too fast, unexpected micro-damage may occur before the pipe fully exhibits its plastic deformation characteristics; if the loading rate is too slow, the test cycle will be significantly prolonged, reducing test efficiency and making precise control of the pipe's dynamic deformation process impossible.

[0006] Current hydrostatic tests primarily focus on macroscopic phenomena such as pressure-strain curves and whether the pipe ruptures, lacking monitoring of changes in the pipe's microstructure. In reality, macroscopic failure of thin-walled nickel alloy pipes under hydrostatic pressure often stems from the accumulation of internal lattice distortions and the initiation and expansion of microscopic defects (such as dislocations and micropores). However, current technologies cannot obtain data at these microscopic levels, making it difficult to fundamentally reveal the damage evolution mechanism of the pipe. Consequently, test results can only serve as a basis for macroscopic compliance judgments, failing to provide in-depth technical support for material performance optimization and pipe service life assessment.

[0007] Existing three-dimensional stress field reconstruction models are mostly constructed based on preset material constitutive parameters. During the experiment, the influence of microscopic defects in the tube on the material's mechanical properties is not considered, nor are the models corrected based on the actual experimental data collected. As the experiment progresses, the deviation between the model's preset parameters and the actual material properties of the tube gradually increases, leading to a decrease in the accuracy of the calculated equivalent plastic deformation threshold. This, in turn, renders the subsequently generated loading commands unreasonable, creating a vicious cycle of "model deviation - command inaccuracy - data distortion," which seriously affects the reliability of the experimental results. Summary of the Invention

[0008] The purpose of this invention is to provide a hydrostatic testing system for thin-walled nickel alloy pipes to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides a hydrostatic testing system for thin-walled nickel alloy pipes, the system comprising:

[0010] Under axial loading of a thin-walled nickel alloy tube, a closed hydraulic cavity is established by an annular sealing clamp, and a distributed fiber optic sensor array is used to collect strain distribution data on the tube surface.

[0011] A three-dimensional stress field reconstruction model is constructed based on the strain distribution data of the tube surface, and the equivalent plastic deformation threshold of the thin-walled nickel alloy tube under different water pressure gradients is calculated through the three-dimensional stress field reconstruction model.

[0012] The slope parameter of the water pressure loading curve is adjusted according to the equivalent plastic deformation threshold to generate a dynamic water pressure loading command;

[0013] The dynamic water pressure loading command is executed by a high-frequency pressure pulse generator, which simultaneously triggers an X-ray diffractometer to collect lattice distortion data of the thin-walled nickel alloy tube.

[0014] The lattice distortion data is input into the material micro-damage evolution model, and the spatial distribution map of micro-defects in the thin-walled nickel alloy tube is output.

[0015] Based on the spatial distribution map of micro-defects, the material constitutive parameters of the three-dimensional stress field reconstruction model are corrected to form a closed-loop feedback control hydrostatic test system.

[0016] Preferably, the establishment of a closed hydraulic cavity using an annular sealing clamp includes:

[0017] Hydraulic expansion sealing rings are installed at both ends of the thin-walled nickel alloy tube, and radial preload is applied through the hydraulic expansion sealing rings;

[0018] The contact surface of the hydraulic expansion sealing ring is covered with a multi-layer graphene coating, and the change in the friction coefficient of the sealing interface is monitored in real time.

[0019] The hydraulic expansion pressure is dynamically adjusted according to the change in the friction coefficient to maintain the leakage rate of the closed water pressure cavity below a preset threshold.

[0020] Preferably, the deployment method for acquiring strain distribution data on the pipe surface using a distributed fiber optic sensor array includes:

[0021] Single-mode optical fibers are spirally wound along the axis of a thin-walled nickel alloy tube to form a spiral sensing network with adjustable spacing.

[0022] The circumferential strain gradient of the tube is captured by the spiral sensing network, and a full-surface strain distribution matrix is ​​generated by combining the axial strain data.

[0023] The spatial resolution of the full-surface strain distribution matrix was reduced to sub-millimeter level using Brillouin optical time-domain reflectometry.

[0024] Preferably, the calculation process of the three-dimensional stress field reconstruction model includes:

[0025] The full-surface strain distribution matrix is ​​discretized into finite element mesh node data;

[0026] An elastoplastic constitutive equation is established based on the anisotropic parameters of thin-walled nickel alloy tubes, and the stress tensor of the mesh nodes is solved.

[0027] The stress field in the region without fiber optic cables is reconstructed using the Kriging interpolation algorithm, and a complete three-dimensional stress field cloud map is output.

[0028] Preferably, the method for determining the equivalent plastic deformation threshold includes:

[0029] Extract the maximum principal stress trajectory line from the three-dimensional stress field cloud map;

[0030] Integrate the cumulative plastic strain energy density along the trajectory of the maximum principal stress;

[0031] When the cumulative plastic strain energy density reaches the inflection point of the material hardening curve, the current water pressure value is marked as the equivalent plastic deformation threshold.

[0032] Preferably, the logic for generating the dynamic water pressure loading command includes:

[0033] The equivalent plastic deformation threshold is used as the upper limit to divide the water pressure loading range into multiple levels;

[0034] The loading time for each interval is allocated according to the wall thickness uniformity parameter of the thin-walled nickel alloy tube;

[0035] An S-shaped curve is used to smoothly connect the pressure gradients of adjacent intervals, generating a continuously adjustable water pressure loading curve.

[0036] Preferably, executing the dynamic water pressure loading command via a high-frequency pressure pulse generator includes:

[0037] A servo motor drives a plunger pump to generate millisecond-level pressure pulses.

[0038] The stroke frequency of the plunger pump is adjusted based on the slope parameter of the dynamic water pressure loading command.

[0039] A piezoelectric ceramic sensor is used to provide real-time feedback on the rising edge steepness of the pulse waveform, and the speed of the servo motor is corrected in a closed loop.

[0040] Preferably, the synchronously triggered X-ray diffractometer acquires lattice distortion data of the thin-walled nickel alloy tube, including:

[0041] A grid of diffraction regions is defined on the surface of a thin-walled nickel alloy tube, with each grid point corresponding to an independent diffraction angle scanning range.

[0042] Synchronize the triggering timing of the high-frequency pressure pulses and collect the lattice constant offset during the pressure trough phase;

[0043] The dislocation density and twin boundary orientation difference are calculated using the lattice constant offset to form a lattice distortion data set.

[0044] Preferably, the method for constructing the material micro-damage evolution model includes:

[0045] The lattice distortion data set is mapped to the corresponding three-dimensional stress field mesh element;

[0046] The critical shear stress of each mesh element is calculated based on dislocation dynamics theory;

[0047] The nucleation and propagation path of microcracks under critical shear stress is simulated using the phase-field method, and a spatial distribution map of micro-defects is output.

[0048] Preferably, the implementation method of the hydraulic pressure test system forming closed-loop feedback control includes:

[0049] Extract the coordinate information of the defect clustering area from the spatial distribution map of the micro-defects;

[0050] Correct the elastic modulus attenuation coefficient of the corresponding coordinates in the three-dimensional stress field reconstruction model;

[0051] The corrected elastic modulus attenuation coefficient is substituted into the stress field calculation of the next round of hydrostatic testing to form an iteratively optimized material constitutive parameter library.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] In terms of ensuring basic experimental conditions, the system employs an annular sealing clamp, which can reliably establish a closed hydrostatic chamber under axial loading of thin-walled nickel alloy tubes. This sealing method can adapt to the minute deformation of the tube under axial loading. Through the uniform force characteristics of the annular structure, it ensures that the sealing surface remains tightly fitted at all times, effectively avoiding the pressure leakage problem that is prone to occur in traditional sealing methods. This keeps the pressure in the hydrostatic chamber stable during the test, eliminating the need for frequent pressure replenishment. This not only improves experimental efficiency but also provides a stable and reliable experimental environment for subsequent strain acquisition, stress field calculation, and other stages, ensuring that the data obtained in each stage can accurately reflect the stress and deformation state of the tube under different water pressures.

[0054] In terms of strain data acquisition, the application of a distributed fiber optic sensor array enables comprehensive monitoring of the strain distribution on the pipe surface. Compared to traditional discrete-point strain acquisition methods, this sensor array can acquire continuous, high-density strain data from the pipe surface. It can accurately capture both the overall strain changes in the axial and circumferential directions of the pipe, as well as stress concentrations in localized areas such as welds and wall thickness deviations. This rich and detailed strain data provides ample foundational information for the three-dimensional stress field reconstruction model, enabling the reconstructed stress field to more accurately match the actual stress distribution of the pipe. This avoids deviations in stress field analysis caused by missing data and provides precise data support for the subsequent calculation of the equivalent plastic deformation threshold.

[0055] In the water pressure loading control stage, the system constructs a three-dimensional stress field reconstruction model based on the strain distribution data of the pipe surface. This model can calculate the equivalent plastic deformation threshold under different water pressure gradients and adjust the slope parameter of the water pressure loading curve accordingly to generate dynamic water pressure loading commands. This dynamic loading method breaks through the limitations of the traditional fixed loading mode. It can optimize the loading rate in real time according to the actual plastic deformation characteristics of the pipe under different water pressures, making the loading process match the deformation response of the material itself. This avoids unexpected micro-damage to the pipe due to excessively fast loading rates, and also prevents prolonged test cycles due to excessively slow loading rates. While ensuring test safety, it also improves test efficiency and achieves precise control over the dynamic deformation process of the pipe.

[0056] In terms of microscopic monitoring and analysis, the system executes dynamic water pressure loading commands via a high-frequency pressure pulse generator while simultaneously triggering an X-ray diffractometer to collect lattice distortion data of the tube, achieving simultaneous macroscopic water pressure loading and microscopic structure monitoring. This design fills the gap in traditional water pressure tests that lack microscopic monitoring, enabling the acquisition of not only macroscopic pressure and strain data of the tube during the test but also real-time monitoring of changes in the internal lattice structure. This provides crucial microscopic data for in-depth analysis of the damage origin of materials under water pressure. After inputting the collected lattice distortion data into the material's microscopic damage evolution model, the system outputs a spatial distribution map of microscopic defects in the tube, clearly presenting the location, morphology, and distribution patterns of these defects. This intuitively reflects the evolution process of microscopic damage in the material, fundamentally revealing the damage mechanism of the tube and providing in-depth technical support for material performance optimization and tube service reliability assessment.

[0057] In terms of model optimization and closed-loop control, the system corrects the material constitutive parameters of the three-dimensional stress field reconstruction model based on the spatial distribution map of micro-defects, forming a complete closed-loop feedback control system. This closed-loop design enables the three-dimensional stress field reconstruction model to continuously optimize its material constitutive parameters based on the actual micro-defect data obtained during the experiment. As the experiment progresses, the matching degree between the model parameters and the actual material properties of the pipe body continuously improves, thereby improving the accuracy of the model's calculation of the equivalent plastic deformation threshold and ensuring that the subsequently generated dynamic water pressure loading commands are more reasonable. Through the cyclical optimization of "data acquisition - model calculation - command execution - micro-monitoring - model correction", the system's experimental accuracy and reliability can be continuously improved, effectively avoiding the vicious cycle of "model preset - data deviation - command inaccuracy" in traditional experiments. It can better meet the stringent requirements of high-end fields such as aerospace and nuclear power for the experimental accuracy and data integrity of thin-walled nickel alloy pipes, providing comprehensive and reliable technical support for the quality control and performance testing of thin-walled nickel alloy pipes. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the working principle of the thin-walled nickel alloy pipe hydrostatic testing system described in this invention.

[0059] Figure 2 A flowchart illustrating the deployment and data acquisition of a distributed fiber optic sensor array;

[0060] Figure 3 A flowchart for generating logic for dynamic water pressure loading commands. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1 This invention provides a hydrostatic testing system for thin-walled nickel alloy tubes. The system includes: a closed hydrostatic chamber established by an annular sealing clamp; and a distributed fiber optic sensor array deployed to collect strain distribution data on the tube surface. A three-dimensional stress field reconstruction model is constructed based on the collected strain distribution data, and the model calculates the equivalent plastic deformation threshold of the thin-walled nickel alloy tube under different hydrostatic gradients. The system dynamically adjusts the slope parameter of the hydrostatic loading curve according to the calculated equivalent plastic deformation threshold, generating a dynamic hydrostatic loading command. A high-frequency pressure pulse generator receives and executes the dynamic hydrostatic loading command, simultaneously triggering an X-ray diffractometer to collect lattice distortion data of the thin-walled nickel alloy tube during pressurization. The collected lattice distortion data is input into a pre-set material micro-damage evolution model, which outputs a spatial distribution map of micro-defects in the thin-walled nickel alloy tube.

[0063] Example 1: See Figure 2Under axial loading, the sealing reliability of the closed hydraulic cavity of a thin-walled nickel alloy tube directly affects the accuracy and safety of the test data. Hydraulic expansion sealing rings are installed at both ends of the thin-walled nickel alloy tube. These rings consist of a high-elasticity alloy core and an outer oil-resistant rubber layer. The initial outer diameter of the hydraulic expansion sealing ring is slightly smaller than the inner diameter of the thin-walled nickel alloy tube, facilitating the fitting of the tube onto the ring. An annular pressure chamber is located inside the hydraulic expansion sealing ring, connected to an external hydraulic power unit via a high-pressure oil pipe. Upon startup, the hydraulic power unit injects hydraulic oil into the annular pressure chamber. The pressure of the hydraulic oil causes the high-elasticity alloy core to expand radially, thereby compressing the oil-resistant rubber layer to tightly adhere to the inner wall of the thin-walled nickel alloy tube. Under radial pressure, the oil-resistant rubber layer undergoes elastic deformation, filling the microscopic gap between the hydraulic expansion sealing ring and the inner wall of the thin-walled nickel alloy tube, forming a preliminary sealing interface. To further enhance the long-term stability of the sealing interface and enable condition monitoring, a multi-layer graphene coating is applied to the oil-resistant rubber layer contact surface of the hydraulic expansion sealing ring using a chemical vapor deposition process. This multi-layer graphene coating possesses an extremely low coefficient of friction and excellent self-lubricating properties, reducing wear during relative movement between the hydraulic expansion sealing ring and the thin-walled nickel alloy tube. The multi-layer graphene coating also exhibits a piezoresistive effect, meaning its resistance changes with the normal pressure on the contact surface. A miniature resistance measurement circuit is integrated into the end of the hydraulic expansion sealing ring, electrically connected to the multi-layer graphene coating via a microelectrode embedded in the oil-resistant rubber layer. The resistance measurement circuit monitors the resistance of the multi-layer graphene coating in real time, and changes in resistance are converted into changes in the real-time coefficient of friction at the sealing interface. These changes in the coefficient of friction reflect the contact state of the sealing interface, such as the integrity of the lubricating film and the presence of abnormal wear or stick-slip phenomena.

[0064] The electrical signal indicating the change in the friction coefficient is transmitted to the system's central controller, which has a preset friction coefficient threshold range corresponding to a safe sealing state. The central controller compares the real-time change in the friction coefficient with the preset threshold range. When the real-time change in the friction coefficient exceeds the upper limit of the preset threshold range, it indicates that the sealing interface may be too tight or there is a risk of dry friction. The central controller then sends a command to the hydraulic power unit to reduce the hydraulic expansion pressure supplied to the annular pressure chamber. The reduction in hydraulic expansion pressure decreases the radial preload, and the friction coefficient decreases accordingly. When the real-time change in the friction coefficient falls below the lower limit of the preset threshold range, it indicates that the sealing interface may have insufficient preload and a potential leakage risk. The central controller then instructs the hydraulic power unit to increase the hydraulic expansion pressure and increase the radial preload. This dynamic adjustment based on the change in the friction coefficient is a continuous process, aiming to maintain the friction state of the sealing interface within an optimal range, thereby ensuring that the leakage rate of the closed hydrostatic chamber remains below the preset safety threshold throughout the hydrostatic test.

[0065] The acquisition of strain distribution data on the tube surface was accomplished by deploying a distributed fiber optic sensor array. The distributed fiber optic sensor array used standard communication-grade single-mode fiber as the sensing element. The single-mode fiber was wound around the outer surface of the thin-walled nickel alloy tube in a constant helical trajectory. The winding process was executed by a precision CNC winding machine, which controlled the rotational speed of the thin-walled nickel alloy tube and the feed speed of the single-mode fiber through a program, achieving precise adjustment of the helical winding spacing. The helical winding spacing could be set according to the spatial resolution requirements of the experiment, typically adjustable between 1 mm and 10 mm. The beginning and end of the single-mode fiber were connected to the optical output port and optical input port of a Brillouin optical time-domain reflectometry (OTDR) analyzer via fiber optic patch cords, respectively. The ORT analyzer injected a narrow-linewidth pulsed laser beam into the single-mode fiber, which generated spontaneous backscattering as it propagated within the fiber. When the thin-walled nickel alloy tube was subjected to internal water pressure, it deformed, causing corresponding axial strain in the single-mode fiber tightly attached to its surface. Axial strain in a single-mode fiber alters its internal elasto-optic effect, causing a shift in the center frequency of the backscattered Brillouin light, known as the Brillouin shift. The amount of the Brillouin shift exhibits a strong linear relationship with the axial strain experienced by the single-mode fiber. A Brillouin optical time-domain reflectometry (BDR) analyzer determines the location of strain occurrence by measuring the return time of the backscattered Brillouin light signal and determines the strain value at that location by detecting the magnitude of the Brillouin shift. Since the single-mode fiber is helically wound, each measurement point on the fiber simultaneously contains information about the axial and circumferential deformation of the thin-walled nickel alloy tube. Through spatial geometry, the strain values ​​measured along the helix are decomposed and transformed into the axial and circumferential coordinates of the thin-walled nickel alloy tube.

[0066] The Brillouin optical time-domain reflectometry (OTDR) analyzer continuously measures along a single-mode fiber, acquiring strain data at tens of thousands of spatial points. These massive amounts of discrete strain data are transmitted to a data processing computer. Based on the winding parameters of the single-mode fiber and the geometry of the thin-walled nickel alloy tube, the computer establishes a mapping from helical coordinates to two-dimensional unfolded coordinates on the tube surface. Using an interpolation algorithm, the discrete measurement data is reconstructed to generate a continuous strain distribution matrix covering the entire surface of the thin-walled nickel alloy tube. The rows and columns of the strain distribution matrix correspond to the axial position and circumferential angle of the thin-walled nickel alloy tube, respectively, and each element in the matrix represents the strain value at that location. The Brillouin OTD analyzer, after calibration and optimization, achieves sub-millimeter spatial resolution, meaning that the strain distribution matrix can capture very subtle strain gradient changes on the surface of the thin-walled nickel alloy tube.

[0067] Example 2: The full-surface strain distribution matrix acquired and generated by the distributed fiber optic sensor array serves as the fundamental input data for the three-dimensional stress field reconstruction model. The full-surface strain distribution matrix is ​​a digital matrix containing axial coordinates, circumferential coordinates, and corresponding strain values. The data processing computer imports the geometric model of the thin-walled nickel alloy tube into the finite element analysis software, generating a dense two-dimensional mesh on the surface of the tube. The mesh nodes correspond to the coordinates of the data points in the full-surface strain distribution matrix. Each mesh node is assigned a strain value obtained from the full-surface strain distribution matrix using nearest-neighbor interpolation. This process discretizes the continuous experimental measurement data into the initial node conditions required for finite element analysis. For regions where the data point density in the full-surface strain distribution matrix is ​​higher than the finite element mesh node density, the strain values ​​from multiple measurement points are fused using the arithmetic mean method and assigned to a single mesh node. For regions where the data point density is lower than the mesh node density, the strain value of that node is temporarily left empty for subsequent interpolation processing. An elastoplastic constitutive equation was established based on the material property parameters of a thin-walled nickel alloy tube. These parameters, obtained through independent material mechanical property tests, include Young's modulus, Poisson's ratio, yield strength, hardening exponent, and the Hill yield criterion parameters describing the anisotropic behavior of the material. The elastoplastic constitutive equation defines the linear relationship between stress and strain in the elastic stage and the nonlinear stress-strain relationship considering the material hardening effect in the plastic stage. In the finite element analysis software, discretized nodal strain data were loaded as boundary conditions onto the mesh model of the thin-walled nickel alloy tube surface. The solver, based on the principle of virtual work and the elastoplastic constitutive equation, iteratively calculated each mesh node to solve the nodal force balance equations. The calculation outputs the complete stress tensor at each mesh node, including the normal stress along the axial direction of the thin-walled nickel alloy tube, the circumferential normal stress, and the in-plane shear stress components. The calculated stress tensor results visually reflect the macroscopic stress distribution on the surface of the thin-walled nickel alloy tube under specific hydraulic loads.

[0068] Because the distributed fiber optic sensor array uses a helical winding method, and because there may be occlusion or sensor failure areas on the surface of the thin-walled nickel alloy tube, some nodes in the discretized finite element mesh lack direct experimental strain data. To reconstruct a complete and continuous three-dimensional stress field, it is necessary to estimate the strain values ​​of these blank nodes. The system uses the Kriging interpolation algorithm to complete this spatial interpolation task. The Kriging interpolation algorithm is an optimal unbiased estimation method based on statistical theory. It first calculates a semi-variogram based on the strain values ​​of known mesh nodes. The semi-variogram describes the spatial correlation of strain values ​​as a function of distance. Based on the fitted semi-variogram model, the Kriging interpolation algorithm calculates the weighting coefficients between each blank node to be interpolated and its surrounding known nodes. These weighting coefficients consider not only distance factors but also the spatial distribution structure of the known data. The calculated weighting coefficients are used to perform a weighted average of the strain values ​​of the known nodes to obtain the optimal estimate of the strain values ​​of the blank nodes. After filling the finite element mesh with the estimated values ​​of all blank nodes, stress calculation based on the elastoplastic constitutive equation is performed again, ultimately generating a seamless three-dimensional stress field cloud map covering the entire surface of the thin-walled nickel alloy tube.

[0069] The determination of the equivalent plastic deformation threshold relies on the three-dimensional stress field contour map output by the three-dimensional stress field reconstruction model. The algorithm identifies the spatial distribution of the maximum principal stress from the three-dimensional stress field contour map; the maximum principal stress is an important indicator describing the degree of stress severity of the material. By tracing the contour lines of the maximum principal stress, a trajectory line of the maximum principal stress running through the surface of the thin-walled nickel alloy tube is extracted. This trajectory line is the path for the initiation and development of potential plastic deformation. Along the trajectory line of the maximum principal stress, the trajectory line is discretized into a series of continuous points. For each point on the trajectory line, its stress tensor components and the plastic strain increment calculated through constitutive relations are read from the three-dimensional stress field contour map. The calculation of the plastic strain energy density is based on plasticity mechanics theory, and the formula involves the inner product operation of the stress tensor and the plastic strain increment. The plastic strain energy density of all discrete points on the trajectory line is numerically integrated, with the integration along the arc length of the trajectory line, to obtain the cumulative plastic strain energy density from the starting point of the trajectory line to the current point. The cumulative plastic strain energy density characterizes the plastic deformation work absorbed by the material during deformation. The material hardening curve of the thin-walled nickel alloy tube is obtained through a uniaxial tensile test. The curve is plotted with true stress on the ordinate and true plastic strain on the abscissa. The curve typically includes a distinct elastic segment, a yield point, and a subsequent plastic hardening segment. In the plastic hardening segment, the curve slope gradually decreases, and there is an inflection point marking the material's deformation limit. After this inflection point, the material enters an unstable deformation stage. The system compares the calculated cumulative plastic strain energy density along the maximum principal stress trajectory with the material hardening curve in real time. The comparison process involves mapping the cumulative plastic strain energy density value onto the area integral under the material hardening curve. When the cumulative plastic strain energy density value obtained by integrating along the trajectory reaches the plastic strain energy density value corresponding to the inflection point of the material hardening curve, the system determines that the thin-walled nickel alloy tube has entered a critical plastic state. At this time, the system records the current internal water pressure value applied by the hydrostatic testing system. This water pressure value is defined as the equivalent plastic deformation threshold of the thin-walled nickel alloy tube under the current loading conditions. The equivalent plastic deformation threshold, as a key control parameter, will be used to guide the generation of subsequent dynamic hydraulic loading commands in order to achieve precise control over the plastic deformation process of thin-walled nickel alloy tubes.

[0070] Example 3: See Figure 3This paper describes the generation logic of dynamic hydraulic loading commands and the execution mechanism of a high-frequency pressure pulse generator in a hydrostatic testing system for thin-walled nickel alloy tubes. The generation of dynamic hydraulic loading commands begins with the equivalent plastic deformation threshold obtained by the system. This threshold, a key parameter calculated from a three-dimensional stress field reconstruction model, marks the critical pressure point at which the thin-walled nickel alloy tube enters plastic deformation. The generation logic divides the entire pressure range from zero pressure to the equivalent plastic deformation threshold into multiple continuous hydraulic loading intervals. The number of intervals depends on the length-to-diameter ratio of the thin-walled nickel alloy tube, typically set to five to ten intervals to ensure gradual pressure changes. The pressure span of each hydraulic loading interval can be uniformly distributed or non-uniformly distributed based on historical test data. Non-uniform distribution usually uses smaller pressure intervals when the pressure approaches the equivalent plastic deformation threshold to capture subtle material responses. The allocation of loading duration within each hydraulic loading interval is a dynamic process. The system calls pre-stored thin-walled nickel alloy tube wall thickness uniformity parameters, obtained by scanning the entire tube surface with an ultrasonic thickness gauge, and represented as a set of wall thickness measurements at different axial and circumferential positions. For areas where the wall thickness measurement shows a thinner wall thickness, the system automatically allocates a longer loading time to the corresponding water pressure loading range. The extended loading time slows down the rate of pressure rise, reducing the risk of local plastic deformation caused by stress concentration. For areas with a high uniformity of wall thickness measurement, the system allocates a shorter loading time, allowing the pressure to pass through the range quickly to improve test efficiency.

[0071] The hydraulic loading curve needs to smoothly connect the pressure gradients of each hydraulic loading interval to avoid impact damage to the thin-walled nickel alloy tube caused by pressure abrupt changes. The system uses an S-curve function to achieve a smooth transition of pressure changes. The S-curve function is a mathematical function whose graph presents a smooth S-shape, capable of converting a linear pressure gradient into a continuously differentiable curve. The algorithm for generating dynamic hydraulic loading commands uses the start and end pressures of each hydraulic loading interval as nodes and applies the S-curve function for interpolation. The mathematical expression of the S-curve function is as follows:

[0072]

[0073] in: Representing time Instantaneous pressure value at time, This represents the upper limit pressure value of the current water pressure loading range. This parameter represents the rate of pressure increase and controls the steepness of the curve. The time parameter representing the center of pressure growth. Represents a time variable. Parameter and The value of is dynamically calculated based on the pressure span and allocated loading duration of the water pressure loading interval, ensuring the continuity of the first derivative at the connection between adjacent intervals. The generation algorithm traverses all water pressure loading intervals and outputs a continuous and smooth water pressure loading curve. This curve is the core content of the dynamic water pressure loading command and is stored in the system controller in the form of a digital signal.

[0074] The high-frequency pressure pulse generator is responsible for physically executing the dynamic water pressure loading command. Its core components include a servo motor, a precision plunger pump, and a pressure feedback unit. The servo motor is a permanent magnet synchronous motor, with its speed controlled by a frequency converter. The output shaft of the servo motor is directly connected to the crankshaft of the precision plunger pump via a coupling. The precision plunger pump contains multiple piston cylinders, and the pistons reciprocate under the drive of the crankshaft, generating periodic pressure pulses. The system controller converts the instantaneous slope parameter of the dynamic water pressure loading command into the servo motor's speed command in real time. The instantaneous slope parameter is the water pressure loading curve at a given time point. first derivative The instantaneous slope parameter represents the rate of pressure change. When the dynamic hydraulic loading command requires a rapid pressure increase, the instantaneous slope parameter value is large. The system controller outputs a high-frequency pulse signal to the frequency converter driver of the servo motor. The servo motor accelerates its rotation, increasing the stroke frequency of the precision plunger pump, thereby pumping more hydraulic oil into the closed hydraulic chamber per unit time, generating high-frequency, high-pressure pulses. When the dynamic hydraulic loading command requires a stable or slow pressure change, the instantaneous slope parameter value is small. The servo motor slows down, the stroke frequency of the precision plunger pump decreases, and the pressure pulse amplitude decreases.

[0075] The quality of the pressure pulse waveform is crucial for test accuracy, especially the steepness of the pressure pulse rise edge, which needs precise control. The rise edge steepness is defined as the reciprocal of the time required for the pressure to rise from 10% to 90% of its rated value. A high-frequency pressure pulse generator integrates a piezoelectric ceramic sensor for real-time monitoring of the pressure pulse waveform. The piezoelectric ceramic sensor is installed near the output port of the precision plunger pump, directly sensing the pressure fluctuations of the hydraulic oil. The piezoelectric ceramic sensor converts the pressure signal into a charge signal, which is amplified by a charge amplifier and then sent to a high-speed data acquisition card. The data acquisition card records the waveform of pressure changing over time at a megahertz sampling rate. The system controller extracts the measured rise edge steepness value from the acquired waveform data and compares it with the expected steepness value implicit in the dynamic water pressure loading command. The expected steepness value is derived from the curve parameters of the dynamic water pressure loading command. If the measured rise edge steepness value deviates from the expected value, the system controller generates a correction signal. This correction signal is calculated using a proportional-integral algorithm to adjust the servo motor speed command. The calibration process constitutes a closed-loop control, ensuring that the actual pressure pulse waveform closely tracks the requirements of the dynamic water pressure loading command, thereby reducing system response lag and overshoot.

[0076] The connecting pipeline between the high-frequency pressure pulse generator and the closed hydrostatic chamber uses high-pressure stainless steel tubing with an optimized inner diameter to reduce fluid resistance and resonance effects. A buffer tank and a pulsation damper are installed on the pipeline; the buffer tank absorbs pressure spikes, and the pulsation damper smooths pressure fluctuations, ensuring a pure pressure pulse waveform input to the thin-walled nickel alloy tube. Throughout the process, the system controller continuously monitors the operating status of the high-frequency pressure pulse generator, including parameters such as servo motor current, precision plunger pump oil temperature, and peak pipeline pressure. These parameters are fed back to the safety monitoring unit via sensors. If any parameter exceeds the preset safety range, the safety monitoring unit immediately triggers an emergency shutdown procedure, cutting off the servo motor power and opening the pressure relief valve to protect the thin-walled nickel alloy tube and the test system from damage. The generation of dynamic hydrostatic loading commands and the execution of the high-frequency pressure pulse generator together achieve precise and controllable loading for the hydrostatic test of the thin-walled nickel alloy tube, providing a stable macroscopic mechanical environment for subsequent microscopic data acquisition.

[0077] Example 4: The process of synchronously triggering the X-ray diffractometer to collect lattice distortion data of the thin-walled nickel alloy tube in the hydrostatic testing system of the thin-walled nickel alloy tube. The data acquisition process of the X-ray diffractometer is strictly synchronized with the dynamic hydrostatic load applied by the high-frequency pressure pulse generator. A rectangular area is selected on the outer surface of the thin-walled nickel alloy tube as the diffraction analysis area. The size of the diffraction analysis area is determined according to the X-ray beam spot size and the stepper motor movement range. A laser positioning instrument is used to divide the diffraction analysis area into a regular diffraction grid. The grid consists of an M-row N-column matrix of measurement points, with each measurement point representing an independent X-ray diffraction measurement position. The row and column spacing between measurement points is set according to the curvature of the thin-walled nickel alloy tube and the required spatial resolution, and the spacing value is usually between 0.1 mm and 1 mm. The goniometer of the X-ray diffractometer is equipped with a high-precision stepper motor-driven two-dimensional moving platform. The two-dimensional moving platform carries the X-ray source and detector components and can automatically position itself to each measurement point according to the preset grid coordinate sequence.

[0078] The triggering time of X-ray diffraction data acquisition is locked with a specific phase of the pressure pulse waveform of the high-frequency pressure pulse generator. The system uses a hardware trigger signal to achieve precise synchronization. The pressure sensor of the high-frequency pressure pulse generator outputs an analog voltage signal, which is converted into digital pressure waveform data by an analog-to-digital converter. A real-time processor continuously monitors the pressure waveform data and identifies the trough point of each pressure pulse cycle. The trough point is defined as the local minimum point of the pressure value within a cycle. Near the trough point, the pressure change is gradual, the impact effect of fluid dynamic load on the thin-walled nickel alloy tube is minimal, and the thin-walled nickel alloy tube is in a quasi-static stress state. After detecting the trough point, the real-time processor immediately generates a transistor-to-transistor logic level trigger pulse signal, which is transmitted to the external trigger input port of the X-ray diffractometer via a coaxial cable. After receiving the trigger pulse signal, the X-ray diffractometer immediately starts the diffraction angle scanning sequence of the current measurement point. The X-ray diffractometer presets the diffraction angle scanning range according to the crystal structure characteristics of the thin-walled nickel alloy tube, and the scanning range covers the expected angle range of the target diffraction peak. For nickel-based alloys, the diffraction peaks corresponding to the (111) crystal plane family are usually selected for measurement. The X-ray source uses a copper target Kα beam with a wavelength of approximately 0.154 nm. The detector scans a preset angular range at a fixed angular step speed, recording the diffraction intensity at each angle. Diffraction patterns acquired during the pressure trough phase exhibit low background noise and clear diffraction peaks. The acquired raw diffraction pattern data is temporarily stored in the X-ray diffractometer's local cache. After each measurement point is acquired, the X-ray diffractometer sends a completion signal to the system controller, which instructs the two-dimensional moving platform to move to the next measurement point coordinate in the grid. Simultaneously, the real-time processor continues to monitor the pressure waveform, waiting for the next pressure trough to generate a new trigger pulse. This process is repeated until all measurement points within the diffraction region grid have completed data acquisition.

[0079] Table 1: Lattice Distortion Data Set

[0080]

[0081] The data from all measurement points are aggregated to form a lattice distortion dataset, which is a data table containing spatial coordinates and various microstructural parameters. Refer to Table 1 for the structure of the lattice distortion dataset. The dislocation density ρ in the lattice distortion dataset is calculated through the diffraction peak broadening effect. The Williamson-Hall method or the more precise Warren-Avbach method is used to separate the broadening contributions caused by grain refinement and microstrain from the full width at half maximum (FWHM) of the diffraction peaks, and then the dislocation density is calculated based on the strain broadening components. The analysis of twin boundary orientation difference is based on the changes in diffraction peak shape. When annealed twins are present in the material, the diffraction peaks will exhibit splitting or asymmetry. By analyzing the sub-peak positions and intensities of the diffraction peaks, the relative orientation difference between the grains on both sides of the twin boundary can be determined. Each data row in the lattice distortion dataset corresponds to a specific spatial location in the diffraction region grid, containing the microstrain, defect density, and interface information of that point under a specific hydrostatic load. The lattice distortion dataset is transmitted to the central data processing server via a data bus. The central data processing server performs a quality check on the dataset, including data integrity, diffraction peak goodness of fit, and the reasonable range of parameter values. For abnormal measurement points with goodness of fit below a threshold, the central data processing server marks their data status as suspicious and assigns them lower weights or performs interpolation compensation in subsequent analyses. The quality-checked lattice distortion dataset is converted into a standard format file and stored in the system database. The lattice distortion dataset provides direct experimental input for the material micro-damage evolution model. The model uses lattice constant offset to invert the micro-stress field, uses dislocation density to assess the degree of plastic damage accumulation, and uses twin boundary orientation difference to identify interface locations where micro-cracks are prone to nucleation. The entire acquisition process precisely correlates macroscopic pressure loading with microstructural response measurements in time and space, achieving in-situ characterization of material micro-behavior under service simulation conditions. The X-ray diffractometer's optical path system is equipped with a collimator and a Sola slit to reduce diffraction geometry errors, and the X-ray detector uses a high-resolution solid-state detector to improve angle measurement accuracy.

[0082] Example 5: The lattice distortion dataset contains the spatial coordinates, lattice constant offset, and dislocation density of each measurement point in the diffraction region grid on the surface of a thin-walled nickel alloy tube. The output of the three-dimensional stress field reconstruction model is a three-dimensional stress field cloud map covering the entire surface of the thin-walled nickel alloy tube. The three-dimensional stress field cloud map is composed of finite element mesh elements, each of which stores stress tensor components. The data fusion module first performs spatial registration, mapping the spatial coordinates of each measurement point in the lattice distortion dataset to the corresponding finite element mesh element in the three-dimensional stress field cloud map. For measurement points that are exactly located on a mesh node, their lattice distortion data are directly assigned to that node; for measurement points located inside a mesh element, an inverse distance-weighted interpolation algorithm is used to distribute the lattice distortion data of the measurement point to the mesh element nodes surrounding the measurement point according to distance weights. After registration, each finite element mesh node not only has macroscopic stress data but also is associated with microscopic lattice distortion data from X-ray diffraction measurements. The material micro-damage evolution model calculates the critical shear stress for each grid node based on dislocation dynamics theory, which describes the motion, multiplication, and interaction of dislocations in crystalline materials. The model treats each grid node as a representative volumetric unit, whose microstructure is characterized by the associated dislocation density parameter. The critical shear stress is the shear stress required for dislocations to begin large-scale slip, and its magnitude is related to the dislocation density, lattice friction stress, and the strength of inter-dislocation interactions. The model uses a constitutive relation that includes a dislocation density term to calculate the critical shear stress, considering strengthening mechanisms such as dislocation tangles and dislocation cell structure formation. For each grid node, its associated dislocation density value and the local stress state from a 3D stress field contour plot are input, and the model outputs the calculated critical shear stress for that node under the current load conditions. The calculated critical shear stress reflects the material's microstructure's ability to resist further plastic deformation.

[0083] After obtaining the calculated critical shear stress values ​​for all mesh nodes, the material micro-damage evolution model employs the phase-field method to simulate the evolution of micro-damage. The phase-field method describes the microstructural evolution of the material system by introducing a set of continuous field variables, avoiding explicit tracking of complex interfaces. The model defines two phase-field variables: one phase-field variable... The order parameter represents the complete material phase and takes a value between 0 and 1. This indicates that the materials are intact. Indicates complete material damage; another phase field variable Used to distinguish different crystal orientations and simulate the effects of grain boundaries and twin boundaries. The governing equations for phase field evolution are a set of coupled nonlinear partial differential equations, including Ginzburg-Landau equations and mechanical equilibrium equations. The Ginzburg-Landau equations control the dynamic process of phase field variables evolving over time, while the mechanical equilibrium equations ensure that the simulation satisfies stress equilibrium conditions. The driving force for phase field simulation comes from the mechanochemical potential, which consists of the elastic strain energy density and the chemical potential energy associated with the phase field variables. The model uses the calculated critical shear stress value for each grid node as a threshold condition for local damage evolution. The simulation is performed in the time domain, with each time step representing a small increment of load. At each time step, the model checks whether the equivalent stress of each grid node exceeds its calculated critical shear stress value. For nodes where the equivalent stress exceeds the calculated critical shear stress value, the mechanochemical potential drives the phase field variables. The evolution from 1 to 0 is controlled by the phase field dynamics coefficients. Phase field variables. The decrease in phase field variable φ simulates the nucleation process of microcracks. Simultaneously, the model considers twin boundary orientation difference information provided by the lattice distortion dataset, pre-defining the twin boundary positions in the phase field variable φ settings, and adjusting the phase field variable at the twin boundaries. A sudden change occurs. In the simulation, microcracks tend to move along... The gradient extends in the direction of maximum gradient and preferentially along predefined weak interfaces such as twin boundaries, because the energy required to extend along these interfaces is lower.

[0084] Phase-field simulations are performed on a high-performance computing cluster. The computational domain is discretized into a fine mesh, with mesh sizes much smaller than the feature sizes of the microstructures. The simulation continues until a preset number of load steps is reached or the damage area expands to a certain scale. The final output of the simulation is a spatial distribution map of micro-defects, which displays the phase-field variables within a specified region on the surface of the thin-walled nickel alloy tube in two-dimensional or three-dimensional cloud map form. The distribution of . Regions with values ​​close to 0 are identified as micro-defect clusters, and are typically marked with different color depths in the spectrum. The magnitude of the value visually represents the nucleation location, size, and propagation path of microcracks. The spatial distribution map of micro-defects also contains quantitative data, such as the area, perimeter, and orientation of each defect region. The implementation of the closed-loop feedback control hydrostatic test system is based on the spatial distribution map of micro-defects output by the material micro-damage evolution model. The system extracts the spatial coordinates of defect clusters from the spatial distribution map. The criterion for determining defect clusters is that the average value of the phase field variable η is lower than a preset threshold (e.g., ...). The extracted coordinate information is fed back into the 3D stress field reconstruction model. In the 3D stress field reconstruction model, the finite element mesh elements corresponding to the coordinates of the defect accumulation area are located. The material constitutive parameters of these mesh elements are corrected, with a key focus on introducing an elastic modulus attenuation coefficient ξ. Elastic modulus attenuation coefficient ξ is a dimensionless number between 0 and 1, representing the degree of material stiffness degradation caused by the presence of microscopic defects. The assignment rule for the elastic modulus attenuation coefficient ξ is based on the average value of η within the defect cluster region; the lower the η value (the more severe the damage), the smaller the elastic modulus attenuation coefficient ξ. When the system is subjected to hydraulic loading again, the three-dimensional stress field reconstruction model uses the updated material constitutive parameter library for calculation. Due to the reduced equivalent elastic modulus in the defect region, these regions will exhibit higher calculated strain under the same load, thus more accurately predicting stress concentration and potential further damage evolution. This process, from microscopic damage detection to microscopic damage evolution simulation, to macroscopic constitutive parameter correction, and finally to a new round of macroscopic stress prediction, constitutes a complete closed loop. The system continuously conducts iterative experiments, and the material constitutive parameter library is updated after each hydraulic loading and microscopic measurement cycle. The iteratively optimized material constitutive parameter library continuously enhances the predictive ability of the three-dimensional stress field reconstruction model as the experiment progresses, enabling it to more accurately reflect the true mechanical response and damage evolution process of thin-walled nickel alloy tubes under complex loads.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A thin walled nickel alloy tube hydrostatic testing system characterized by, The application relates to a method for testing the plastic deformation threshold of a thin-walled nickel alloy pipe under a water pressure. The method comprises the following steps: A distributed fiber sensor array is used to collect the strain distribution data of the pipe body surface under the axial loading state of the thin-walled nickel alloy pipe, including: A single-mode optical fiber is spirally wound along the axial direction of the thin-walled nickel alloy pipe to form a spiral sensing network with adjustable spacing; The circumferential strain gradient of the pipe body is captured through the spiral sensing network, and a full-surface strain distribution matrix is generated in combination with the axial strain data; The spatial resolution of the full-surface strain distribution matrix is analyzed to be sub-millimeter level by using the Brillouin optical time domain reflection technology; A three-dimensional stress field reconstruction model is constructed based on the strain distribution data of the pipe body surface, and the equivalent plastic deformation threshold of the thin-walled nickel alloy pipe under different water pressure gradients is calculated through the three-dimensional stress field reconstruction model; The slope parameter of the water pressure loading curve is adjusted according to the equivalent plastic deformation threshold to generate a dynamic water pressure loading instruction; The dynamic water pressure loading instruction is executed through a high-frequency pressure pulse generator, and an X-ray diffractometer is synchronously triggered to collect the lattice distortion data of the thin-walled nickel alloy pipe; The lattice distortion data is input into a material micro-damage evolution model to output a micro-defect spatial distribution map of the thin-walled nickel alloy pipe; The material constitutive parameters of the three-dimensional stress field reconstruction model are corrected based on the micro-defect spatial distribution map to form a closed-loop feedback control water pressure test system; The calculation process of the three-dimensional stress field reconstruction model comprises the following steps: The full-surface strain distribution matrix is discretized into finite element grid node data; An elastic-plastic constitutive equation is established based on the anisotropic parameters of the thin-walled nickel alloy pipe to solve the stress tensor of the grid node; The stress field of the region without fiber arrangement is reconstructed through a Kriging interpolation algorithm to output a complete three-dimensional stress field cloud chart; The determination method of the equivalent plastic deformation threshold comprises the following steps: A maximum principal stress trajectory line is extracted from the three-dimensional stress field cloud chart; The plastic strain energy density is integrated and accumulated along the maximum principal stress trajectory line; When the accumulated plastic strain energy density reaches the inflection point of the material hardening curve, the current water pressure value is marked as the equivalent plastic deformation threshold; The generation logic of the dynamic water pressure loading instruction comprises the following steps: The equivalent plastic deformation threshold is taken as the upper limit to divide multiple water pressure loading intervals; The wall thickness uniformity parameters of the thin-walled nickel alloy pipe are used to allocate the loading time length of each interval; The pressure gradient of adjacent intervals is smoothly connected by using an S-shaped curve to generate a continuous and adjustable water pressure loading curve; The execution of the dynamic water pressure loading instruction through the high-frequency pressure pulse generator comprises the following steps: A millisecond pressure pulse is generated by a plunger pump driven by a servo motor; The stroke frequency of the plunger pump is adjusted based on the slope parameter of the dynamic water pressure loading instruction; The rising edge steepness of the pulse waveform is fed back in real time by using a piezoelectric ceramic sensor to close-loop correct the rotating speed of the servo motor; The synchronous triggering of the X-ray diffractometer to collect the lattice distortion data of the thin-walled nickel alloy pipe comprises the following steps: A diffraction region grid is demarcated on the surface of the thin-walled nickel alloy pipe, and each grid point corresponds to an independent diffraction angle scanning range; The triggering time sequence of the high-frequency pressure pulse is synchronized, and the lattice constant offset is collected in the pressure valley stage; The dislocation density and twin boundary orientation difference are calculated based on the lattice constant offset to form a lattice distortion data set; The method for constructing the material micro-damage evolution model comprises the following steps: mapping the crystal lattice distortion data set to a corresponding three-dimensional stress field grid element; calculating the critical shear stress of each grid element based on dislocation dynamics theory; simulating the nucleation and expansion path of micro-cracks under the action of critical shear stress by a phase field method, and outputting a micro-defect spatial distribution map.

2. The thin walled nickel alloy tube hydrotest system of claim 1, wherein, The method for establishing a closed water pressure cavity by a ring-shaped sealing clamp comprises the following steps: setting a hydraulic expansion sealing ring at both ends of the thin-walled nickel alloy pipe, and applying a radial pre-tightening force through the hydraulic expansion sealing ring; adopting a multi-layer graphene coating to cover the contact surface of the hydraulic expansion sealing ring, and monitoring the friction coefficient change of the sealing interface in real time; dynamically adjusting the hydraulic expansion pressure according to the friction coefficient change, and maintaining the leakage rate of the closed water pressure cavity below a preset threshold.

3. The thin walled nickel alloy tube hydrotest system of claim 2, wherein, The implementation mode of the water pressure test system forming a closed loop feedback control comprises the following steps: extracting the coordinate information of the defect aggregation area from the micro-defect spatial distribution map; correcting the elastic modulus attenuation coefficient of the corresponding coordinates in the three-dimensional stress field reconstruction model; substituting the corrected elastic modulus attenuation coefficient into the stress field calculation of the next round of water pressure test, and forming an iteratively optimized material constitutive parameter library.

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