A method for error propagation and control in full three-dimensional performance simulation of aero-engine

Abstract

This invention provides a method for error propagation and control in full three-dimensional performance simulation of an aero-engine, relating to the field of aero-engine performance simulation technology. The method includes the following steps: decomposing the aero-engine into multiple core components along the airflow direction and establishing aerodynamic and thermodynamic coupling simulation models between these components; identifying and quantifying the simulation error sources of each component and defining the simulation error values ​​for each component; constructing an overall engine error propagation network, including sensitivity weights and coupling propagation coefficients; calculating the overall engine cumulative error based on the sensitivity weights and coupling propagation coefficients; and during the full three-dimensional simulation of the entire engine, collecting simulation data of the outlet airflow parameters of each component, comparing the simulation data with experimental data, and iteratively correcting the simulation models of each component and the overall engine cumulative error based on the comparison results. This invention improves the overall engine simulation accuracy, ultimately reducing the overall engine simulation error to within 2.5%, thus enhancing the engineering reliability of the simulation results.

Description

Technical Field

[0001] This invention relates to the field of aero-engine performance simulation technology, specifically to a method for error transmission and control in full three-dimensional performance simulation of an aero-engine. Background Technology

[0002] Aero-engines are the core power equipment of aviation systems, and the accuracy of their performance simulation directly determines the development cycle, cost, and reliability in service. With the in-depth application of full 3D numerical simulation technology, multi-physics coupled simulations are needed for multiple components such as fans, compressors, combustion chambers, turbines, afterburners, and nozzles to reveal the coupled flow patterns of upstream and downstream components, thus comprehensively reflecting the overall performance level of the engine. Therefore, the simulation accuracy of each component has a significant impact on the overall performance simulation accuracy of the engine. However, current full 3D simulation of the entire engine faces the following core challenges:

[0003] (1) The error propagation mechanism of upstream and downstream components is unclear. Existing methods mainly perform error calibration for individual components (such as compressors and turbines) without considering the coupling relationship between multiple components of the whole machine. The simulation output of the previous component (such as compressor outlet temperature and pressure) is the input boundary of the next component (such as combustion chamber), and the simulation results of the next component will affect the calculation iteration of the previous component. At the same time, the propagation mechanism of simulation errors of each component (such as mesh discretization error, turbulence model error, airflow parameter error uncertainty, etc.) in the whole machine coupled simulation process is unclear. The error will be propagated step by step along the entire link from the engine intake fan to the engine nozzle, resulting in an increase in the whole machine level error and low whole machine simulation accuracy.

[0004] (2) Lack of quantitative cumulative model. The influence weights of errors of each component (such as fan + 1%, compressor + 1%, combustion chamber + 2%) are different (for example, the influence of combustion chamber on thrust and fuel consumption rate is much more sensitive than that of bypass duct). Existing technologies mostly use simple algebraic superposition to calculate cumulative error, without introducing "component sensitivity weight" and "coupling transfer coefficient", which makes it impossible to accurately quantify the final error of the whole machine.

[0005] (3) Poor universality of control strategies. Currently, there is a lack of systematic component error correction and overall engine error accumulation control strategies. Existing error control mostly adopts general methods such as "unified mesh densification" and "global model correction," without developing differentiated error control schemes for different component error sources (such as combustion chamber errors mainly from chemical reaction model simplification, turbine errors mainly from cold air point source position deviation, and rotor-static interface data transmission errors, etc.), resulting in limited accuracy improvement. At the same time, the integration of test data and simulation of each component is low, failing to fully utilize component test data to control the accuracy of the simulation model of each component of the engine, resulting in low utilization of test resources and low simulation confidence.

[0006] The aforementioned problems result in deviations of more than 5% between the full 3D simulation results and experimental data, making it difficult to meet the engineering application requirement of "simulation deviation ≤ 2.5%". This restricts the shortening of engine design cycle and the efficiency of performance optimization. There is an urgent need for a method that can accurately analyze the error propagation law and realize active control of the simulation error of the whole engine based on experimental data, in order to improve the accuracy of the full 3D performance simulation of the aero-engine. Summary of the Invention

[0007] To address the shortcomings of existing technologies, such as "uncoupled analysis of error propagation, lack of quantitative models for cumulative errors, and lack of differentiated control strategies," this application provides a method for error propagation and control in full three-dimensional performance simulation of an aero-engine. Through a technical path of "quantification of component errors - correction of test data - cumulative control of overall engine errors," the method improves the accuracy of overall engine simulation and ultimately reduces the overall engine simulation error to within 2.5%, thereby enhancing the engineering reliability of the simulation results.

[0008] This application provides the following technical solution: a method for error transmission and control in full three-dimensional performance simulation of an aero-engine, comprising:

[0009] Step S1. Decompose the aero-engine into multiple core components along the airflow direction, and establish a full three-dimensional simulation model of the whole engine with aerodynamic and thermodynamic coupling between each component to determine the transmission relationship of airflow parameters between each component;

[0010] Step S2. Identify the simulation error sources of each component and define the component simulation error value, wherein the component simulation error value is the relative deviation between the simulation results and experimental values ​​of the key performance parameters of the component;

[0011] Step S3. Based on the full three-dimensional simulation model of the whole machine, define the sensitivity weight of each component and the coupling transmission coefficient between components to construct the whole machine error transmission network; wherein, the sensitivity weight characterizes the degree of influence of component error on the whole machine performance; the coupling transmission coefficient characterizes the amplification or attenuation effect of upstream component error on downstream component error;

[0012] Step S4. Based on the component simulation error value, the sensitivity weight, and the coupling transfer coefficient, calculate the total error of the whole machine using a weighted-coupling two-factor model;

[0013] Step S5. During the full 3D simulation of the whole machine, collect the simulation data of the outlet airflow parameters of each component, compare the collected simulation data with the test data of the corresponding component, and obtain the component simulation error; based on the component simulation error, iteratively correct the simulation model parameters of the component based on the whole machine error propagation network, and repeat steps S3 to S5 until the cumulative error of the whole machine meets the preset accuracy threshold.

[0014] According to one embodiment of this application, the formula for calculating the cumulative error of the entire machine is as follows:

[0015]

[0016] in, Let i be the sensitivity weight. For component i, simulation error, Let j be the sensitivity weight of component j. Let j be the simulation error of component j, and n be the number of core components.

[0017] According to one embodiment of this application, the value range of the coupling transfer coefficient is 0-0.2.

[0018] According to one embodiment of this application, the sensitivity weight is calculated by the control variable method, and the sensitivity weight of each component is in the range of 0-1.

[0019] According to one embodiment of this application, in step S2, the simulation error sources include numerical simulation error and geometric model error; the numerical simulation error includes errors caused by at least one of mesh resolution, static-to-dynamic interface data transmission, turbulence model selection, and combustion reaction model; the geometric model error includes errors caused by at least one of engine cold and hot state geometric differences, machining and assembly errors, adjustable guide vane root tip clearance deviation, and working fluid physical property parameters.

[0020] According to one embodiment of this application, in step S5, a virtual monitoring point is set at the component outlet, and airflow parameters are collected according to a set time step. The airflow parameters include temperature, pressure and flow rate.

[0021] According to one embodiment of this application, after step S5, the method further includes: step S6: using measured data from the engine's full-process ground test or high-altitude test to verify the corrected full three-dimensional simulation model of the engine, confirming that the simulation error of the engine's key performance parameters is within the engineering allowable range.

[0022] According to one embodiment of this application, the core components include an intake section, a fan, a compressor, an outer bypass duct, a combustion chamber, a turbine, an afterburner, a nozzle, and an exhaust section.

[0023] Compared with existing technologies, the beneficial effects achieved by at least one of the above-mentioned technical solutions adopted in the embodiments of this specification include at least the following: The advantage of the error propagation and control method for full three-dimensional performance simulation of aero-engine proposed in the embodiments of this invention lies in the fact that, for the complex flow process of full three-dimensional simulation of the entire engine, a quantifiable error propagation model is proposed, which can accurately quantify the contribution weight of the errors of each component to the entire engine, thus solving the problem of the "error black box" in traditional simulation. For the first time, "sensitivity weight + coupling coefficient" is introduced to establish an engine-level error propagation model, solving the problem that traditional "isolated analysis" cannot reflect the coupling effect of components. Differentiated solutions are formulated for the error sources of different components (such as adjusting the reaction mechanism of the combustion chamber, adjusting the position of the cooling gas point source of the turbine, etc.), which can significantly improve the accuracy compared with single and general methods such as "global mesh densification". At the same time, it makes full use of component test data resources, realizes accurate correction of single component simulation models and dynamic control of the overall engine error, and can improve the simulation confidence by more than 30%. Finally, this invention can utilize systematic errors to control overall simulation errors, improve the reliability of simulation data, and shorten the time and repeated iterations of full 3D simulation of the entire engine. The effect is significant and it is applicable to simulation error analysis and accuracy optimization of various aero-engines such as turbofan and turbojet engines. It can be used to improve the simulation accuracy of aero-engines and support engine design iteration, performance prediction and error tracing. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating the error propagation and control method for full three-dimensional performance simulation of the entire aero-engine of the present invention.

[0026] Figure 2 This is a schematic diagram of an embodiment of the error propagation and control method for full three-dimensional performance simulation of an aero-engine according to the present invention;

[0027] Figure 3 This is a schematic diagram of the overall error transmission network of an aero-engine according to an embodiment of the present invention. Detailed Implementation

[0028] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0029] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] like Figure 1 As shown, this embodiment of the invention provides a method for error propagation and control in full three-dimensional performance simulation of an aero-engine, including:

[0031] Step S1. Decompose the aero-engine into multiple core components along the airflow direction, and establish a full three-dimensional simulation model of the whole engine with aerodynamic and thermodynamic coupling between each component to determine the transmission relationship of airflow parameters between each component;

[0032] Step S2. Identify the simulation error sources of each component and define the component simulation error value, wherein the component simulation error value is the relative deviation between the simulation results and experimental values ​​of the key performance parameters of the component;

[0033] Step S3. Based on the full three-dimensional simulation model of the whole machine, define the sensitivity weight of each component and the coupling transmission coefficient between components to construct the whole machine error transmission network; wherein, the sensitivity weight characterizes the degree of influence of component error on the whole machine performance; the coupling transmission coefficient characterizes the amplification or attenuation effect of upstream component error on downstream component error;

[0034] Step S4. Based on the component simulation error value, the sensitivity weight, and the coupling transfer coefficient, calculate the total error of the whole machine using a weighted-coupling two-factor model;

[0035] Step S5. During the full 3D simulation of the whole machine, collect the simulation data of the outlet airflow parameters of each component, compare the collected simulation data with the test data of the corresponding component, and obtain the component simulation error; based on the component simulation error, iteratively correct the simulation model parameters of the component based on the whole machine error propagation network, and repeat steps S3 to S5 until the cumulative error of the whole machine meets the preset accuracy threshold.

[0036] In some embodiments, after step S5, the method further includes: step S6: using measured data from the engine's full-process ground test or high-altitude test to verify the corrected full three-dimensional simulation model of the engine, confirming that the simulation error of the engine's key performance parameters is within the engineering allowable range.

[0037] The method of this invention uses "transfer modeling → error quantification → cumulative calculation → control correction → verification closed loop" as its core process. Based on the error distribution patterns of the engine fan → compressor → combustion chamber → turbine → afterburner → nozzle, the technical solution is as follows:

[0038] (1) Decomposition and process modeling of the whole engine components. The aero-engine is decomposed into core components such as intake section, fan, compressor, bypass duct, combustion chamber, turbine, afterburner, nozzle and exhaust section along the airflow direction. A full three-dimensional simulation model of the whole engine is established between each component. This model covers the internal aerodynamic and thermodynamic coupling simulation model of the engine, clarifies the transmission relationship of airflow parameters (temperature, pressure, speed, flow rate, etc.) between components, and constructs the "digital network" of the whole engine simulation.

[0039] (2) Component error source identification and error quantification model construction. The simulation error sources for each engine component are categorized as follows: ① Numerical simulation errors: covering errors in mesh resolution, data transfer at the rotor-static interface, turbulence model selection, and combustion reaction model. ② Geometric model errors: differences in the engine's overall cold and hot geometric models, actual machining and assembly errors, adjustable guide vane root tip clearance deviation, and working fluid physical properties. Different components have different error sources and require separate analysis based on their characteristics. Subsequently, for each component of the engine, error sources and quantification indicators are defined: Let the simulation error of the i-th component (i=1 for the fan, i=2 for the compressor, i=3 for the combustion chamber, i=4 for the turbine, i=5 for the afterburner, i=6 for the nozzle, and i=7 for the bypass duct) be... Its physical meaning is the relative deviation between the simulation results and experimental values ​​of key performance parameters (temperature, pressure, flow rate, efficiency) of the component, that is:

[0040]

[0041] in, The simulation results for component i are as follows. Key parameters (flow rate, pressure ratio, etc.) were obtained from the test characteristic diagrams of the corresponding components.

[0042] (3) Construct the overall error propagation network. Based on the physical coupling relationship between the components, two types of key parameters are defined: ① Sensitivity weight This characterizes the impact of component i's error on overall engine performance (such as thrust and fuel consumption rate). It is calculated using the controlled variable method (keeping other components fixed and only changing the error of component i, observing the rate of change in overall engine performance), satisfying the following conditions: (n is the number of core components). ② Coupling transfer coefficient : Characterizes the amplification / attenuation effect of the error of component i on the error of component j (j>i, i.e., downstream component) through component coupling and interface, with a value range of 0 ≤ ≤0.2 (e.g., a measured statistical value of a certain engine, such as the compressor → combustion chamber) =0.1, Turbine → Afterburner =0.15).

[0043] Specifically, the full three-dimensional simulation model of the entire engine, constructed in step 1, is used. This model encompasses the aerodynamic and thermodynamic coupling relationships of each component. Based on the specific characteristics of each component of the target engine, the error sensitivity weight and coupling transmission coefficient of each component are identified (e.g., the compressor sensitivity weight is...). Coupling transfer coefficient The acquisition of this data involved identifying potential factors affecting the simulation accuracy of each component (mesh resolution, geometric model accuracy, root tip clearance, etc.), and forming a network diagram of the overall engine error transmission from the engine inlet to the outlet.

[0044] (4) Calculate the cumulative error of the whole machine. Introduce the "weight-coupling two-factor" model, which covers the main sources of simulation error generated by each component individually. And based on the additional error term caused by the coupling relationship between upstream and downstream components: By integrating the error propagation mesh of all components from the engine inlet to outlet, a formula for calculating the cumulative error of the entire engine was formed. This model breaks through the limitations of traditional simple superposition and achieves a higher cumulative error of the entire engine. The calculation formula is:

[0045]

[0046] The first term is the weighted contribution of the errors of each component, and the second term is the additional error caused by the coupling between components (such as the turbine + 2% error and the afterburner + 2% error will generate additional errors through coupling).

[0047] (5) Accumulated error control and iterative correction of the whole machine to realize closed-loop correction of simulation-experiment. During the full three-dimensional simulation of the whole machine, virtual monitoring points (such as fan outlet, compressor outlet, combustion chamber outlet, etc.) are set at the outlet of each component, and the outlet airflow parameters (temperature, pressure, flow rate, etc.) of the component are collected at a certain time step; at the same time, the actual outlet airflow parameters of the component under the operating condition can be obtained by calculating the relative conversion speed value of the operating condition using the test characteristic line data of each component; at this time, the simulation data is compared with the data results of the corresponding component to obtain the deviation between the two. By employing parameter inversion and model correction techniques, and utilizing the error propagation network constructed in step three, the simulation model parameters of the component are adjusted. This identifies key error sources that may affect component deviations (such as the type of interface between components, the loss coefficient of the transition-to-static interface, compressor root tip clearance, combustion chamber chemical reaction model, and mixing of turbine cold air with the mainstream). This achieves consistency correction between single-component simulation and experimental results, ensuring that the simulation results for each component do not deviate from the experimental results. The computation can then be considered converged. Simultaneously, the cumulative effect of each component's error in the coupled simulation of the entire machine is calculated using the method in step four. If the overall machine error exceeds the allowable limit (e.g., accuracy threshold), the error control model feeds back to the corresponding component, and the simulation parameters of that component are corrected using experimental data. This process is repeated 3-5 times until the overall machine's cumulative error... At this point, it is assumed that the overall simulation error meets the simulation accuracy requirements.

[0048] (6) Verification of the accuracy of the whole machine simulation. The corrected whole machine full three-dimensional simulation model was verified by using measured data from ground tests and high-altitude tests of the entire engine process. The simulation error of key engine parameters (inlet flow rate, thrust, fuel consumption rate, exhaust temperature) and other core indicators was verified to be within the allowable range of engineering.

[0049] In one specific embodiment, the error propagation in a full 3D simulation of a turbofan engine is used. This engine consists of a three-stage fan, a seven-stage compressor, an annular combustion chamber, a single-stage high-pressure turbine, a single-stage low-pressure turbine, an afterburner, an outer bypass duct, and nozzles. The proposed specific implementation steps in the full 3D simulation of the engine include six parts: component decomposition and process modeling, component error source identification and error quantification model construction, construction of the engine error propagation network, calculation of the engine cumulative error, engine error accumulation control and iterative correction, and engine simulation accuracy verification. Figure 2 As shown.

[0050] 1. Overall component decomposition and process modeling

[0051] Based on the whole-machine simulation error analysis method, the whole machine is divided into 8 components: a three-stage fan, a seven-stage compressor, an annular combustion chamber, a single-stage high-pressure turbine, a single-stage low-pressure turbine, an afterburner, an outer bypass duct, and a nozzle. Aerodynamic and thermodynamic coupling simulation models are established for each component in sequence to clarify the transmission relationship of airflow parameters (temperature, pressure, velocity, flow rate, etc.) between components, thus constructing the "digital network" of the whole-machine simulation.

[0052] 2. Component error source identification and error quantification model construction

[0053] Based on the full three-dimensional simulation model of the engine established in step one, the simulation error sources of each component are identified and classified as follows: (1) Numerical simulation error: covering errors such as mesh resolution, data transfer at the junction of rotation and static flow, selection of turbulence model, and combustion reaction model. (2) Geometric model error: differences in the geometric model of the engine in cold and hot states, actual processing and assembly errors, deviation of adjustable guide vane root tip clearance, and working fluid physical parameters. The sources of error for different components are different and need to be analyzed separately according to the characteristics of different components. Through classification, the key errors that affect the generation of engine error are mainly: the root tip clearance of the adjustable guide vane of the fan, the actual clearance of the compressor blade tip, the chemical reaction model of the combustion chamber, and the deviation caused by the mesh coarseness.

[0054] 3. Construct a whole-machine error propagation network

[0055] Based on the error quantification models of each engine component obtained in step two, an overall engine error propagation network is constructed. Based on the characteristics of each component and its physical coupling with upstream and downstream components, a zero-dimensional engine simulation program is used to identify the sensitivity of each component's simulation errors to the engine's key performance parameters, and the sensitivity weights of each component are obtained. (i represents the component name), and considering the coupling response between upstream and downstream components, analyze the amplification / attenuation effect of upstream component errors on downstream component simulation errors, and obtain the coupling transfer coefficient of each component. (The error at the coupling interface of two components ij) is used to construct the overall machine error propagation network model, such as Figure 3 As shown.

[0056] 4. Calculate the cumulative error of the entire machine.

[0057] Based on the "weight-coupling two-factor" model obtained in step three, the sensitivity weights of each component were obtained. and the coupling transfer coefficient of each component Then, step two is used to obtain the simulation errors of each engine component, specifically the simulation error of the fan component. =+1% (outlet temperature and pressure deviation), compressor component simulation error (outlet temperature and pressure deviation) is =+1%, combustion chamber component simulation error is =+1.5% (outlet temperature and pressure deviation), turbine component simulation error is =-0.5% (outlet temperature and pressure deviation), simulation error of afterburner components is =+2.0% (outlet temperature and pressure deviation), nozzle component simulation error is =1.5% (outlet temperature and pressure deviation). Cumulative error across the entire machine. The simulation error of the whole machine can be obtained by using the calculation formula.

[0058] 5. Overall machine error accumulation control and iterative correction

[0059] Through simulation-experiment closed-loop correction, virtual monitoring points (such as fan outlet, compressor outlet, combustion chamber outlet, etc.) were set at the outlets of each component during the full 3D simulation of the entire machine. Airflow parameters (temperature, pressure, flow rate, etc.) at the component outlets were collected every 20 time steps. Simultaneously, using the test characteristic curve data of each component, the actual outlet airflow parameters of the component under the calculated operating conditions could be obtained by calculating the relative equivalent speed values. Calculations revealed that the fan calculation error... =+1% is greater than 0.5%. At this point, by increasing the fan mesh density and controlling the thickness of the first layer of the fan blade mesh to 0.001mm, the Y+ value of the first layer mesh is reduced. After correction, the deviation from the experimental value is... =+0.3%; the initial simulation error of the compressor is =+1%, at this point, by adjusting the root tip clearance of the compressor guide vanes and the actual tip clearance of the rotor blades, the corrected deviation from the test is: =+0.2%; Initial simulation error of the combustion chamber is =+1.5%, at this point, by adjusting the combustion reaction model from "Eddy Dissipation" to "Finite Rate Chemistry and Eddy Dissipation" and increasing the grid density at the diffuser location in the combustion chamber, the deviation from the experiment after correction is... =+0.4%; the initial simulation error of the turbine component is =-0.5%, which meets the requirements, so no adjustment is needed. The initial simulation error of the afterburner is... =+2.0%, at this point, the combustion reaction model was adjusted from "Eddy Dissipation" to "Finite Rate Chemistry and Eddy Dissipation", the grid density at the afterburner stabilizer location was increased, and a grid boundary layer was added. The height of the first grid layer was adjusted by 0.01 mm. After correction, the deviation from the experiment was... =-0.5%; the initial simulation error of the nozzle component is =+1.5%, at this point, the mesh density of the nozzle exit jet core region is increased, and the area sizes of nozzle A8 and A9 are corrected. The deviation from the experiment after correction is... =+0.4%.

[0060] 6. Verification of overall machine simulation accuracy

[0061] Based on the corrected simulation deviations of each component obtained in the previous step, the deviations between the simulation data and the experimental data at the outlet of each component are all ≤0.5%. This is verified by the cumulative error of the entire machine. The calculation formula can be used to obtain the deviation of the key performance parameters of the engine. Using the measured data of the engine, the corrected full three-dimensional simulation model of the whole engine was verified. The verification results show that the deviation of the engine inlet flow rate is ≤1%, the thrust deviation is ≤2.5%, and the fuel consumption rate deviation is ≤2.5%, which meets the engineering-level accuracy requirements for engine development.

[0062] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for error propagation and control in full three-dimensional performance simulation of an aero-engine, characterized in that, include: Step S1. Decompose the aero-engine into multiple core components along the airflow direction, and establish a full three-dimensional simulation model of the whole engine with aerodynamic and thermodynamic coupling between each component to determine the transmission relationship of airflow parameters between each component; Step S2. Identify the simulation error sources of each component and define the component simulation error value, wherein the component simulation error value is the relative deviation between the simulation results and experimental values ​​of the key performance parameters of the component; Step S3. Based on the full three-dimensional simulation model of the whole machine, define the sensitivity weight of each component and the coupling transmission coefficient between components to construct the whole machine error transmission network; wherein, the sensitivity weight characterizes the degree of influence of component error on the whole machine performance; the coupling transmission coefficient characterizes the amplification or attenuation effect of upstream component error on downstream component error; Step S4. Based on the component simulation error value, the sensitivity weight, and the coupling transfer coefficient, calculate the total error of the whole machine using a weighted-coupling two-factor model; Step S5. During the full 3D simulation of the whole machine, collect the simulation data of the outlet airflow parameters of each component, compare the collected simulation data with the test data of the corresponding component, and obtain the component simulation error; based on the component simulation error, iteratively correct the simulation model parameters of the component based on the whole machine error propagation network, and repeat steps S3 to S5 until the cumulative error of the whole machine meets the preset accuracy threshold.

2. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, The formula for calculating the cumulative error of the entire machine is as follows: in, Let i be the sensitivity weight. For component i, simulation error, Let j be the sensitivity weight of component j. Let j be the simulation error of component j, and n be the number of core components.

3. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, The value range of the coupling transfer coefficient is 0-0.

2.

4. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, The sensitivity weights are calculated using the controlled variable method, and the sensitivity weights of each component are within the range of 0-1.

5. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, In step S2, the simulation error sources include numerical simulation error and geometric model error; the numerical simulation error includes errors caused by at least one of the following: mesh resolution, data transfer at the static-to-dynamic interface, turbulence model selection, and combustion reaction model; the geometric model error includes errors caused by at least one of the following: engine cold and hot state geometric differences, machining and assembly errors, adjustable guide vane root tip clearance deviation, and working fluid physical property parameters.

6. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, In step S5, a virtual monitoring point is set at the component outlet, and airflow parameters are collected according to a set time step. The airflow parameters include temperature, pressure and flow rate.

7. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, Following step S5, the following is also included: Step S6: Use measured data from the engine's full-process ground test or high-altitude test to verify the corrected full three-dimensional simulation model of the engine, and confirm that the simulation error of the engine's key performance parameters is within the engineering allowable range.

8. The method for error propagation and control in full three-dimensional performance simulation of an aero-engine as described in claim 1, characterized in that, The core components include an intake section, fan, compressor, bypass duct, combustion chamber, turbine, afterburner, nozzle, and exhaust section.

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