Method and device for estimating service life of turbine blade after repairing of liquid rocket engine

Abstract

The invention provides a life estimation method and device for a repaired turbine blade of a liquid rocket engine, and relates to the technical field of life estimation of repaired turbine blades, and the life estimation method comprises the following steps: obtaining a target test piece for cladding repair; performing a material performance test on the repaired target test piece to obtain a material performance parameter set of the repaired turbine blade of the liquid rocket engine at different temperature points and different strain amplitudes; determining a constitutive model of the repaired turbine blade based on the material performance parameter set and a hardening model combining Chboch follow-up hardening and Voce nonlinear isotropic hardening; working cycle simulation of the repaired turbine blade is carried out, stress-strain data of the turbine blade at the dangerous point position are obtained, accumulated damage of the turbine blade in operation of the liquid rocket engine is determined based on the stress-strain data, and then the cycle life of the turbine blade is calculated; the technical problem that due to the fact that the service life of the repaired turbine blade is unknown, liquid rocket application safety is affected is solved.

Description

Technical Field

[0001] This invention relates to the field of life prediction technology for repaired turbine blades, and in particular to a method and apparatus for predicting the life of repaired turbine blades in liquid rocket engines. Background Technology

[0002] Turbine blades, as crucial structural components of liquid rocket engines, operate in complex environments, subjected to continuous temperature and aerodynamic loads, as well as centrifugal loads generated by the high-speed rotation of the turbine, making them prone to fatigue fracture. Because blades are manufactured using precision casting, a complex process with a long production cycle, directly replacing damaged blades with brand-new ones would be prohibitively expensive. Laser cladding repair, due to its low cost and short manufacturing cycle, has become an important method for repairing damage.

[0003] However, in the field of liquid rocket engines, the lifespan of repaired turbine blades has not yet been considered, and the service life of repaired turbine blades cannot be guaranteed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for estimating the lifespan of turbine blades after repair of a liquid rocket engine, so as to alleviate the technical problem that the unknown lifespan of the repaired turbine blades affects the safety of liquid rocket applications.

[0005] In a first aspect, embodiments of the present invention provide a method for estimating the lifespan of turbine blades after repair of a liquid rocket engine, including: Obtain a target test piece of an unrepaired turbine blade for characterizing a liquid rocket engine, and perform cladding repair on the target test piece; Material property tests were conducted on the repaired target test piece to obtain a set of material property parameters for the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes. Based on the set of material performance parameters and the hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, the constitutive model of the repaired turbine blade is determined by the particle swarm optimization algorithm; wherein, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic load. Based on the constitutive model, a working cycle simulation was performed on the repaired turbine blade to obtain stress and strain data of the repaired turbine blade at the critical point. Based on the stress-strain data, the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine is calculated, and the cycle life of the repaired turbine blade is estimated.

[0006] Furthermore, the steps for obtaining target test specimens of unrepaired turbine blades for characterizing liquid rocket engines include: Finite element simulation was performed on the unrepaired turbine blades of a liquid rocket engine to obtain the stress gradient and stress values ​​at critical points of the unrepaired turbine blades. Based on the material performance test conditions, a first test piece was determined to be consistent with the stress gradient and stress value at the critical point location of the unrepaired turbine blade. The preset area range of the dangerous point location of the first test piece is removed; The heat dissipation base is determined based on the dimensions of the first test piece and the cladding test results; The first test piece is assembled with the heat dissipation base to obtain the target test piece of the unrepaired turbine blade.

[0007] Furthermore, the step of performing cladding repair on the target test piece includes: A substrate material corresponding to the target test piece is selected, and under the action of the target laser cladding process parameters, a preset area of ​​the dangerous point in the target test piece is clad and repaired; wherein, the target laser cladding process parameters are determined through orthogonal experimental results.

[0008] Further, the steps of conducting material property tests on the repaired target test piece to obtain a set of material property parameters for the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes include: A uniaxial tensile test was performed on the repaired target specimen to obtain the stress-strain curves of the repaired turbine blade at different temperature points. Cyclic loading tests were conducted on the repaired target specimen to obtain stable cyclic hysteresis curves of the turbine blade under different strain amplitudes.

[0009] Furthermore, based on the aforementioned set of material performance parameters and the hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, the step of determining the constitutive model of the repaired turbine blade using a particle swarm optimization algorithm includes: Using the set of material performance parameters, the constitutive model of the repaired turbine blade is constructed by fitting the target parameters in the hardening model obtained by combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening through particle swarm optimization algorithm.

[0010] Furthermore, the step of performing a working cycle simulation on the repaired turbine blade based on the constitutive model to obtain the stress and strain data of the repaired turbine blade at the critical point location includes: Flow field simulation was performed on the repaired turbine blade to obtain the temperature, pressure and convective heat transfer coefficient of the surface gas of the repaired turbine blade. Using the temperature of the surface gas and the convective heat transfer coefficient as heat loads, thermal analysis was performed on the repaired turbine blades to obtain the temperature distribution of the repaired turbine blades. Based on the constitutive model, under the conditions of the temperature distribution, pressure, and preset rotational speed, a three-dimensional finite element structural analysis is performed on the repaired turbine blade to obtain stress and strain data corresponding to the critical points of the repaired turbine blade.

[0011] Furthermore, the step of calculating the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on the stress-strain data, and estimating the cycle life of the repaired turbine blade, includes: Based on the stress-strain data, the low-cycle fatigue damage and ratchet damage of the repaired turbine blade are calculated respectively. The low-cycle fatigue damage and the ratchet damage are summed to obtain the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine. The cycle life of the repaired turbine blade is determined based on the time it takes for the accumulated damage to reach a preset cumulative damage threshold.

[0012] Secondly, embodiments of the present invention also provide a life prediction device for turbine blades after repair of a liquid rocket engine, comprising: The repair module acquires a target test piece of an unrepaired turbine blade used to characterize a liquid rocket engine, and performs cladding repair on the target test piece; The test module conducts material property tests on the repaired target test piece to obtain a set of material property parameters of the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes. The determination module, based on the set of material performance parameters and a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, determines the constitutive model of the repaired turbine blade using a particle swarm optimization algorithm; wherein, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic loading. The simulation module performs a working cycle simulation on the repaired turbine blade based on the constitutive model to obtain stress and strain data of the repaired turbine blade at the critical point. The estimation module calculates the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on the stress and strain data, and estimates the cycle life of the repaired turbine blade.

[0013] Thirdly, an embodiment provides an electronic device including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method described in any of the foregoing embodiments.

[0014] Fourthly, an embodiment provides a machine-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the steps of the method described in any of the foregoing embodiments.

[0015] This invention provides a method and apparatus for predicting the lifespan of a repaired turbine blade in a liquid rocket engine. First, a target test specimen of an unrepaired turbine blade of a liquid rocket engine is designed and constructed. This specimen undergoes cladding repair treatment, followed by material performance testing to obtain the material performance parameters of the repaired turbine blade. Based on these material performance parameters, target parameters are fitted to a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening to obtain a constitutive model characterizing the stress-strain response of the repaired turbine blade under cyclic loading. Then, based on this constitutive model, operational cycle simulations are performed on the repaired turbine blade to obtain relatively accurate stress-strain data at critical locations. Based on this stress-strain data, the cumulative damage that the repaired turbine blade will cause when applied to a liquid rocket engine can be predicted, and the cyclic lifespan of the repaired turbine blade can be estimated, ensuring the safety and reliability of liquid rocket engines using such repaired turbine blades.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for estimating the lifespan of a repaired turbine blade in a liquid rocket engine, provided as an embodiment of the present invention. Figure 2 A flowchart of a method for determining a target test specimen provided in an embodiment of the present invention; Figure 3 A three-dimensional finite element structural analysis model of a repaired turbine blade is provided in this embodiment of the invention. Figure 4 A schematic diagram of a target test specimen provided in an embodiment of the present invention; Figure 5 A schematic diagram of the functional modules of a liquid rocket engine turbine blade life prediction device after repair, provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware architecture of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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, 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.

[0021] The inventors discovered that there is currently a lack of lifespan prediction for repaired turbine blades, which makes it difficult to fully guarantee the reliability of liquid rocket engines.

[0022] Based on this, the present invention provides a method and apparatus for estimating the lifespan of turbine blades after repair of liquid rocket engines, which can estimate the lifespan of the repaired turbine blades and replace them in time before their lifespan is reached, so as to ensure the safety and reliability of the liquid rocket engines in which they are used.

[0023] To facilitate understanding of this embodiment, a method for estimating the lifespan of turbine blades after repair of a liquid rocket engine, as disclosed in this embodiment of the invention, will be described in detail first. This method can be applied to intelligent control devices such as host computers, servers, and controllers.

[0024] Figure 1 A flowchart illustrating a method for estimating the lifespan of a repaired turbine blade in a liquid rocket engine, provided as an embodiment of the present invention.

[0025] Reference Figure 1 This method mainly includes the following steps: Step S102: Obtain the target test piece of the unrepaired turbine blade used to characterize the liquid rocket engine, and perform cladding repair on the target test piece.

[0026] Here, the target test piece is designed based on an unrepaired turbine blade of a liquid rocket engine. After cladding repair, the repaired turbine blade of the liquid rocket engine can be characterized in order to obtain the material property test results of the repaired turbine blade.

[0027] Step S104: Conduct material property tests on the repaired target test piece to obtain a set of material property parameters of the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes.

[0028] The set of material performance parameters at different temperatures and strain amplitudes can be understood as the material mechanical performance parameter curves corresponding to different temperatures and strain amplitudes, in order to characterize the results of material performance tests on the repaired turbine blades.

[0029] Step S106: Based on the set of material performance parameters and the hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, the constitutive model of the repaired turbine blade is determined by the particle swarm optimization algorithm.

[0030] The Chaboche model is a multi-component nonlinear kinematic hardening model, mainly used in simulation analysis to model the cyclic hardening or softening behavior of materials. In this embodiment, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic loading. By fitting the target parameters of the constitutive model based on the set of material performance parameters obtained from the material performance tests in the aforementioned embodiments, the constitutive model corresponding to the repaired turbine blade can be accurately determined, enabling subsequent steps to predict the lifespan of the repaired turbine blade.

[0031] Step S108: Based on the constitutive model, perform working cycle simulation on the repaired turbine blade to obtain stress and strain data of the repaired turbine blade at the critical point.

[0032] Here, the stress-strain response of the repaired turbine blade under cyclic load is considered. By performing working cycle simulation on the repaired turbine blade, more accurate stress-strain data can be obtained.

[0033] Step S110: Calculate the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on stress and strain data, and estimate the cycle life of the repaired turbine blade.

[0034] It should be noted that this method uses stress and strain data from the critical point, i.e. the location most prone to damage, to predict the cumulative damage that the repaired turbine blade will cause when applied to a liquid rocket engine, thereby estimating the cycle life of the repaired turbine blade and ensuring its reliable application.

[0035] In a preferred embodiment for practical application, firstly, a target test specimen of an unrepaired turbine blade for a liquid rocket engine is designed and constructed. This specimen undergoes cladding repair treatment, followed by material performance testing to obtain the material performance parameters of the repaired turbine blade. Based on these material performance parameters, target parameters are fitted to a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening to obtain a constitutive model characterizing the stress-strain response of the repaired turbine blade under cyclic loading. Then, based on this constitutive model, operational cycle simulations are performed on the repaired turbine blade to obtain relatively accurate stress-strain data at critical locations. This stress-strain data can predict the cumulative damage that the repaired turbine blade will cause when applied to a liquid rocket engine and estimate its cyclic life, ensuring the safety and reliability of liquid rocket engines using such repaired turbine blades.

[0036] In some embodiments, step S102 may involve the following steps to construct and design the target test specimen, enabling a more accurate characterization of the unrepaired turbine blades of a liquid rocket engine: Step 1.1) Perform finite element simulation on the unrepaired turbine blades of the liquid rocket engine to obtain the stress gradient and stress values ​​at the critical points of the unrepaired turbine blades.

[0037] First, finite element simulation of the unrepaired turbine blades of the liquid rocket engine is performed to obtain the stress values ​​and stress gradients at various locations on the turbine blades, and the locations with the highest stress values ​​are identified as the danger points.

[0038] Step 1.2): Based on the material performance test conditions, determine the first test piece that is consistent with the stress gradient and stress value at the critical point location of the unrepaired turbine blade; Here, the material performance test conditions can be understood as the equipment conditions for subsequent material performance tests. Based on the equipment type, fixture size, and other equipment information for subsequent material performance tests, the shape and size of the first test piece (cladding test piece) are designed, such as plate or rod. Then, a finite element simulation test is performed on the first test piece to compare the stress values ​​at the critical points of the first test piece with those at the critical points of the unrepaired turbine blade, and to compare the stress gradients at various locations of the first test piece with those at various locations of the unrepaired turbine blade. If the stress values ​​at the critical points and the gradient values ​​at each location are consistent, the first test piece is qualified. If there are inconsistencies in the stress values ​​at the critical points or inconsistencies in the stress gradients at any location, the first test piece is unqualified, and the design of the first test piece is then optimized and improved.

[0039] Step 1.3) Remove the preset area range of the dangerous point location of the first test piece.

[0040] For small-area damage defects, such as cracks, on the surface of nickel-based superalloy turbine blades, the damaged area is usually removed by machining before repair. Therefore, after obtaining a reasonably designed first test piece, the crack size is determined by methods such as penetrant testing on an actual unrepaired turbine blade. A predetermined area at the critical point is then removed as the area for subsequent cladding repair. This predetermined area can be set according to the crack size.

[0041] It should be noted that the cladding zone is generally set at one end of the first test piece, and the danger point is located in the cladding zone. First, the preset area including the danger point is removed by turning or other means, and then the subsequent cladding repair is carried out in the preset area.

[0042] Step 1.4) Determine the heat dissipation base based on the dimensions of the first test piece and the cladding test results.

[0043] Because the actual size of turbine blades is relatively small, the size of the equivalent first test piece is also small, which can easily lead to poor heat dissipation during laser cladding. Therefore, based on the size of the first test piece, a laser cladding heat dissipation base is designed, and the effectiveness of the heat dissipation base is verified by whether the first test piece deforms after the cladding test. It is understood that if the heat dissipation base design is compliant, the first test piece will not deform after the cladding test (which can be a simulation test), otherwise...

[0044] Step 1.5) Assemble the first test piece with the heat dissipation base to obtain the target test piece of the unrepaired turbine blade.

[0045] The target test piece is used to characterize unrepaired turbine blades. Figure 4 The diagram shows a target test specimen; wherein the cladding test specimen is equivalent to the first test specimen, and the heat dissipation base is connected to the end of the cladding area of ​​the cladding test specimen; For example, see Figure 2 Another method for determining the target test specimen, as shown, to obtain the target test specimen design, specifically includes: Finite element simulation of liquid rocket engine turbine blades is used to obtain the location of critical points on the turbine blades. Based on the location of the critical points and stress gradient changes, the shape and size of the test specimen are designed. It is then determined whether the stress at the critical point in the finite element simulation of the test specimen is consistent with that at the critical point on the blade. If they are inconsistent, the process of designing the shape and size of the test specimen based on the location of the critical point and stress gradient changes is repeated. If they are consistent, the critical point location of the test specimen is removed by cladding according to the size of the turbine blade crack. A laser cladding heat dissipation base is designed. The effectiveness of the heat dissipation base is verified to determine whether it is effective. If it is not reasonable, the process of designing the laser cladding heat dissipation base is repeated. If it is reasonable, the test specimen and the heat dissipation base are assembled to complete the overall design of the laser cladding repair test specimen.

[0046] Based on the aforementioned embodiments, the cladding repair operation in step S102 is performed on the target test piece, including: Step 2.1) Select the substrate material corresponding to the target test piece, and under the action of the target laser cladding process parameters, perform cladding repair on the preset area of ​​the dangerous point in the target test piece.

[0047] The target laser cladding process parameters were determined through orthogonal experiments. These experiments were designed taking into account factors such as the type of laser cladding equipment and the cladding material. Based on the designed target test piece, laser cladding is used to repair a pre-defined area at the critical point location. To avoid differences in cladding results due to different materials, the same base material as the target test piece, such as high-temperature alloy powder, is selected for cladding. Simultaneously, orthogonal experiments can be designed to determine the optimal laser cladding process parameters based on the type of equipment performing the laser cladding operation, the size of the target test piece, and the characteristics of the base material. The experimental variables include laser power, powder feed rate, spot size, and scanning speed. The quality of the cladding result can be determined by the morphology and porosity of the cladding layer.

[0048] In some embodiments, step S104 is further implemented through the following steps to obtain test results for characterizing the material properties of the repaired turbine blade, specifically including: Step 3.1) Perform a uniaxial tensile test on the repaired target specimen to obtain the stress-strain curves of the repaired turbine blade at different temperature points.

[0049] Step 3.2) Perform a cyclic loading test on the repaired target test piece to obtain the stable cyclic hysteresis curves of the repaired turbine blade at different strain amplitudes corresponding to a preset temperature point.

[0050] After laser cladding, uniaxial tensile tests and cyclic loading tests were conducted on the clad specimens to obtain material performance parameters. To minimize experimental errors, each test step was performed three times. The uniaxial tensile test can be understood as directly stretching the material at different temperature points until it breaks, obtaining the stress-strain curve. This curve reveals parameters such as the elastic modulus, tensile strength, yield strength, elongation after fracture, and reduction of area of ​​the repaired material at different temperature points. The cyclic loading test involves performing cyclic loading tests at a predetermined temperature point with different strain amplitudes to obtain a stable cyclic curve. This curve reveals parameters such as yield strength, cyclic stiffness, and hardening parameters.

[0051] Based on the aforementioned embodiments, it is known that the set of material performance parameters consists of two curves obtained from two different material performance test operations. These curves can characterize the set of material performance parameters of the repaired turbine blade at different temperature points and different strain amplitudes. In step S104, this set of material performance parameters is combined with the hardening model to determine the constitutive model of the repaired turbine blade, including: Step 4.1) Considering the ratchet effect, the constitutive model of the repaired turbine blade is constructed by fitting the target parameters in the hardening model obtained by combining Chaboche kinematic hardening and nonlinear isotropic hardening of the Voce function through the set of material property parameters and the particle swarm optimization algorithm.

[0052] Nonlinear isotropic hardening of the Voce function is a specific type of isotropic hardening.

[0053] For example, the Chaboche kinematic hardening model is based on the Von Mises yield criterion, with the following yield function:

[0054] in, For stress tensor, Let be the back stress tensor, representing the center of the yield surface; For the deviatoric stress tensor, Let be the deviatoric stress tensor, representing the center of the yield surface in deviatoric stress space; This represents the yield surface size.

[0055] The Chaboche hardening model is a superposition of several Armstrong-Frederick (AF) nonlinear hardening models, and its expression is:

[0056]

[0057] Where M represents the number of superimposed hardening models. and These are parameters related to materials and temperature. and This is part of the target parameters to be fitted in the constitutive model.

[0058] Based on the AF evolution law, when in a stable cycle under strain control, the following formula exists:

[0059] For the yield surface size, according to the isotropic hardening criterion, it can be expressed by the following formula:

[0060] in, For yield stress, For plastic strain tensor, , b represents another part of the target parameters to be fitted in the constitutive model.

[0061] Using the set of material performance parameters obtained from material experiments, namely stress-strain curves at different temperatures and stable cyclic hysteresis curves at different strain amplitudes at preset temperatures, the target parameters in the constitutive model are then fitted using a particle swarm optimization algorithm. , , , and b, specifically including: First, transform the stress-strain curve to the stress-plastic strain coordinate system to obtain the plastic segment curve. Then, divide the plastic segment curve into n+1 segments and calculate the secant slope of each segment, denoted as . Based on this plastic segment curve, we can obtain... and The initial value; where, the strengthening modulus The difference in the secant slope between any two adjacent plastic segments of the curve represents the attenuation rate of the strengthening modulus. It is the reciprocal of the plastic strain of the plastic segment curve.

[0062] Since the strain on the horizontal axis of the stress-strain curve is the total strain including both elastic and plastic strain, subtracting the elastic strain from the total strain yields the stress-plastic strain diagram; based on this stress-plastic strain diagram, one can obtain... , The initial values ​​of b; where the stress-plastic strain diagram (curve) is divided into two segments: the first segment is a small strain segment with large curvature changes, and the second segment is a large strain segment with approximately constant curvature. Take the slope of the tangent or secant line of the second segment. + The point where the aforementioned secant lines intersect the σ-axis. The slope of the initial tangent or secant when plastic strain just begins to occur.

[0063] Based on the initial values ​​of the above five target parameters, the initial constitutive model of the repaired turbine blade is determined; based on the initial constitutive model, its corresponding predicted cyclic hysteresis curve can be determined, and the predicted cyclic hysteresis curve is compared with the cyclic hysteresis curve obtained by the material performance test in the aforementioned embodiment steps; it is determined whether the difference between the predicted cyclic hysteresis curve and the experimentally obtained cyclic hysteresis curve meets the requirement of being within 5% of the preset difference threshold. If the requirements are met, the target parameters and the constitutive model corresponding to these target parameters will be as expected, which are the final optimized target parameters and constitutive model. If the requirements are not met, the particle swarm optimization algorithm is used for parameter optimization. By iteratively updating the velocity and position of the particles, the two objectives in the fitness function of the particle swarm optimization algorithm are optimized together to minimize the overall error. The fitness function of the particle swarm optimization algorithm is the difference between the point-to-point deviation and the area of ​​the hysteresis loop between the predicted cyclic hysteresis curve and the experimentally obtained cyclic hysteresis curve. It can be understood that the two objectives to be optimized together are the difference between the point-to-point deviation and the area of ​​the hysteresis loop between the cyclic hysteresis curves. Then, the process returns to the step of determining whether the difference between the predicted cyclic hysteresis curve and the experimentally obtained cyclic hysteresis curve meets the requirement of being within 5% of the preset difference threshold. It should be noted that when both objectives to be co-optimized are reduced to within 5% of the preset difference threshold, the difference between the predicted cyclic hysteresis curve and the experimentally obtained cyclic hysteresis curve meets the requirement of being within 5% of the preset difference threshold, and the iterative optimization operation stops.

[0064] In some embodiments, step S108 introduces the influence of the constitutive model to perform working cycle simulation on the repaired turbine blades in order to obtain more accurate stress-strain data. This can be achieved through the following steps: Step 4.1) Perform flow field simulation on the repaired turbine blades to obtain the temperature, pressure and convective heat transfer coefficient of the surface combustion gas on the repaired turbine blades.

[0065] Step 4.2) Using the surface gas temperature and convective heat transfer coefficient as thermal loads, thermal analysis is performed on the repaired turbine blades to obtain the temperature distribution of the repaired turbine blades.

[0066] Step 4.3), based on the constitutive model, performs a three-dimensional finite element structural analysis on the repaired turbine blade under the conditions of temperature distribution, pressure, and preset rotational speed, to obtain stress and strain data corresponding to the critical points of the repaired turbine blade. The preset rotational speed can be understood as a specific rotational speed value corresponding to the actual estimated service life of the repaired turbine blade, based on its model or the engine model it is applied to.

[0067] Based on the temperature distribution obtained from the aforementioned thermal analysis, a constitutive model is introduced, followed by a three-dimensional finite element structural analysis of the repaired turbine blade. Considering the circumferential symmetry of the turbine, a single blade and a portion of the turbine disk are used for simulation calculations. Radial constraints are applied to the nodes on the hub centerline to constrain the circumferential displacement of nodes within the radius of the downstream end face center axis. Coupled constraints are applied to the nodes on the circumferential surface of the hub to obtain a cyclically symmetric boundary, and the circumferential displacement of nodes on the circumferential surface of the turbine disk boss is constrained. The analysis model is as follows: Figure 3 As shown, the location of the critical point and the equivalent stress cloud map of the blade are obtained through simulation, and then the stress and strain data at the critical point location are extracted.

[0068] Based on the aforementioned embodiments, step S110 estimates the applicable lifespan of the repaired turbine blades for liquid rocket engines based on stress-strain data at the most vulnerable locations, including: Step 5.1) Based on the stress-strain data, calculate the low-cycle fatigue damage and ratchet damage of the repaired turbine blades respectively.

[0069] For the repeated start-stop operation of the engine, local strain analysis is used to perform turbine low-cycle fatigue analysis. The strain-life prediction model is established using Manson-Coffin (MC) theory, and its general MC formula is as follows:

[0070] in, For total strain, For the total strain range, For elastic strain, For the elastic strain range, For the range of plastic strain, This refers to the number of cycles in low-cycle fatigue life. The elastic modulus of the material, , , , These are the fatigue performance parameters of the material.

[0071] Since the initiation and propagation of cracks in the repaired turbine blades are still affected by the principal stress in practical applications, in order to more accurately estimate the life of the repaired turbine blades, the above MC formula is modified by SWT based on the fatigue theory proposed by Smith et al., taking into account the influence of the principal stress, and the strain-life formula is as follows:

[0072] The turbine exhibits thermal structural response under cyclic thermal and centrifugal loads. There are r strain amplitudes, and the lifetimes at each strain amplitude are as follows: Low-cycle fatigue damage can be obtained in one working cycle. as follows:

[0073] Ratchet damage It can be defined as a function of cumulative plastic tensile strain:

[0074] in, This represents the residual strain after the cycle ends. The initial strain of the cycle, It represents the ultimate strain of a material.

[0075] Step 5.2) sums up the low-cycle fatigue damage and ratchet damage to obtain the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine.

[0076] Assuming all types of damage accumulate linearly, the total damage within one cycle is... for:

[0077]

[0078] Where D represents the cumulative damage after a certain number of cycles, i is the number of cycles, and the cycle life is... The cumulative damage formula can be understood as follows: after i cycles of repair, the cycle life of the turbine blade is... The cumulative damage it suffers is D.

[0079] Step 5.3) Determine the cycle life of the repaired turbine blade based on the time it takes for the cumulative damage to reach the preset cumulative damage threshold.

[0080] According to the Palmgren-Miner linear cumulative damage rule, when the cumulative damage reaches a preset cumulative damage threshold, the repaired turbine blade structure will fail. Based on this, the cycle life is calculated as follows: The preset cumulative damage threshold is set to 1.

[0081] This invention first designs a laser cladding repair test piece, then selects appropriate process parameters for laser cladding, and after laser cladding, the surface of the test piece is machined to achieve a good surface gloss. Based on this, tensile and cyclic loading tests are conducted on the repaired test piece to obtain material property parameters. Then, a particle swarm optimization algorithm is used to fit the parameters involved in the constitutive model. Finally, a multi-cycle turbine working process simulation is performed to obtain the stress-strain response of the turbine blades during the repair process. These responses are then substituted into the damage model, and the turbine's lifespan after repair is obtained based on the linear cumulative damage criterion.

[0082] In some embodiments, such as Figure 5 As shown, this embodiment of the invention provides a device for estimating the lifespan of turbine blades after repair of a liquid rocket engine, comprising: The repair module acquires a target test piece of an unrepaired turbine blade used to characterize a liquid rocket engine, and performs cladding repair on the target test piece; The test module conducts material property tests on the repaired target test piece to obtain a set of material property parameters of the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes. The determination module, based on the set of material performance parameters and a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, determines the constitutive model of the repaired turbine blade using a particle swarm optimization algorithm; wherein, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic loading. The simulation module performs a working cycle simulation on the repaired turbine blade based on the constitutive model to obtain stress and strain data of the repaired turbine blade at the critical point. The estimation module calculates the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on the stress and strain data, and estimates the cycle life of the repaired turbine blade.

[0083] Furthermore, the repair module is also used to perform finite element simulation on the unrepaired turbine blade of the liquid rocket engine to obtain the stress gradient and stress values ​​at the critical point location of the unrepaired turbine blade; determine a first test piece that is consistent with the stress gradient and stress values ​​at the critical point location of the unrepaired turbine blade according to the material performance test conditions; remove the preset area range of the critical point location of the first test piece; determine the heat dissipation base based on the size of the first test piece and the cladding test results; and assemble the first test piece with the heat dissipation base to obtain the target test piece of the unrepaired turbine blade.

[0084] Furthermore, the repair module is also used to select the substrate material corresponding to the target test piece, and under the action of the target laser cladding process parameters, to perform cladding repair on a preset area of ​​the dangerous point in the target test piece; wherein, the target laser cladding process parameters are determined through orthogonal experimental results.

[0085] Furthermore, the test module is also used to conduct uniaxial tensile tests on the repaired target test piece to obtain the stress-strain curves of the repaired turbine blade at different temperature points; and to conduct cyclic loading tests on the repaired target test piece to obtain the stable cyclic hysteresis curves of the repaired turbine blade at different strain amplitudes.

[0086] Furthermore, the determination module is also used to construct the constitutive model of the repaired turbine blade by fitting the target parameters in the hardening model obtained by combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening through the set of material performance parameters using the particle swarm optimization algorithm.

[0087] Furthermore, the simulation module is also used to perform flow field simulation on the repaired turbine blade to obtain the temperature, pressure, and convective heat transfer coefficient of the surface combustion gas on the repaired turbine blade; using the temperature of the surface combustion gas and the convective heat transfer coefficient as thermal loads, thermal analysis is performed on the repaired turbine blade to obtain the temperature distribution of the repaired turbine blade; based on the constitutive model, under the action of the temperature distribution, the pressure, and the preset rotational speed, a three-dimensional finite element structural analysis is performed on the repaired turbine blade to obtain the stress and strain data corresponding to the critical point location of the repaired turbine blade.

[0088] Furthermore, the estimation module is also used to calculate the low-cycle fatigue damage and ratchet damage of the repaired turbine blade based on the stress-strain data; to sum the low-cycle fatigue damage and the ratchet damage to obtain the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine; and to determine the cycle life of the repaired turbine blade based on the time when the cumulative damage reaches a preset cumulative damage threshold.

[0089] The present invention provides an embodiment for implementing an electronic device. In this embodiment, the electronic device may be, but is not limited to, a personal computer (PC), a laptop computer, a monitoring device, a server, or other computer device with analysis and processing capabilities.

[0090] As an exemplary embodiment, see [reference]. Figure 6The electronic device 110 includes a communication interface 111, a processor 112, a memory 113, and a bus 114. The processor 112, the communication interface 111, and the memory 113 are connected via the bus 114. The memory 113 is used to store a computer program that supports the processor 112 in executing the above-described method. The processor 112 is configured to execute the program stored in the memory 113.

[0091] The machine-readable storage medium mentioned in this article can be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, etc. For example, machine-readable storage media can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drives (such as hard disk drives), any type of storage disk (such as optical discs, DVDs, etc.), or similar storage media, or combinations thereof.

[0092] Non-volatile media can be non-volatile memory, flash memory, storage drives (such as hard disk drives), any type of storage disk (such as optical discs, DVDs, etc.), or similar non-volatile storage media, or combinations thereof.

[0093] It is understood that the specific operation methods of each functional module in this embodiment can be referred to the detailed description of the corresponding steps in the above method embodiment, and will not be repeated here.

[0094] The computer-readable storage medium provided in the embodiments of the present invention stores a computer program. When the computer program code is executed, it can implement the method described in any of the above embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0097] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0098] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A method for predicting the lifespan of turbine blades after repair of a liquid rocket engine, characterized in that, include: Obtain a target test piece of an unrepaired turbine blade for characterizing a liquid rocket engine, and perform cladding repair on the target test piece; Material property tests were conducted on the repaired target test piece to obtain a set of material property parameters for the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes. Based on the set of material performance parameters and the hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, the constitutive model of the repaired turbine blade is determined by the particle swarm optimization algorithm; wherein, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic load. Based on the constitutive model, a working cycle simulation was performed on the repaired turbine blade to obtain stress and strain data of the repaired turbine blade at the critical point. Based on the stress-strain data, the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine is calculated, and the cycle life of the repaired turbine blade is estimated.

2. The method according to claim 1, characterized in that, The steps for obtaining target test specimens of unrepaired turbine blades for characterizing liquid rocket engines include: Finite element simulation was performed on the unrepaired turbine blades of a liquid rocket engine to obtain the stress gradient and stress values ​​at critical points of the unrepaired turbine blades. Based on the material performance test conditions, a first test piece was determined to be consistent with the stress gradient and stress value at the critical point location of the unrepaired turbine blade. The preset area range of the dangerous point location of the first test piece is removed; The heat dissipation base is determined based on the dimensions of the first test piece and the cladding test results; The first test piece is assembled with the heat dissipation base to obtain the target test piece of the unrepaired turbine blade.

3. The method according to claim 1, characterized in that, The steps for cladding repair of the target test piece include: A substrate material corresponding to the target test piece is selected, and under the action of the target laser cladding process parameters, a preset area of ​​the dangerous point in the target test piece is clad and repaired; wherein, the target laser cladding process parameters are determined through orthogonal experimental results.

4. The method according to claim 1, characterized in that, The steps of conducting material property tests on the repaired target test specimen to obtain a set of material property parameters for the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes include: A uniaxial tensile test was performed on the repaired target specimen to obtain the stress-strain curves of the repaired turbine blade at different temperature points. Cyclic loading tests were conducted on the repaired target specimen to obtain the stable cyclic hysteresis curves of the repaired turbine blade under different strain amplitudes.

5. The method according to claim 1, characterized in that, Based on the aforementioned set of material performance parameters and a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, the steps for determining the constitutive model of the repaired turbine blade using a particle swarm optimization algorithm include: Using the set of material performance parameters, the constitutive model of the repaired turbine blade is constructed by fitting the target parameters in the hardening model obtained by combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening through particle swarm optimization algorithm.

6. The method according to claim 1, characterized in that, The steps of performing working cycle simulation on the repaired turbine blade based on the constitutive model to obtain stress and strain data of the repaired turbine blade at the critical point include: Flow field simulation was performed on the repaired turbine blade to obtain the temperature, pressure and convective heat transfer coefficient of the surface gas of the repaired turbine blade. Using the temperature of the surface gas and the convective heat transfer coefficient as heat loads, thermal analysis was performed on the repaired turbine blades to obtain the temperature distribution of the repaired turbine blades. Based on the constitutive model, under the conditions of the temperature distribution, pressure, and preset rotational speed, a three-dimensional finite element structural analysis is performed on the repaired turbine blade to obtain stress and strain data corresponding to the critical points of the repaired turbine blade.

7. The method according to claim 1, characterized in that, The steps of calculating the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on the stress-strain data and estimating the cycle life of the repaired turbine blade include: Based on the stress-strain data, the low-cycle fatigue damage and ratchet damage of the repaired turbine blade are calculated respectively. The low-cycle fatigue damage and the ratchet damage are summed to obtain the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine. The cycle life of the repaired turbine blade is determined based on the time it takes for the accumulated damage to reach a preset cumulative damage threshold.

8. A device for predicting the lifespan of turbine blades after repair of a liquid rocket engine, characterized in that, include: The repair module acquires a target test piece of an unrepaired turbine blade used to characterize a liquid rocket engine, and performs cladding repair on the target test piece; The test module conducts material property tests on the repaired target test piece to obtain a set of material property parameters of the repaired turbine blades of the liquid rocket engine at different temperature points and different strain amplitudes. The determination module, based on the set of material performance parameters and a hardening model combining Chaboche kinematic hardening and Voce nonlinear isotropic hardening, determines the constitutive model of the repaired turbine blade using a particle swarm optimization algorithm; wherein, the constitutive model is used to simulate the stress-strain response of the repaired turbine blade under cyclic loading. The simulation module performs a working cycle simulation on the repaired turbine blade based on the constitutive model to obtain stress and strain data of the repaired turbine blade at the critical point. The estimation module calculates the cumulative damage of the repaired turbine blade during the operation of the liquid rocket engine based on the stress and strain data, and estimates the cycle life of the repaired turbine blade.

9. An electronic device, characterized in that, It includes a memory, a processor, and a program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed, implements the method described in any one of claims 1-7.

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