Gas turbine component performance adaptation method based on measured gas path data

Abstract

The application provides a gas turbine component performance adaptation method based on measured gas path data, which can complete matching of characteristic curves and actual component characteristics of a gas turbine. In the condition of only having part of field operation data, component characteristic parameters are reversely calculated, an adjustment factor of a component general characteristic formula is optimized through an intelligent optimization algorithm, an optimal adjustment factor is obtained, performance self-adaptation is realized, thereby when there is no enough experimental data to generate an accurate component characteristic map, the general characteristic formula is adapted based on discrete gas turbine field measured data, the accuracy of the component characteristic map is improved, and high-precision gas turbine gas path simulation and state monitoring are facilitated.

Description

Technical Field

[0001] This invention relates to the field of adaptive technology for gas turbine component characteristics, and more specifically to a method for adapting the performance of gas turbine components based on measured gas path data. Background Technology

[0002] Gas turbines are crucial power machines in maritime, aviation, and energy transportation sectors, and their application in my country's energy and power industries is becoming increasingly widespread. However, gas turbines operate for extended periods in harsh environments characterized by high temperature, high pressure, and high speed, making them prone to gas path performance degradation faults such as compressor fouling, blade wear, and turbine thermal corrosion. Gas path performance simulation is a vital tool for early warning and diagnosis of gas turbine gas path faults. Accurate gas turbine component characteristic diagrams are the prerequisite and guarantee for gas path performance simulation. Figure 1 This information is usually obtained during factory testing, which is costly, expensive, and difficult to obtain because it is proprietary information of the manufacturer.

[0003] For gas turbine users, high-precision characteristic curves are generally unavailable, or they can only obtain publicly available general characteristic curves or formulas from existing literature. However, due to differences in manufacturing processes, installation errors, maintenance and disassembly, even gas turbines of the same model have differences in component characteristics. Therefore, general characteristic curves cannot be directly applied to actual gas turbines and need to be adapted according to these differences. Summary of the Invention

[0004] In view of this, the present invention provides a method for adapting the performance of gas turbine components based on measured gas path data, which can match the characteristic curves with the actual characteristics of gas turbine components.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A method for adapting the performance of gas turbine components based on measured gas path data includes the following steps:

[0007] Step S1: Using the general characteristic formula of the corresponding gas turbine component as the benchmark formula, select the adjustment factor;

[0008] Step S2: Set the initial value of the adjustment factor, define the domain and constraints;

[0009] Step S3: Using the measured parameter vector Y r The actual ratio characteristic parameter vector is obtained through a performance parameter reverse calculation subroutine.

[0010] Step S4: Based on the actual ratio characteristic parameter vector The analytical ratio characteristic parameter vector is obtained by improving the general characteristic formula of the component.

[0011] Step S5: Define fitness OF as the objective function, where fitness OF represents the average relative error between the analytical specific characteristic parameter and the actual specific characteristic parameter. The smaller the fitness OF, the higher the matching degree between the general component characteristic formula and the component characteristics of the actual gas turbine.

[0012] The fitness calculation is performed using an intelligent optimization algorithm, and the adjustment factor is updated. When the iteration accuracy is reached or the number of iterations is terminated, the output result is the optimal adjustment factor for improving the general component characteristic formula.

[0013] Step S6: Substitute the optimal adjustment factor into the general characteristic formula of the improved component to generate the component characteristic diagram with the highest matching degree with the actual gas turbine, thereby completing the performance adaptation of the gas turbine component.

[0014] Step S1 includes the following steps: 1) Selecting a general characteristic formula for compressor flow rate and selecting a corresponding adjustment factor; 2) Selecting a general characteristic formula for compressor efficiency and selecting a corresponding adjustment factor; 3) Selecting a general characteristic formula for turbine flow rate and selecting a corresponding adjustment factor; 4) Selecting a general characteristic formula for turbine efficiency and selecting a corresponding adjustment factor.

[0015] In step S1, the general characteristic formula for compressor flow rate is selected, and the corresponding adjustment factor is selected, specifically as follows:

[0016] The general characteristic formula for compressor flow rate represents the ratio of the converted speed. Specific pressure ratio Comparison of converted flow rate The strong nonlinear relationship between them is expressed by the following formula:

[0017]

[0018] in: Where α,β,γ, These are the selected adjustment factors related to the compressor flow characteristics, where a, m, and p are intermediate variables; The term is a newly added displacement improvement term based on the original general characteristic formula of compressor flow.

[0019] In step S1, the general characteristic formula for compressor efficiency is selected, and the corresponding adjustment factor is selected, specifically as follows:

[0020] The general formula for compressor efficiency characterizes the ratio of the reduced speed. Comparison of converted flow rate Isoentropy efficiency The strong nonlinear relationship between them is expressed by the following formula:

[0021]

[0022] Where λ,κ are the selected adjustment factors related to the compressor efficiency characteristics. This term is a new and improved term based on the original general characteristic formula of compressor efficiency.

[0023] In step S1, the turbine flow rate universal characteristic formula is selected, and the corresponding adjustment factor is selected, specifically as follows:

[0024] The general formula for turbine flow rate characterizes the ratio of the reduced speed. Specific pressure ratio Comparison of converted flow rate The strong nonlinear relationship between them is expressed by the following formula:

[0025]

[0026] Where τ is the selected adjustment factor related to turbine flow characteristics, and the subscript "0" indicates the value under design conditions.

[0027] In step S1, selecting the general characteristic formula for turbine efficiency and choosing the corresponding adjustment factor specifically involves:

[0028] The general formula for turbine efficiency characterizes the ratio of the speed to the rotational speed. Comparison of converted flow rate Isoentropy efficiency The relationship between them is strongly non-linear, as shown in the following formula:

[0029]

[0030] Where θ is the selected adjustment factor related to turbine efficiency characteristics.

[0031] In step S2, the initial value of the adjustment factor is set to a fixed value of the original general characteristic formula, and the domain and constraints of the adjustment factor are used to ensure that the generated characteristic map maintains the correct shape.

[0032] In step S3, the actual characteristic ratio parameter vector is obtained through the performance parameter reverse calculation subroutine. The specific process is divided into two internal and external circulations; the internal circulation is the compressor flow rate G. c The calculation relies on the iterative calculation of the oil-gas ratio; the external circulation is the iteration of the combustion chamber outlet temperature T3, which relies on the power balance between the compressor and the turbine, and the power balance between the power turbine and the load.

[0033] The input to the inverse solution algorithm is: the measurable gas path parameter Y. mControl parameter u, and preset unmeasurable parameters T3 and G c The algorithm calculation process is as follows: by analyzing the compressor flow rate G... c The performance parameters X = [n] of each component are obtained by iterative calculation of the combustion chamber outlet temperature T3 and the combustion chamber outlet temperature T3. c ,π c G c ,η c ,n t ,π t G t ,η t ,n pt ,π pt G pt ,η pt ], and the unmeasurable gas path parameter Y um = [T3, P3], the output of the solution algorithm is: the complete component parameter vector V total =[Y,X,u]=[Y m ,Y um [,X,u] is used for the calculation of specific characteristic parameters.

[0034] In step S5, the intelligent optimization algorithm includes particle swarm optimization, genetic algorithm, and ant colony optimization.

[0035] Beneficial effects:

[0036] 1. This invention reverse-calculates component characteristic parameters under the condition of only partial field operation data, and optimizes the adjustment factor of the component's general characteristic formula through intelligent optimization algorithm to obtain the optimal adjustment factor and achieve performance self-adaptation. Thus, when there is insufficient experimental data to generate accurate component characteristic diagrams, the general characteristic formula is adapted based on discrete field measured data of gas turbines, improving the accuracy of component characteristic diagrams and facilitating high-precision gas turbine gas path simulation and condition monitoring.

[0037] 2. This invention improves the initial component general characteristic formula by adding a displacement term and corresponding adjustment factor to the compressor component general formula, which greatly improves the adaptive adjustment capability of the general characteristic formula and increases the upper limit of the global adaptability of the improved component general characteristic formula.

[0038] 3. The performance parameter reverse calculation subroutine of the intermediate process of this invention can be used independently to solve the characteristic parameter values ​​of each component, providing a method for calculating actual characteristic parameters, which can be used for offline analysis of gas turbine gas circuit performance degradation or gas circuit faults.

[0039] 4. In this invention, the improved particle swarm optimization algorithm has stronger global search capability and stronger local convergence capability, and can output the optimal adjustment factor more stably.

[0040] 5. This invention does not require calling the gas turbine gas path performance simulation model during the performance adaptive calculation process, resulting in a small computational load and extremely high computational efficiency. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the adaptation method of the present invention;

[0042] Figure 2 This is a schematic diagram of the structure and data measurement point parameters of the gas turbine components used in this embodiment of the invention;

[0043] Figure 3 These are the measured control parameters required for the adaptation method in the embodiments of the present invention;

[0044] Figure 4 This is a flowchart of the performance reverse calculation subroutine in an embodiment of the present invention;

[0045] Figure 5 This is a comparison chart of compressor performance parameters before and after adaptation in an embodiment of the present invention;

[0046] Figure 6 This is a comparison chart of compressor performance parameter errors before and after adaptation in an embodiment of the present invention;

[0047] Figure 7 This is a comparison diagram of measurable parameters before and after adaptation in an embodiment of the present invention.

[0048] Figure 8 This is a comparison chart of the errors in measurable parameters before and after adaptation in an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] One approach to achieving a characteristic curve for a gas turbine is to adaptively modify a general characteristic curve or formula. Compared to factory testing, this approach is less costly and has strong generalization capabilities. However, effectively modifying the general characteristic curve or formula based on limited user data remains a significant challenge. This invention provides a method for adapting the performance of gas turbine components based on measured gas path data. Based on the measured gas path data, the actual characteristic ratio parameters of the component are calculated through reverse performance calculation; the analytical characteristic ratio parameters are obtained by improving the general characteristic formula; the optimal adjustment factor for improving the general characteristic formula of the component is found through an optimization algorithm; and the optimal adjustment factor is substituted into the improved general characteristic formula of the component, resulting in the highest degree of matching between the improved general characteristic formula and the actual gas path characteristics of the gas turbine. The process is shown in the attached figure. Figure 1 As shown, the specific steps include the following:

[0051] Step S1: Using the general characteristic formula of the corresponding gas turbine component as the benchmark formula, an adjustment factor is selected to obtain the improved general characteristic formula of the component. Among them, the general characteristic formula for component flow rate represents the relationship between the equivalent speed n, pressure ratio π, and equivalent flow rate G (the general characteristic formula for compressor flow rate is a parabolic formula group, and the general characteristic formula for turbine flow rate is an improved Fleuger formula group); the general characteristic formula for component efficiency represents the relationship between the equivalent speed n, equivalent flow rate G, and efficiency η (all general characteristic formulas for efficiency adopt a parabolic formula group). All characteristic parameters (speed, pressure ratio, flow rate, efficiency) are expressed as ratio parameters in the general formula, such as ratio equivalent speed: n c T1 represents the actual compressor speed, T1 represents the actual compressor inlet temperature, and the subscript "0" indicates the design operating conditions for both speed and temperature; specific isentropic efficiency: η c η represents the actual isentropic efficiency of the compressor. c0 The isentropic efficiency under design conditions is referred to simply as efficiency. For gas turbines containing a power turbine, the general characteristic formulas for flow rate and efficiency are the same as those for turbines.

[0052] Step S2: Set the initial value of the adjustment factor, define the domain and constraints;

[0053] Step S3: Using the measured parameter vector Y r The actual ratio characteristic parameter vector is obtained through a performance parameter reverse calculation subroutine.

[0054] Step S4: Based on the actual ratio characteristic parameter vector The analytical ratio characteristic parameter vector is obtained by improving the general characteristic formula of the component.

[0055] Step S5: Define fitness OF as the objective function, where fitness OF represents the average relative error between the analytical specific characteristic parameter and the actual specific characteristic parameter. The smaller the fitness OF, that is, the smaller the error between the analytical specific characteristic parameter and the actual specific characteristic parameter, the higher the matching degree between the general component characteristic formula and the component characteristics of the actual gas turbine.

[0056] A smart optimization algorithm is used to calculate the fitness and update the adjustment factor. When the iteration accuracy is reached or the number of iterations is terminated, the output result is the optimal adjustment factor for improving the general component characteristic formula.

[0057] Step S6: Substitute the optimal adjustment factor into the general characteristic formula of the improved component to generate the component characteristic diagram with the highest matching degree with the actual gas turbine, thereby completing the performance adaptation of the gas turbine component.

[0058] Step S1 includes the following steps: 1) Select the general characteristic formula for compressor flow rate and select the corresponding adjustment factor; 2) Select the general characteristic formula for compressor efficiency and select the corresponding adjustment factor; 3) Select the general characteristic formula for turbine flow rate and select the corresponding adjustment factor; 4) Select the general characteristic formula for turbine efficiency and select the corresponding adjustment factor.

[0059] Specifically, the general characteristic formula for compressor flow rate is selected, along with the corresponding adjustment factor. The general characteristic formula for compressor flow rate characterizes the reduced rotational speed. Specific pressure ratio Comparison of converted flow rate The strong nonlinear relationship between them is expressed by the following formula:

[0060]

[0061] in: Where α,β,γ, These are the four selected adjustment factors related to the compressor flow characteristics, with a, m, and p being intermediate variables. Specifically, This item is a new displacement improvement item added to the original general characteristic formula of compressor flow rate in this invention.

[0062] Select the general characteristic formula for compressor efficiency and the corresponding adjustment factor. Specifically, the general characteristic formula for compressor efficiency characterizes the ratio-reduced speed. Comparison of converted flow rate Isoentropy efficiency The strong nonlinear relationship between them is expressed by the following formula:

[0063]

[0064] Where λ and κ are two selected adjustment factors related to the compressor efficiency characteristics. Specifically, This item is a new and improved item in this invention based on the original general characteristic formula of compressor efficiency.

[0065] Select the general characteristic formula for turbine flow rate and the corresponding adjustment factor, specifically: the general characteristic formula for turbine flow rate characterizes the specific converted speed. Specific pressure ratio Comparison of converted flow rate The strong nonlinear relationship between them is expressed by the following formula:

[0066]

[0067] Wherein, τ is a selected adjustment factor related to turbine flow characteristics, and the subscript "0" indicates the value under design conditions.

[0068] Select the general characteristic formula for turbine efficiency and the corresponding adjustment factor. Specifically, the general characteristic formula for turbine efficiency characterizes the specific reduced speed. Comparison of converted flow rate Isoentropy efficiency The relationship between them is strongly non-linear, as shown in the following formula:

[0069]

[0070] Wherein, θ is a selected adjustment factor related to turbine efficiency characteristics.

[0071] In step S2, the initial value of the adjustment factor is set to a fixed value of the original general characteristic formula. The domain and constraints of the adjustment factor are used to ensure that the generated characteristic map maintains the correct shape. This embodiment is detailed below:

[0072] 1) Initial value: The subscript "o" indicates the initial value of the adjustment factor.

[0073] 2) Domain: And ε = 0.1

[0074] 3) Constraints: Set constraints on the shape adjustment factors related to the compressor flow characteristics. α, β, and γ should satisfy β > 1.15 + 0.1·α and γ > 2·(β-1) to ensure that the compressor flow characteristic curves do not intersect.

[0075] Step S3 includes the following steps: 1) Obtain the on-site measurable parameter vector Y r ;2) Reverse calculation of performance parameters;3) Actual specific characteristic parameter vector calculate;

[0076] Specifically, obtain the on-site measurable parameter vector Y. r The process is as follows: The data in this embodiment was obtained during field testing of a certain type of split-shaft gas turbine. At the gas turbine operating site, the number of sensors that can be deployed is limited. The measurable parameters of the sensors are the measurable parameter vector Y. r =[Y m [,u,N] includes: measurable component inlet and outlet temperatures and pressures Y m =[T2,T4,T5,P2,P4,P5]; Control parameter u = [T1,P1,G f [The data is determined by ambient temperature T1, pressure P1, and fuel flow rate G.] f Composition; Rotational speed vector N = [n c ,n pt [, determined by the compressor speed n] c and power turbine speed n ptComposition. Each data set was measured at 20-minute intervals, totaling 2367 sets. Fifty sets were selected for verification in this example. These 50 sets of data represent operating conditions from low to high, and generally show a uniform increasing trend. Figure 2 This is a schematic diagram of the gas turbine component structure and data measurement point parameters used in this embodiment. The control parameters in the 50 sets of data used for component performance adaptation calculations in this embodiment are as follows: Figure 3 As shown, the operating conditions indicated by the data show an increasing trend from low to high, which can be used for adaptive accuracy verification under all operating conditions.

[0077] The reverse calculation process for performance parameters is as follows: The reverse calculation consists of two loop processes. The inner loop is the compressor flow rate G. c The calculation relies on iterative calculation of the air-fuel ratio. The external circulation calculation, based on the combustion chamber outlet temperature T3, depends on the power balance between the compressor and turbine, and the power balance between the power turbine and the load. The input to the inverse solution algorithm is the measurable air path parameter Y. m Control parameter u, and preset unmeasurable parameters T3 and G c The algorithm calculation process is as follows: by analyzing the compressor flow rate G... c The performance parameters X of each component are obtained by iterative calculation of the combustion chamber outlet temperature T3 and the combustion chamber outlet temperature T3. r =[n cr ,π cr G cr ,η cr ,n tr ,π tr G tr ,η tr ,n ptr ,π ptr G ptr ,η ptr ], and the unmeasurable gas path parameter Y um = [T3, P3] (i.e., combustion chamber outlet temperature T3 and pressure P3). The output of the solution algorithm is: the complete component parameter vector V. total =[Y,X r ,u]=[Y m ,Y um ,X r ,u], and used for actual specific characteristic parameter calculation. Figure 4 This is a flowchart of the performance reverse calculation subroutine that can be used independently in this invention.

[0078] Actual ratio characteristic parameter vector The calculation process is as follows: The complete component parameter vector V is obtained through reverse calculation based on the performance parameters. total Solving for the problem, we get:

[0079]

[0080]

[0081] The subscript "0" indicates the value under design conditions. Specifically, this includes the specific converted speeds of the compressor, turbine, and power turbine components. Specific pressure ratio Comparison of converted flow rate Efficiency There are a total of 12 specific characteristic parameters, written in vector form, with the subscript "r" indicating the actual specific characteristic parameters obtained by the reverse calculation method.

[0082] Step S4 specifically involves: According to equation (1), the specific pressure ratio in the actual specific characteristic parameters can be used... With specific speed Calculate the compressor's analytical specific flow rate. According to equation (2), the specific flow rate in the actual characteristic parameters is... With specific speed Calculate the compressor's analytical ratio efficiency. According to equation (3), the specific pressure ratio in the actual specific characteristic parameters can be used to determine the specific pressure ratio. With specific speed Calculate the turbine analytical flow rate According to equation (4), the specific flow rate in the actual characteristic parameters is... With specific speed Calculate the turbine analytical ratio efficiency Similarly, the analytical flow rate of the power turbine can be calculated. And the efficiency ratio of power turbine resolution To distinguish it from the actual specific characteristic parameters, the subscript "s" indicates the analytical specific characteristic parameters, which are obtained as follows:

[0083]

[0084] In step S5, fitness OF is defined as the objective function, and the calculation formula is shown below:

[0085]

[0086] Where q is the number of specific characteristic parameters in one set; n is the number of measurement data sets participating in adaptive optimization; It is the analytical ratio characteristic parameter value, representing and These are the actual component characteristic parameter values, representing and

[0087] Many intelligent optimization algorithms are available, such as particle swarm optimization (PSO), genetic algorithm, and ant colony optimization (ACO). This embodiment uses PSO as the intelligent optimization algorithm. The adjustment factor is updated based on the individual particle and the globally optimal position (the adjustment factor value corresponding to the minimum fitness). The basic formula for PSO is:

[0088]

[0089] Where c1 is the individual acceleration constant; c2 is the global acceleration constant; r1 and r2 are random numbers in the range of 0 to 1; P best It is the optimal position for the current individual; G best It is the globally optimal position among all particles; i is the i-th particle; S k It is the position of the k-th generation particle, S k+1 It is the position of the (k+1)th generation particle; V k V is the velocity of the kth generation particle. k+1 ω is the velocity of the (k+1)th generation particle. The population size is set to 60-100; the termination iteration count is set to 100-150; ω is the inertia weight. Specifically, in this invention, the inertia weight does not use a traditional fixed value, but is improved to a weight that varies with the iteration count, possessing superior global search capability and local convergence capability. The calculation formula is as follows:

[0090]

[0091] Where, ω min and ω max The preset minimum and maximum inertia weights; f k,mean f is the average fitness of all particles in generation k; k,min It is the smallest fitness among all particles in the kth generation; It is the fitness of the i-th particle in the k-th generation.

[0092] In this embodiment, the adjustment factor is updated according to formulas (8) and (9). When the iteration accuracy is reached or the number of iterations is terminated, the output result is the optimal adjustment factor of the improved general component characteristic formula.

[0093] This invention further compares the adapted output results with the actual values ​​to verify the effectiveness of the adaptation method. Specifically, this includes: 1) comparing the error between the adapted analytical characteristic ratio parameter and the actual ratio parameter; 2) comparing the error between the adapted performance model output parameter and the measured parameter.

[0094] Specifically, Figure 5 The graph shows a comparison of the compressor flow and efficiency characteristics before and after adaptation, demonstrating that the performance parameters have a very high degree of matching with the actual parameters after optimization using the method proposed in this invention. Figure 6The graph shows a comparison of the compressor flow and efficiency characteristic parameters before and after adaptation. Under low operating conditions, the maximum error decreased from 50% and 11.6% before adaptation to 1.75% and -1.2%, respectively, indicating that the error was significantly reduced after adaptive adjustment. Figure 7 These are the measurable parameters before and after adaptation (compressor speed n). c The comparison chart of outlet temperature T2 and outlet pressure P2 shows that after adaptive optimization by the method proposed in this invention, the simulated output measurable parameters and the measured parameters have a very high matching degree. Figure 8 These are the measurable parameters before and after adaptation (compressor speed n). c The error comparison chart for outlet temperature T2 and outlet pressure P2 shows that the errors decreased from the original -16.3%, -6%, and -5.5% to -0.28%, -0.5%, and -2.5%, respectively, indicating that the errors were significantly reduced after adaptive adjustment.

[0095] In summary, the above are merely embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of gas turbine component performance adaptation based on measured gas path data, characterized by, The method comprises the following steps: Step S1, taking a general characteristic formula of a gas turbine component of a corresponding model as a reference formula, and selecting an adjustment factor; Step S2, setting an initial value, a definition domain and a constraint condition of the adjustment factor; Step S3, using the measured parameter vector Y r The actual bit characteristic parameter vector is obtained by the performance parameter reverse calculation subprogram Step S4, deriving an analytical bit characteristic parameter vector based on the actual bit characteristic parameter vector and the improved component general characteristic formula Step S5, defining fitness OF as a target function, wherein the fitness OF represents an average relative error between an analytical characteristic parameter and an actual characteristic parameter, and the smaller the fitness OF is, the higher the matching degree of the general component characteristic formula and the actual component characteristic of the gas turbine is; An intelligent optimization algorithm is used to calculate the fitness, and the adjustment factor is updated, and when an iteration precision or a termination iteration number is reached, the output result is the optimal adjustment factor of the improved general component characteristic formula; Step S6, substituting the optimal adjustment factor into the improved general component characteristic formula, so that a component characteristic graph with the highest matching degree with the actual gas turbine is generated, thereby completing the component performance adaptation of the gas turbine; In the step S1, the following steps are included: 1) selecting a general characteristic formula of a compressor flow rate, and selecting a corresponding adjustment factor; 2) selecting a general characteristic formula of a compressor efficiency, and selecting a corresponding adjustment factor; 3) selecting a general characteristic formula of a turbine flow rate, and selecting a corresponding adjustment factor; 4) selecting a general characteristic formula of a turbine efficiency, and selecting a corresponding adjustment factor; In the step S2, the initial value of the adjustment factor is set as a fixed value of an original general characteristic formula, and the definition domain and the constraint condition of the adjustment factor are used to ensure that the generated characteristic graph maintains a correct shape; In the step S3, the actual characteristic ratio parameter vector is solved by the performance parameter reverse calculation subprogram The specific process is divided into two inner loops and an outer loop; the inner loop is the calculation of the compressor flow G c which depends on the oil-gas ratio iterative calculation; the outer loop is the iteration of the combustion chamber outlet temperature T3 which depends on the power balance of the compressor and turbine and the power balance of the power turbine and load; The input of the inverse solution algorithm is: measurable gas path parameters Y m , control parameters u, and preset unmeasurable parameters T3 and G c The algorithm calculation process is: through iterative calculation of the compressor flow G c and the combustion chamber outlet temperature T3, the performance parameters X of each component are inversely calculated, X = [n c , π c , G c , η c , n t , π t , G t , η t , n pt , π pt , G pt , η pt ], and unmeasurable gas path parameters Y um = [T3, P3], the output of the solution algorithm is: the complete component parameter vector V total = [Y, X, u] = [Y m , Y um , X, u], which is used for specific characteristic parameter calculation.

2. The method of claim 1, wherein, In the step S1, the general characteristic formula of the compressor flow rate is selected, and the corresponding adjustment factor is selected, and specifically: Generalized performance formula of compressor flow Specific pressure ratio Specific flow The strong nonlinear relationship between them is as follows: wherein: wherein a, β, γ, are selected adjustment factors related to the compressor flow characteristics, a, m and p are intermediate variables; The term is a newly added displacement improvement term based on the compressor flow original general characteristic formula.

3. The method of claim 1, wherein, In the step S1, the general characteristic formula of the compressor efficiency is selected, and the corresponding adjustment factor is selected, and specifically: Generalized characteristic equation of compressor efficiency Specific converted flow rate Specific isentropic efficiency The strong nonlinear relationship between them is as follows: where λ, κ are selected adjustment factors related to the compressor efficiency characteristics, Item is the new improvement based on the original general formula of compressor efficiency.

4. The method of claim 1, wherein, In the step S1, the general characteristic formula of the turbine flow rate is selected, and the corresponding adjustment factor is selected, and specifically: General characteristic equation of turbine flow represents specific converted speed Specific pressure ratio Specific converted flow The strong nonlinear relationship between them is as follows: Wherein, τ is the selected adjustment factor related to the turbine flow rate characteristic, and the subscript "0" represents a value under a design condition.

5. The method of claim 1, wherein, In the step S1, the general characteristic formula of the turbine efficiency is selected, and the corresponding adjustment factor is selected, and specifically: Turbine efficiency general characteristic formula represents specific converted speed Specific converted flow rate Specific isentropic efficiency The relationship between them is a strong nonlinear relationship, and the formula is as follows: Wherein, θ is the selected adjustment factor related to the turbine efficiency characteristic.

6. The method of claim 1, wherein, In the step S5, the intelligent optimization algorithm includes a particle swarm algorithm, a genetic algorithm and an ant colony algorithm.

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