Gas turbine performance retention control method and system based on finite time damage reconstruction multi-gradient model

Abstract

The application discloses a gas turbine performance maintenance control method and system based on a finite time damage reconstruction multi-gradient model, comprising the following steps: establishing a high-precision engine nonlinear simulation model, simultaneously establishing a turbine blade creep-fatigue nonlinear coupling damage model considering coating damage, and calibrating the damage model through coating high-temperature oxidation experiments and blade metallographic experiments; based on the idea of using finite time to cross the over-temperature boundary to expand the performance maintenance adjustment space, an over-temperature operation potential function multi-gradient model is established; joint simulation is carried out, the constraint boundary of the expanded performance is calculated in the control problem, and the transition state performance maintenance effect of the turbine is optimized. Based on the application, the gas turbine is controlled to increase the load, and temporarily "borrows" the finite time damage operation area, so that the performance maintenance adjustment space can be effectively expanded, the adjustment time is greatly reduced, and the end oscillation phenomenon in the adjustment process is avoided under the condition that the life loss is equivalent.

Description

Technical Field

[0001] This invention relates to the field of gas turbine performance maintenance technology, and in particular to a gas turbine performance maintenance control method and system based on a finite-time damage reconstruction multi-gradient model. Background Technology

[0002] Gas turbines, as highly efficient energy conversion devices, play a vital role in power generation, aviation, and marine industries. With the growth of global energy demand and increasing environmental protection requirements, the performance and reliability requirements for gas turbines are becoming increasingly stringent. The performance of its gas path components, such as the compressor and turbine, directly affects the overall efficiency and output power of the gas turbine, making their continuous stability and reliability crucial. However, during long-term operation, engines inevitably experience performance degradation, manifested as a deviation of the overall engine operating line from its design state. The efficient operating range of critical engine components such as the compressor and turbine significantly narrows; for example, a 6% decrease in compressor flow reduces the matching working space with the turbine by 39%. Simultaneously, turbine blade degradation leads to airflow streamline shift, disrupting the matching relationship between the moving blade entrainment velocity and the stationary blade absolute velocity, amplifying changes in the outlet airflow angle, and further exacerbating the overall engine performance imbalance. This degradation not only leads to increased fuel consumption and decreased thrust, but the damage state after performance degradation is also difficult to quantify precisely. It significantly compresses the engine's control margin and safety boundaries (covering new coatings and coating experiments at 80%, 50%, and 20% oxidation levels), limiting the effectiveness of traditional model-based performance optimization control strategies and leaving insufficient room for adjustment. Therefore, it is essential to conduct research on methods for maintaining the performance of gas turbines.

[0003] Traditional gas path performance maintenance models constrain the engine operating range by calculating control boundaries such as overheating, overspeed, surge, and shutdown. However, the constraint range changes significantly after performance degradation, the overheating boundary deviates, leading to inaccurate calculations. During the adjustment process, the surge line is followed, and overshoot oscillations at the end speed are prone to occur. Therefore, it is difficult to achieve the ideal performance maintenance effect by applying traditional methods. Summary of the Invention

[0004] The purpose of this invention is to provide a gas turbine performance maintenance control method and system based on a finite-time damage reconstruction multi-gradient model, which improves the problems existing in traditional performance maintenance methods.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model, comprising:

[0007] Establish a nonlinear simulation model of the engine;

[0008] A nonlinear coupled damage model of creep and fatigue for turbine blades that considers damage to high-temperature coatings was established.

[0009] The nonlinear coupled damage model of turbine blade creep-fatigue considering high-temperature coating damage was calibrated by high-temperature oxidation experiments on turbine blade coating and metallographic experiments on blades.

[0010] A multi-gradient model of the over-temperature operating potential function is established, and the operating range is divided into the initial isotherm, the main operating range, and the finite-time damage operating region by the potential function value.

[0011] Perform co-simulation to determine the constraint boundaries that improve the potential function during the control process.

[0012] Preferably, the nonlinear simulation model of the engine is used to simulate the working state of the engine. The input control quantities include the main fuel flow rate and the throat area of ​​the tail nozzle. The simulation is carried out under certain flight operating parameters and speeds. The simulation process needs to satisfy the common working equation of the engine. The Newton method is used for iterative solution. The simulation outputs the temperature and pressure of the cross-section of each component of the engine and the thrust fuel consumption performance parameters.

[0013] Preferably, the establishment of the turbine blade creep-fatigue nonlinear coupled damage model considering high-temperature coating damage includes:

[0014] Creep fatigue damage The calculation is as follows: , The first term represents fatigue damage, the third term represents creep damage, and the second term represents the nonlinear coupling effect of creep and fatigue. It is the fatigue area. This indicates a specific stress or strain level. Below, the actual number of load cycles that the component or material bears. At stress level Below, the critical number of cycles that leads to fatigue failure of the material. and It is an interaction factor. It is at the stress level The running time is as follows. It is the creep life under a specific combination of temperature and stress. It is the creep factor; The high-temperature coating damage calculation is based on the Arrhenius equation to establish a coating oxidation rate model, and the high-temperature coating damage is calculated by the amount of remaining metal in the coating.

[0015] Preferably, the calibration process for the high-temperature oxidation experiment of the turbine blade coating is as follows:

[0016] Turbine blade coating samples with different oxidation levels were selected, including new coatings and coating samples with oxidation levels of 80%, 50%, and 20%.

[0017] Oxidation characteristics of each sample were tested under high temperature conditions to simulate the actual operating temperature conditions of a gas turbine.

[0018] Record the oxide layer thickness and residual metal content of the coating for samples with different oxidation levels;

[0019] The obtained experimental data were substituted into the high-temperature coating damage calculation model, and the model parameters were corrected to make the calculated high-temperature coating damage value match the measured oxidation degree.

[0020] Preferably, the metallographic calibration process for the blade is as follows:

[0021] Turbine blades that have been decommissioned after service are selected and subjected to cutting, grinding, polishing and corrosion treatment.

[0022] The internal defects of the turbine blades were observed using a metallographic microscope, and the location and size of the defects were recorded.

[0023] Observe the edge region of the turbine blade, detect edge crack defects, record the crack location and measure the length;

[0024] Substitute the internal defect size and edge crack size data into the creep-fatigue nonlinear coupled damage model and adjust the fatigue damage-related parameters.

[0025] Preferably, the step of establishing a multi-gradient model of the overtemperature operating potential function, which divides the operating range into an initial isotherm, a main operating range, and a finite-time damage operating region using the potential function values, includes:

[0026] Historical data on over-temperature boundaries during long-term operation of the gas turbine were collected, and the control boundary characteristics of over-temperature, over-revving, surge, and shutdown in the traditional performance preservation model were combined to determine the basic parameters of the model; the basic parameters of the model refer to the boundary constraints and engine performance parameters in the traditional performance preservation model.

[0027] Define the control input and system state in the potential function, and divide the region by the potential function value, including the initial isotherm, the main operating range and the finite-time damage operating range;

[0028] The potential function is expressed as: argmax P ( u,x,σ ) =F ( u ) -σψ ( x ), P ( u,x,σ Let ) be the potential function.u It is the engine control variable vector in the optimization control problem. x It is the engine state variable vector. σ It is a penalty factor vector. F ( u The main revenue function is the performance metric that needs to be maximized. ψ ( x () is the constraint violation function, used to quantify the safety constraints of the engine;

[0029] Based on the heat resistance limit and damage accumulation law of gas turbine blades, the temperature range of the finite-time damage operation zone and the longest single operation time are determined.

[0030] Preferably, the co-simulation includes:

[0031] A joint simulation platform was built, integrating the engine nonlinear simulation model, coupled damage model, and over-temperature operating potential function multi-gradient model. Data interaction interfaces between models were set up to ensure that the models work together.

[0032] Input the initial and target operating parameters of the gas turbine under load transition state to simulate the problem scenario of "following the surge line during the adjustment process and overshooting oscillation at the end speed" in the traditional method;

[0033] Initiate joint simulation, control the gas turbine load increase process, observe the operating line during the control process, and when approaching the surge boundary, briefly cross the damage operating area for a limited time to increase the surge margin;

[0034] Real-time monitoring of turbine blade damage accumulation ensures that lifespan loss is consistent with traditional methods;

[0035] After the simulation is completed, the transient settling time, speed overshoot, and damage accumulation are output.

[0036] The present invention also provides a gas turbine performance maintenance control system based on a finite-time damage reconstruction multi-gradient model, comprising:

[0037] The co-simulation platform integrates the engine nonlinear simulation model, coupled damage model, and over-temperature operating potential function multi-gradient model, and configures data interaction interfaces between models;

[0038] The coupled damage model is a nonlinear coupled damage model of turbine blade creep-fatigue considering high-temperature coating damage, and it is calibrated by high-temperature oxidation experiments of turbine blade coating and metallographic experiments of blade.

[0039] The multi-gradient model of the over-temperature operating potential function divides the operating range into the initial isotherm, the main operating range, and the finite-time damage operating region through the potential function value.

[0040] On the co-simulation platform, co-simulation is performed based on each model to determine the constraint boundary that makes the potential function better during the control process.

[0041] The present invention also provides a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above for gas turbine performance maintenance control based on a finite-time damage reconstruction multi-gradient model.

[0042] The present invention also provides a computing device comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods in the above-described gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] This invention establishes a high-precision nonlinear coupled damage model for turbine blade creep and fatigue, reducing the life prediction error from 10% to below 4%. This invention reveals the damage mechanism of operating beyond the over-temperature boundary, proposes the concept of a "finite-time damage operating region," and establishes a multi-gradient model of the over-temperature operating potential function, doubling the performance adjustment space. By using this method to control the load increase of the gas turbine and briefly "borrow" the finite-time damage operating region, the performance maintenance adjustment space can be effectively expanded, and the adjustment time reduced by 60%. Under the condition of comparable life loss, it avoids the end-of-process oscillation phenomenon, achieving faster and more stable turbine performance maintenance. Attached Figure Description

[0045] Figure 1 A schematic diagram of the gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model provided by the present invention;

[0046] Figure 2 A schematic diagram illustrating the process of calculating high-temperature coating damage using the coupled damage model established in this invention;

[0047] Figure 3 This is a schematic diagram illustrating the effect of the performance adjustment method used in this invention.

[0048] Figure 4 This is a schematic diagram comparing the transition state performance retention control results of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0050] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0051] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0052] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0053] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0054] It should be emphasized here that the step markers mentioned below are not a limitation on the order of the steps, but should be understood as meaning that the steps can be executed in the order mentioned in the embodiments, or in a different order than in the embodiments, or several steps can be executed simultaneously.

[0055] This invention provides a gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0056] Step 1: Establish a high-precision nonlinear simulation model of the engine;

[0057] In step 1, the high-precision nonlinear simulation model of the engine needs to be combined with the performance degradation characteristics of the gas turbine, namely "the overall speed triangle deviates from the design state and the working range of the components becomes smaller". The component characteristics are corrected by test run and test flight data to ensure that the model can accurately simulate the working conditions after performance degradation, and provide a reliable foundation for subsequent joint simulation.

[0058] A simulation model refers to a component-level simulation model of the overall performance of an engine. It is used to simulate the working state of the engine. The input is generally a control variable, such as the main fuel flow rate and the throat area of ​​the tail nozzle. At the same time, it simulates certain flight operating parameters, such as altitude Mach, and given certain speed conditions. The simulation process needs to satisfy the common working equations of the engine and use Newton's method for iterative solution. The simulation outputs performance parameters such as temperature and pressure of the cross-section of each engine component, as well as thrust and fuel consumption rate.

[0059] The specific implementation steps are as follows: a design point model is established based on some actual design data, and the benchmark parameters such as turbine inlet temperature and compressor outlet pressure under the design point are calculated through gas dynamics equations; then, non-design point corrections are made for the characteristics of components such as fan, compressor, high-pressure turbine and low-pressure turbine based on test run and test flight data, which can achieve a key parameter error of less than 5% at the steady state point.

[0060] It should be noted that the design point model is a simulation model that simulates the engine under design conditions, while the non-design point model is the engine's operating state under non-design conditions other than the design point.

[0061] Step 2: Establish a nonlinear coupled damage model of turbine blade creep-fatigue considering coating damage;

[0062] In step 2, a coating oxidation rate model is established based on the Arrhenius equation, and the coating damage coefficient is calculated using the remaining metal content of the coating. The specific process is as follows: Figure 2 As shown, it includes:

[0063] S21. Calculate the oxidation rate T.

[0064] Corresponding to the "Arrhenius equation oxidation rate model", the oxidation rate is determined by temperature T;

[0065] Oxidation rate: W^2 = Kp·t;

[0066] W represents the weight of the oxide film per unit, Kp represents the oxidation rate constant, and t represents time.

[0067] S22. Calculate the weight of the separated oxides;

[0068] Critical weight for oxide separation: W crit =ρ ox ·h crit ;

[0069] ρ ox h represents the oxide film density. crit To separate the critical thickness;

[0070] Separated oxide weight: W spall =η·W crit ,

[0071] η is the separation ratio;

[0072] S23. Calculate the current cumulative weight of oxides;

[0073] Current cumulative weight of oxides: W acc (t) n =sqrt(k) p ·t n ),

[0074] t n This represents the current oxidation time.

[0075] S24. Calculate the amount of residual metal in the coating.

[0076] The remaining metal amount is the initial metal amount minus the weight of the separated oxides;

[0077] S25. Calculate the high-temperature coating damage D2 and convert the remaining metal amount into a quantified damage value.

[0078] Creep fatigue damage is calculated as follows:

[0079] ,

[0080] The first term calculates fatigue damage, the third term calculates creep damage, and the second term calculates the nonlinear coupling effect of creep and fatigue. It is the fatigue area. This indicates a specific stress level (or strain level). Below, the actual number of load cycles that the component or material bears. At the same stress level Below, the critical number of cycles that leads to fatigue failure of the material. and It is an interaction factor, usually =0.5, It is the runtime under those conditions. It is the creep life under specific temperature and stress combinations. It is a creep factor.

[0081] The core of the coupled damage model of this invention lies in encompassing fatigue damage, creep damage, and the nonlinear coupling effect between the two. At the same time, it introduces coating damage D2 to adapt to the calculation of coating damage with different oxidation levels (new coating and 80%, 50%, and 20% oxidation levels), ensuring that the model can truly reflect the damage accumulation law of blades under complex working conditions.

[0082] Step 3: The nonlinear coupled damage model of turbine blade creep-fatigue established in Step 2 is calibrated by high-temperature oxidation test of turbine blade coating and metallographic test of blade.

[0083] In step 3, the blade coupling damage model established in step 2 is calibrated through high-temperature oxidation experiments on turbine blade coatings (covering new coatings and coatings with oxidation levels of 80%, 50%, and 20%), and metallographic experiments on blades (including internal defect testing, internal defect measurement, edge crack defect testing, and edge crack defect measurement).

[0084] Metallographic experiments on blades should focus on detecting internal defects and edge cracks, and measuring their dimensions. Based on these experimental data, the model parameters are corrected, reducing the life prediction error from 10% to below 4%.

[0085] The calibration process is as follows:

[0086] (1) High-temperature oxidation experiment of coating

[0087] Blade coating samples with different oxidation levels were selected, including new coatings and coating samples with oxidation levels of 80%, 50%, and 20%. Oxidation characteristic experiments were conducted on various samples under high-temperature conditions to simulate the actual operating temperature conditions of the gas turbine. Key data such as oxide layer thickness and residual coating metal were recorded for samples with different oxidation levels to provide a basis for the calibration of the coating damage sub-model. The experimental data were substituted into the coating damage calculation model, and the model parameters were corrected to match the calculated coating damage value with the measured oxidation level.

[0088] (2) Metallographic experiment of the blade

[0089] Turbine blades that have been decommissioned after service are selected and then cut, ground, polished, and corroded.

[0090] Internal defects (such as pores and inclusions) in the blade are observed using a metallographic microscope. The location and size of the defects are recorded. Edge cracks are detected in the blade edge area. The location and length of the cracks are recorded. The internal defect size and edge crack size data are substituted into the coupled damage model. The fatigue damage-related parameters in the model are adjusted to reduce the life calculation error of the model.

[0091] The corrected model can be used in experiments to perform high-precision calculations of turbine blade creep-fatigue damage and life, reducing the life prediction error from 10% to below 4%.

[0092] Step 4: Based on the idea of ​​using finite time to cross the over-temperature boundary to expand the performance maintenance adjustment space, establish a multi-gradient model of the over-temperature operating potential function, and divide the initial isotherm, the main operating range and the finite time damage operating region.

[0093] In step 4, a multi-gradient model of the over-temperature operating potential function is established. The key is to divide the region into three major regions through the potential function: the initial isotherm, the main operating range, and the finite-time damage operating region. Among them, the "finite-time damage operating region" is the core of expanding the performance maintenance adjustment space. The boundary conditions of this region need to be clearly defined to ensure that the life loss during short-term operation is controllable.

[0094] Establishing a multi-gradient model of the over-temperature operating potential function may include the following sub-steps:

[0095] S41. Collect historical data of the over-temperature boundary during long-term operation of the gas turbine, and combine it with the control boundary characteristics such as over-temperature, over-speed, surge, and shutdown in the traditional performance preservation model to determine the basic parameters of the model; where the over-temperature boundary data refers to the engine operating condition data when the operating state exceeds the maximum temperature constraint; the basic parameters of the model refer to the boundary constraints and engine characteristic parameters in the traditional performance preservation model.

[0096] S42. Define the control input and system state in the potential function, clarify the calculation logic of the potential function, and divide the region by the potential function value, including the initial isotherm, the main operating range (the normal operating range with a high potential function value), and the finite-time damage operating range (the potential function value is moderate, allowing short-term operation to expand the adjustment space).

[0097] Control input is u Vectors, generally including engine fuel flow rate and exhaust nozzle throat area, system state is x The vectors are generally the engine's dual-shaft speeds n1 and n2, and these parameters are obtained from the engine model.

[0098] It should be noted that the boundaries of each region are calculated through optimization and can be initially divided using given initial values, based on experience and engine design values.

[0099] The specific formula for the potential function is:

[0100] argmax P ( u,x,σ ) =F ( u ) -σψ ( x ).

[0101] u It is the engine control variable vector in the optimization control problem. x It is the engine state variable vector. σ It is a penalty factor vector. F ( u The main reward function is the performance metric that needs to be maximized. ψ ( x) is a constraint violation function used to quantify the safety constraints of an engine.

[0102] S43. Based on the heat resistance limit and damage accumulation law of gas turbine blades, determine the temperature range and the longest single operation time of the finite-time damage operation zone to ensure that the life loss during operation in this zone is controllable.

[0103] S44. Verify the rationality of the multi-gradient model to ensure that the model can effectively calculate the constraint boundary with better extended performance and solve the problem of insufficient adjustment space in traditional methods.

[0104] Step 5: Perform joint simulation based on the engine model established in Steps 1-4, calculate the constraint boundary with better extended performance in the control problem, and optimize the turbine transient performance retention effect.

[0105] The constraint boundary refers to the finite-time life damage boundary. The control variable 𝒖 is optimized within each boundary constraint range using the potential function to make the potential function 𝑷 reach its maximum value. Then, the optimal constraint boundary is obtained by calculating through the engine model.

[0106] In step 5 of this process, the co-simulation needs to address the problems of "adjusting the surge line and end-of-life oscillation" encountered by traditional methods. Based on the dynamic constraint boundary output by the multi-gradient model, the system briefly "borrows" a path through the finite-time damage operating region during the load increase process, ultimately achieving a 60% reduction in adjustment time, no end-of-life oscillation, and comparable lifespan loss. It should be noted that "borrowing a path" refers to briefly crossing the over-temperature boundary during the control process and operating within the finite-time damage region.

[0107] Specifically, this step includes the following sub-steps:

[0108] S51. Build a joint simulation platform, integrate the engine nonlinear simulation model, coupled damage model and over-temperature operating potential function multi-gradient model, set up data interaction interfaces between models to ensure that each model works together.

[0109] In this invention, various models and related calculation functions are established through a simulation platform, such as MATLAB / SIMULINK. The data of these models are basically based on the engine simulation model. The engine parameters are output to optimize the calculation. The platform can be used for unified modeling and data interface, design simulation process and perform data interaction.

[0110] S52. Input the initial and target operating parameters of the gas turbine under load transition state to simulate the problem scenario of "adjustment process following the surge line and end speed overshoot oscillation" in the traditional method;

[0111] S53. Start the joint simulation and control the gas turbine load increase process based on the dynamic constraint boundary (including the finite-time damage operating region boundary) output by the multi-gradient model of the over-temperature operating potential function: observe the operating line during the control process, and when it is close to the surge boundary, briefly cross over the finite-time damage region to increase the surge margin.

[0112] S54. Real-time monitoring of cumulative blade damage to ensure that life loss is comparable to traditional methods;

[0113] S55. After the simulation is completed, the transient adjustment time, speed overshoot, and damage accumulation are output to verify the degree of reduction in transient adjustment time compared to the traditional method and the absence of end-point oscillation, thus achieving optimized performance retention.

[0114] It should be noted that the unit is based on the engine operating line during the control optimization process, the change curve of the output control quantity, the transient state is judged and the transient state adjustment time is determined.

[0115] This invention enables the control of gas turbine load increase during operation, briefly "borrowing" a limited time to damage the operating range, effectively expanding the performance maintenance adjustment space, and reducing the adjustment time by 60%. Under the condition of comparable life loss, it avoids the end oscillation phenomenon during the adjustment process.

[0116] like Figure 3 The diagram illustrates the transition state performance preservation method proposed in this invention. The control path briefly crosses the traditional overtemperature boundary and operates at the finite-time damage boundary calculated by this method. The adjustment path is optimized, resulting in a larger surge margin during the control process. Figure 4 The experimental results show that applying this method reduces the control settling time by 60% while avoiding end-point oscillations.

[0117] Based on the above inventive concept, the present invention also provides a gas turbine performance maintenance control system based on a finite-time damage reconstruction multi-gradient model, comprising:

[0118] The co-simulation platform integrates the engine nonlinear simulation model, coupled damage model, and over-temperature operating potential function multi-gradient model, and configures data interaction interfaces between models;

[0119] The coupled damage model is a nonlinear coupled damage model of turbine blade creep-fatigue considering high-temperature coating damage, and it is calibrated by high-temperature oxidation experiments of turbine blade coating and metallographic experiments of blade.

[0120] The multi-gradient model of the over-temperature operating potential function divides the operating range into the initial isotherm, the main operating range, and the finite-time damage operating region through the potential function value.

[0121] On the co-simulation platform, co-simulation is performed based on each model to determine the constraint boundary that makes the potential function better during the control process.

[0122] Based on the above-described inventive concept, the present invention also provides a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above for gas turbine performance maintenance control based on a finite-time damage reconstruction multi-gradient model.

[0123] Based on the above-described inventive concept, the present invention also provides a computing device, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods in the above-described gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model, characterized in that, include: A nonlinear simulation model of the engine is established. The nonlinear simulation model of the engine is used to simulate the working state of the engine. The input control quantities include the main fuel flow rate and the throat area of ​​the tail nozzle. The simulation is carried out under certain flight operating parameters and speed. The simulation process needs to satisfy the common working equation of the engine. The Newton method is used for iterative solution. The simulation outputs the temperature and pressure of the cross section of each component of the engine and the thrust fuel consumption rate performance parameters. A nonlinear coupled damage model of turbine blade creep and fatigue considering high-temperature coating damage is established, including: Creep fatigue damage The calculation is as follows: ， The first term represents fatigue damage, the third term represents creep damage, and the second term represents the nonlinear coupling effect of creep and fatigue. It is the fatigue area. This indicates a specific stress or strain level. Below, the actual number of load cycles that the component or material bears. At stress level Below, the critical number of cycles that leads to fatigue failure of the material. and It is an interaction factor. It is at the stress level The running time is as follows. It is the creep life under a specific combination of temperature and stress. It is the creep factor; The high-temperature coating damage calculation is based on the Arrhenius equation to establish a coating oxidation rate model, and the high-temperature coating damage is calculated by the amount of remaining metal in the coating. The nonlinear coupled damage model of turbine blade creep-fatigue considering high-temperature coating damage was calibrated by high-temperature oxidation experiments on turbine blade coating and metallographic experiments on blades. A multi-gradient model of the over-temperature operating potential function is established, and the operating range is divided into the initial isotherm, the main operating range, and the finite-time damage operating region by the potential function value. Perform co-simulation to determine the constraint boundaries that improve the potential function during the control process.

2. The gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model according to claim 1, characterized in that, The calibration process for the high-temperature oxidation experiment of the turbine blade coating is as follows: Turbine blade coating samples with different oxidation levels were selected, including new coatings and coating samples with oxidation levels of 80%, 50%, and 20%. Oxidation characteristics of each sample were tested under high temperature conditions to simulate the actual operating temperature conditions of a gas turbine. Record the oxide layer thickness and residual metal content of the coating for samples with different oxidation levels; The obtained experimental data were substituted into the high-temperature coating damage calculation model, and the model parameters were corrected to make the calculated high-temperature coating damage value match the measured oxidation degree.

3. The gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model according to claim 1, characterized in that, The metallographic calibration process for the blade is as follows: Turbine blades that have been decommissioned after service are selected and subjected to cutting, grinding, polishing and corrosion treatment. The internal defects of the turbine blades were observed using a metallographic microscope, and the location and size of the defects were recorded. Observe the edge region of the turbine blade, detect edge crack defects, record the crack location and measure the length; Substitute the internal defect size and edge crack size data into the creep-fatigue nonlinear coupled damage model and adjust the fatigue damage-related parameters.

4. The gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model according to claim 1, characterized in that, The establishment of a multi-gradient model for the overtemperature operating potential function divides the operating range into an initial isotherm, a main operating range, and a finite-time damage operating region based on the potential function values, including: Historical data on over-temperature boundaries during long-term operation of the gas turbine were collected, and the control boundary characteristics of over-temperature, over-revving, surge, and shutdown in the traditional performance preservation model were combined to determine the basic parameters of the model; the basic parameters of the model refer to the boundary constraints and engine performance parameters in the traditional performance preservation model. Define the control input and system state in the potential function, and divide the region by the potential function value, including the initial isotherm, the main operating range and the finite-time damage operating range; The potential function is expressed as: ， Let be the potential function. It is the engine control variable vector in the optimization control problem. It is the engine state variable vector. It is a penalty factor vector. It is the main revenue function, which refers to the performance metric that needs to be maximized. It is a constraint violation function used to quantify the safety constraints of the engine; Based on the heat resistance limit and damage accumulation law of gas turbine blades, the temperature range of the finite-time damage operation zone and the longest single operation time are determined.

5. The gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model according to claim 4, characterized in that, The joint simulation includes: A joint simulation platform was built, integrating the engine nonlinear simulation model, coupled damage model, and over-temperature operating potential function multi-gradient model. Data interaction interfaces between models were set up to ensure that the models work together. Input the initial and target operating parameters of the gas turbine under load transition state to simulate the problem scenario of "following the surge line during the adjustment process and overshooting oscillation at the end speed" in the traditional method; Initiate joint simulation, control the gas turbine load increase process, observe the operating line during the control process, and when approaching the surge boundary, briefly cross the damage operating area for a limited time to increase the surge margin; Real-time monitoring of turbine blade damage accumulation ensures that lifespan loss is consistent with traditional methods; After the simulation is completed, the transient settling time, speed overshoot, and damage accumulation are output.

6. A gas turbine performance maintenance control system based on a finite-time damage reconstruction multi-gradient model, characterized in that, The system for implementing the gas turbine performance maintenance control method based on the finite-time damage reconstruction multi-gradient model as described in claim 1 includes: The co-simulation platform integrates the engine nonlinear simulation model, coupled damage model, and over-temperature operating potential function multi-gradient model, and configures data interaction interfaces between models; The coupled damage model is a nonlinear coupled damage model of turbine blade creep-fatigue considering high-temperature coating damage, and it is calibrated by high-temperature oxidation experiments of turbine blade coating and metallographic experiments of blade. The multi-gradient model of the over-temperature operating potential function divides the operating range into the initial isotherm, the main operating range, and the finite-time damage operating region through the potential function value. On the co-simulation platform, co-simulation is performed based on each model to determine the constraint boundary that makes the potential function better during the control process.

7. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods in the gas turbine performance maintenance control method based on a finite-time damage reconstruction multi-gradient model according to claims 1 to 5.

8. A computing device, characterized in that, It includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods in the gas turbine performance maintenance control method based on the finite-time damage reconstruction multigradient model according to claims 1 to 5.

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