Aero-engine working point feasible region calculation method suitable for core engine derived design

Abstract

The invention provides an aero-engine working point feasible region calculation method suitable for core engine derivation design, and relates to the technical field of aero-engines, and the method comprises the following steps: introducing turbine blade cooling air quantity demand parameters, building a derivation MTF design model, and calculating to obtain a cold air demand constraint boundary; the method comprises the steps that design parameters are modified into iteration parameters, a multi-constraint boundary calculation model is established, and a mechanical strength constraint boundary, a temperature strength constraint boundary and a compressor surge margin boundary are calculated; constructing a working point feasible region; and determining the optimal working point of the engine according to the working point feasible region. Compared with an optimization algorithm, the aero-engine working point feasible region calculation method suitable for derivation design of the core engine has the advantage that the core engine working point with the maximum thrust can be rapidly positioned, and the performance optimal working point meeting all constraint conditions can be rapidly determined in the initial stage of design.

Description

Technical Field

[0001] The invention relates to a method for calculating a feasible region of an aero-engine operating point applicable to a core engine derivative design, and belongs to the technical field of aero-engines. Background Art

[0002] In the field of aeroengines, the development of new derivative engines is a key path to continuously advancing aviation technology to meet ever-increasing aircraft performance requirements and adapt to diverse mission scenarios. The design and development approach for aeroengines derived from common core engines reuses existing, proven technologies, significantly reducing R&D costs and shortening development cycles. During the derivative design process, the selection of the core engine's operating point is a critical design parameter, directly impacting the overall performance of the derived engine.

[0003] Existing derivative design research methods, based on the Adaptive Cycle Engine (ACE) with a variable rear fan, remove the CDFS from the high-pressure shaft, retain the conventional core engine, and derive a conventional mixed-flow turbofan (MTF). The core engine's shared operating constraints that must be met during the derivative design process primarily include continuity between the compressor outlet flow rate and the high-pressure turbine inlet flow rate, and balance between the compressor power and the high-pressure turbine power. To minimize mixing losses in the mixer, the total pressures at the mixer's inner and outer ducts must be approximately equal. To meet these three constraints, the turbine inlet temperature (T4), the high-pressure turbine pressure ratio, and the fan pressure ratio are typically selected as iterative parameters. The Newton-Raphson method is then used for numerical solution. When all three constraints converge simultaneously, the current design solution is considered to meet the shared operating requirements and possess reasonable matching characteristics.

[0004] Existing derivative design research typically employs the simplified assumption of a fixed cooling air ratio across the various derivative designs. This approach has two significant drawbacks: First, as turbine inlet temperature changes, turbine blade cooling requirements vary due to material temperature resistance and cooling technology limitations. This fixed cooling air ratio design approach cannot meet the actual cooling requirements under varying operating conditions; second, under the constraint of flow continuity between the core compressor and turbine, the cooling air extraction ratio significantly affects the turbine inlet temperature of the derivative engine. This critical coupling relationship is not fully accounted for in existing approaches.

[0005] In the derivative design process of aircraft engines, the selection of the operating point has a significant impact on the performance and various constraints of the derived engine. While existing research has revealed the impact of core engine operating point changes on engine performance from the perspective of matching mechanisms, significant deficiencies remain in practical engineering applications. For the control of thrust, a key performance indicator, current methods overly rely on the designer's empirical judgment or conventional optimization algorithms. Optimization algorithms typically use a given performance target and various constraints to directly optimize the operating point. While this approach ultimately yields a feasible solution, it treats the model as a black box and ignores its internal matching principles, leading to two prominent issues: First, it is difficult to fully tap the core engine's performance potential; second, it requires significant computational resources for iterative optimization, resulting in low design efficiency and difficulty in quickly obtaining the optimal derivative solution. Summary of the Invention

[0006] The purpose of the present invention is to provide a feasible domain calculation method for the operating point of an aircraft engine suitable for the core engine derivative design, so as to solve the problems that the design method with a fixed cold air extraction ratio can neither adapt to the cooling requirements under different working conditions nor achieve optimal performance, and the traditional optimization algorithm takes a long time to solve the optimal operating point optimization iteration and has low computational efficiency.

[0007] To achieve the above-mentioned object, the present invention provides a method for calculating the feasible region of an aircraft engine operating point applicable to a core engine derivative design, which is characterized by comprising the following steps:

[0008] S1. Introducing the turbine blade cooling air volume demand parameter, establishing a derived MTF design model, and calculating the cooling air demand constraint boundary through the derived MTF design model;

[0009] S2. By modifying the design parameters into iterative parameters, a multi-constraint boundary calculation model is established based on the derived MTF design model, and the mechanical strength constraint boundary, the temperature strength constraint boundary, and the compressor surge margin boundary are calculated using the multi-constraint boundary calculation model;

[0010] S3. Constructing the operating point feasible region based on the cooling demand constraint boundary, the mechanical strength constraint boundary, the temperature strength constraint boundary, and the compressor surge margin boundary;

[0011] S4. Determine the optimal operating point of the engine based on the feasible region of the operating point.

[0012] Preferably, the cooling demand constraint boundary in S1 is solved by calculating a 7-dimensional nonlinear equation system using the Newton-Raphson method, and the specific calculation steps are as follows:

[0013] S11. Select a conversion speed line Nc, take the maximum value of the auxiliary line β, calculate and observe whether the derived MTF design model converges;

[0014] S12. If the derived MTF design model does not converge, reduce the β value, calculate the derived MTF design model, and then repeat S12. If the derived MTF design model does not converge and β reaches the minimum value, it means that the entire speed line cannot find an operating point that meets the requirements. Check the derived MTF design model and design parameters. If converged, proceed to S13.

[0015] S13, recording the current β value as the critical β value of the current speed line;

[0016] S14, confirm whether there is any speed line whose critical β is not calculated. If yes, cut the speed line and return to S11; if no, execute S15;

[0017] S15. Connect the critical β values on each speed line to obtain the cooling demand constraint boundary.

[0018] Preferably, the mechanical strength constraint boundary in S2 converts the design parameter β into an iterative parameter, and the calculation steps of the mechanical strength constraint boundary are as follows:

[0019] S211, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the physical speed 100% as the critical β;

[0020] S212: Check whether there are any conversion speed lines that have not calculated the critical β. If yes, cut the speed line and return to S211; if not, execute S213;

[0021] S213 , connecting the critical β on each speed line to obtain the mechanical strength constraint boundary.

[0022] Preferably, the temperature-strength constraint boundary in S2 uses the design parameter β as an iteration parameter, and the calculation steps of the temperature-strength constraint boundary are as follows:

[0023] S221, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the turbine front temperature T4 = 2200K as the critical β;

[0024] S222: Is there any converted speed line without calculating the critical β? If yes, cut the speed line and return to S221; if no, execute S223;

[0025] S223 , connecting the critical β on each speed line to obtain the temperature intensity constraint boundary.

[0026] Preferably, the compressor surge margin constraint boundary in S2 is used as a newly added constraint condition to achieve a compressor surge margin SM of no more than 10%, and is calculated by surge margin interpolation on the characteristic diagram.

[0027] Preferably, in said S3, the cooling demand constraint boundary, the mechanical strength constraint boundary, the temperature strength constraint boundary and the compressor surge margin boundary are plotted on the component characteristic diagram, and the operating point feasible region is obtained by the constraint boundary superposition algorithm.

[0028] Preferably, the optimal working point Fmax in S4 is obtained based on feasible domain analysis, and the maximum thrust point Fopt obtained by optimization coincides with Fmax for verification.

[0029] Therefore, the present invention adopts the above-mentioned method for calculating the feasible region of the operating point of an aero-engine applicable to the core engine derivative design, which has the following beneficial effects:

[0030] (1) The derivative design method considering cooling air demand innovatively introduces the turbine blade cooling air volume demand parameter into the derivative design model. By establishing a correlation model between cooling air volume and turbine inlet temperature, the scheme can adapt to the cooling demand of derivative engines with different turbine inlet temperatures.

[0031] (2) Perform multi-constraint boundary analytical calculations. Based on the engine operating principle, establish boundary calculation models for key constraints such as mechanical strength, thermal strength, and compressor surge margin, and use analytical methods (rather than black box optimization) to accurately solve each constraint boundary.

[0032] (3) Construct the feasible domain of the working point. Through the constraint boundary superposition algorithm, a multi-dimensional feasible domain is constructed to quickly determine the optimal performance working point that meets all constraints and improve design efficiency.

[0033] Therefore, the present invention utilizes the aforementioned method for calculating the feasible region of an aircraft engine's operating point, suitable for core engine derivative design. By establishing an analytical calculation method for the feasible region of the operating point, this method can rapidly determine the optimal operating point that satisfies all constraints during the initial design phase. This provides a scientific basis for selecting derivative engine solutions, significantly shortening the design cycle and reducing R&D costs. Compared to traditional black-box optimization methods, this method significantly improves computational efficiency and ensures the optimality and feasibility of the design solution.

[0034] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the ACE configuration of an adaptive cycle engine with a rear variable fan in an embodiment of the present invention;

[0036] Figure 2 Schematic diagram of the MTF configuration of a mixed turbofan engine in an embodiment of the present invention;

[0037] Figure 3The following is a diagram showing the change of the matching point considering the cooling demand of the present invention: (a) shows the change of the temperature before the turbine of the derived engine with the cooling air extraction amount, and (b) shows the change of the cooling demand with the cooling air extraction amount;

[0038] Figure 4 Component characteristic diagram of an embodiment of the present invention;

[0039] Figure 5 is the feasible region of the operating point for deriving the MTF when the bypass ratio is 0.3 in the embodiment of the present invention;

[0040] Figure 6 is the feasible region of the operating point for deriving the MTF when the bypass ratio is 0.6 in the embodiment of the present invention;

[0041] Figure 7 is the feasible region of the operating point for deriving the MTF when the bypass ratio is 0.9 in the embodiment of the present invention;

[0042] Figure markings: 1-front fan; 2-rear fan; 3-mode selection valve; 4-core drive fan stage; 5-compressor; 6-front duct ejector; 7-external nozzle; 8-combustion chamber; 9-high pressure turbine; 10-low pressure turbine; 11-rear duct ejector; 12-afterburner; 13-main nozzle; 14-fan; 15-mixer; 16-nozzle. Implementation Method

[0043] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0044] The present invention provides a method for calculating the feasible region of the operating point of an aero-engine suitable for the derivative design of a core engine. To illustrate the calculation method proposed by the present invention, this embodiment uses an adaptive cycle engine with a rear variable fan (ACE) as the basic engine. The schematic diagram of the ACE configuration is shown in FIG. Figure 1 As shown in FIG, it includes: a front fan 1, a rear fan 2, a mode selection valve 3, a core drive fan stage 4, a compressor 5, a front duct ejector 6, an outer duct nozzle 7, a combustion chamber 8, a high-pressure turbine 9, a low-pressure turbine 10, a rear duct ejector 11 and an afterburner 12. Based on its core engine, a conventional mixed-flow turbofan engine (Mixed-flow Turbofan, hereinafter referred to as MTF) is derived and developed. The schematic diagram of the MTF configuration is shown in FIG. Figure 2As shown, the core components of this configuration include: a fan 14 , a compressor 5 , a high-pressure turbine 9 , a low-pressure turbine 10 , a mixer 15 and a nozzle 16 .

[0045] It should be noted that this method is a general calculation method, including but not limited to the derived design from ACE to MTF.

[0046] The present invention provides a method for calculating the feasible region of an aircraft engine operating point applicable to a core engine derivative design, comprising the following steps:

[0047] S1. Based on the original derived design model, the turbine blade cooling air volume requirement parameter is introduced to establish a derived MTF design model, such as Figure 3 As shown, Figure 3 (a) shows the variation of the derived engine turbine inlet temperature with the amount of cold air extracted. Figure 3 (b) shows the change of cooling demand (CAreq) with cooling extraction. Lines of different colors represent operating points at different locations. The larger β is, the closer the operating point is to the surge boundary. Figure 3 The two matching points in the figure indicate that there are two different cooling air quantities, both of which can meet multiple derived engine constraints including cooling air requirements. Among them, the turbine inlet temperature of matching point 1 is low and the performance is poor, so matching point 2 is selected as the design solution; Figure 3 It can also be seen that when the operating point is close to the surge boundary, no matter how the cooling air volume is adjusted, the cooling demand cannot be met. The present invention considers the derived design method of cooling air demand and innovatively introduces the turbine blade cooling air volume demand parameter into the derived design model. By establishing an MTF design model, the cooling air volume is associated with the turbine inlet temperature, ensuring that the cooling demand of derived engines with different turbine inlet temperatures is accurately matched. This makes the solution adaptable to the cooling demand of derived engines with different turbine inlet temperatures, and calculates the cooling air demand constraint boundary through the derived MTF design model.

[0048] S2. By modifying the design parameters to iterative parameters, a multi-constraint boundary calculation model is established based on the derived MTF design model. During the derived design process, in addition to adding the cooling air volume requirement constraint to meet the cooling requirements, the derived MTF must also meet certain mechanical strength, temperature strength, and aerodynamic constraints. Specifically, the physical speed (NH) must not exceed 100%, the turbine inlet temperature (T4) must not exceed 2200K, and the compressor surge margin (SM) must not exceed 10%. The mechanical strength constraint boundary, temperature strength constraint boundary, and compressor surge margin constraint boundary are calculated using the multi-constraint boundary calculation model, and the thrust of the MTF is expected to be maximized while satisfying all the constraints.

[0049] S3. Construct the operating point feasible region based on the cooling demand constraint boundary, mechanical strength constraint boundary, temperature strength constraint boundary, and compressor surge margin boundary. By constructing the multidimensional feasible region of the operating point, it is possible to quickly determine the optimal performance operating point that meets all constraints, significantly improving design efficiency.

[0050] S4. Based on the feasible domain of the operating point, the optimal operating point of the engine is determined, so that the compressor operating point position with the maximum derived MTF thrust is located. Compared with the optimization algorithm, it has the advantage of quickly locating the core engine operating point with the maximum thrust.

[0051] The cooling air demand constraint boundary adopts a derived mixed-displacement turbofan engine MTF design model that takes into account the cooling air volume demand. An appropriate amount of cooling air extraction can be used to cool the turbine blades to prevent blade ablation, while excessive cooling air extraction will cause performance loss. In order to achieve the cooling air extraction amount that just meets the cooling demand, the present invention adds additional constraints that meet the cooling demand on the basis of the conventional derivative process, and uses the cooling air extraction amount as the iterative variable. Since the guide vanes and moving blades of the high- and low-pressure turbines need to be cooled independently, the introduction of this constraint expands the dimension of the nonlinear equation group from the original 3 dimensions to 7 dimensions. The Newton-Raphson method is used to calculate and solve the 7-dimensional nonlinear equation group. The iterative variables and constraints are shown in the following table:

[0052] Serial number Iteration variables Constraints 1 Fan pressure ratio Mixer internal and external pressure balance 2 Turbine inlet temperature Core machine traffic balance 3 High-pressure turbine pressure ratio Core engine power balance 4 High-pressure turbine guide vane cooling air Meeting the cooling requirements of high-pressure turbine guide vanes 5 High-pressure turbine blade cooling air Meeting the cooling needs of high-pressure turbine blades 6 Low-pressure turbine guide vane cooling air Meeting the cooling requirements of low-pressure turbine guide vanes 7 Low-pressure turbine blade cooling Meeting the cooling requirements of low-pressure turbine guide vanes

[0053] The specific calculation steps are as follows:

[0054] S11. Select a conversion speed line Nc, take the maximum value of the auxiliary line β, calculate and observe whether the derived MTF design model converges;

[0055] S12. If the derived MTF design model does not converge, reduce the β value, calculate the derived MTF design model, and then repeat S12. If the derived MTF design model does not converge and β reaches the minimum value, it means that the entire speed line cannot find an operating point that meets the requirements. Check the derived MTF design model and design parameters. If converged, proceed to S13.

[0056] S13, recording the current β value as the critical β value of the current speed line;

[0057] S14, confirm whether there is any speed line whose critical β is not calculated. If yes, cut the speed line and return to S11; if no, execute S15;

[0058] S15. Connect the critical β values on each speed line to obtain the cooling demand constraint boundary.

[0059] The mechanical strength constraint boundary transforms the design parameter β into an iterative parameter and uses the physical speed NH = 100% as a new constraint. The iterative variables and constraints of the modified multi-constraint boundary calculation model are:

[0060] Serial number Iteration variables Constraints 1 Fan pressure ratio Mixer internal and external pressure balance 2 Turbine inlet temperature Core machine traffic balance 3 High-pressure turbine pressure ratio Core engine power balance 4 High-pressure turbine guide vane cooling air Meeting the cooling requirements of high-pressure turbine guide vanes 5 High-pressure turbine blade cooling air Meeting the cooling needs of high-pressure turbine blades 6 Low-pressure turbine guide vane cooling air Meeting the cooling requirements of low-pressure turbine guide vanes 7 Low-pressure turbine blade cooling Meeting the cooling requirements of low-pressure turbine guide vanes 8 Compressor auxiliary line β The core machine's physical speed is 100%

[0061] The specific calculation steps are as follows:

[0062] S211, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the physical speed 100% as the critical β;

[0063] S212: Check whether there are any conversion speed lines that have not calculated the critical β. If yes, cut the speed line and return to S211; if not, execute S213;

[0064] S213 , connecting the critical β on each speed line to obtain the mechanical strength constraint boundary.

[0065] The temperature intensity constraint boundary uses the design parameter β as an iteration parameter to achieve the turbine front temperature T4 = 2200K as a new constraint condition. The iterative variables and constraints of the modified multi-constraint boundary calculation model are:

[0066] Serial number Iteration variables Constraints 1 Fan pressure ratio Mixer internal and external pressure balance 2 Turbine inlet temperature Core machine traffic balance 3 High-pressure turbine pressure ratio Core engine power balance 4 High-pressure turbine guide vane cooling air Meeting the cooling requirements of high-pressure turbine guide vanes 5 High-pressure turbine blade cooling air Meeting the cooling needs of high-pressure turbine blades 6 Low-pressure turbine guide vane cooling air Meeting the cooling requirements of low-pressure turbine guide vanes 7 Low-pressure turbine blade cooling Meeting the cooling requirements of low-pressure turbine guide vanes 8 Compressor auxiliary line β Turbine front temperature T4 = 2200K

[0067] The specific calculation steps are as follows:

[0068] S221, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the turbine front temperature T4 = 2200K as the critical β;

[0069] S222: Is there any converted speed line without calculating the critical β? If yes, cut the speed line and return to S221; if no, execute S223;

[0070] S223 , connecting the critical β on each speed line to obtain the temperature intensity constraint boundary.

[0071] The compressor surge margin constraint boundary is a new constraint condition to achieve a compressor surge margin (SM) of no more than 10%. It is calculated by surge margin interpolation on the characteristic diagram. The analytical calculation of multiple constraint boundaries accurately solves each constraint boundary, rather than black box optimization.

[0072] Draw the cooling demand constraint boundary, mechanical strength constraint boundary, temperature strength constraint boundary and compressor surge margin boundary on the component characteristic diagram, such as Figure 4As shown in Figure 1, the derived MTF component characteristic diagram has three design parameters, including the bypass ratio, the converted speed Nc of the compressor operating point, and the auxiliary line β. The feasible region of the operating point is obtained through the constraint boundary superposition algorithm. This algorithm can comprehensively consider various constraints, accurately superimpose all relevant boundary information, accurately determine the feasible region of the operating point, and present it in a graphical form, as shown in Figure 1. Figure 5 As shown in Figure 2, the common area enclosed on the component characteristic diagram is the feasible region of the operating point of the derived MTF when the bypass ratio is 0.3.

[0073] The optimal working point Fmax is obtained based on the feasible region analysis, and the maximum thrust point Fopt obtained through optimization coincides with Fmax, which verifies that this research method has high stability and reliability. Figure 5 As shown in Figure 2, when the temperature before the turbine is the largest, the engine unit thrust is the largest. At this time, a larger converted speed is selected and the engine thrust is the largest. Therefore, the maximum thrust point should be located at the intersection of T4 and the NH constraint boundary, that is, the Fmax point.

[0074] When optimizing for maximum thrust, the optimization variables are the converted speed Nc and the auxiliary line β, the optimization objective is maximum MTF thrust, and the constraints are the aforementioned mechanical strength, thermal strength, and compressor surge margin requirements. The optimization results validate the accuracy of the previous analysis and also demonstrate that the operating point feasible region calculation method proposed in this patent has the advantage of rapidly locating the core engine operating point that maximizes thrust, compared to the optimization algorithm.

[0075] Afterwards, the feasible regions of the operating points under the bypass ratio of 0.6 and 0.9 were calculated respectively, and the maximum thrust point under the bypass ratio of 0.6 was verified again using the optimization algorithm. Figure 6 As shown in Figure 2, when the bypass ratio is 0.6, the maximum thrust point obtained by the optimization algorithm is consistent with the maximum thrust point obtained by the method proposed in this patent; in addition, as the bypass ratio increases, the feasible region of the operating point gradually decreases, as shown in Figure 2. Figure 7 As shown, a feasible region cannot be found for an operating point satisfying all constraints at a bypass ratio of 0.9, indicating that no derivative design solution exists that satisfies all constraints at the current bypass ratio. Therefore, in addition to being computationally more efficient than the optimization algorithm, feasible region analysis can also quickly analyze the feasible range of bypass ratios, providing guidance for the design of derivative engine solutions.

[0076] Therefore, the present invention adopts the above-mentioned method for calculating the feasible domain of the operating point of an aircraft engine suitable for the core engine derivative design. Through the calculation method of the feasible domain of the operating point, the optimal performance operating point that meets all constraints can be quickly determined in the early stage of design, providing a scientific basis for the selection of derivative engine schemes, greatly shortening the design cycle, and reducing R&D costs; compared with traditional black-box optimization methods, the calculation efficiency is significantly improved, and the optimality and feasibility of the design scheme can be guaranteed.

[0077] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating the feasible region of an aircraft engine operating point suitable for core engine derivative design, characterized by: The following steps are involved: S1. Introducing the turbine blade cooling air volume demand parameter, establishing a derived MTF design model, and calculating the cooling air demand constraint boundary through the derived MTF design model; S2. By modifying the design parameters into iterative parameters, a multi-constraint boundary calculation model is established based on the derived MTF design model, and the mechanical strength constraint boundary, the temperature strength constraint boundary, and the compressor surge margin boundary are calculated using the multi-constraint boundary calculation model; S3. Constructing the operating point feasible region based on the cooling demand constraint boundary, the mechanical strength constraint boundary, the temperature strength constraint boundary, and the compressor surge margin boundary; S4. Determine the optimal operating point of the engine based on the feasible region of the operating point.

2. The method for calculating the feasible region of an aircraft engine operating point suitable for a core engine derivative design according to claim 1 is characterized in that: The cooling demand constraint boundary in S1 is solved by calculating the 7-dimensional nonlinear equation system using the Newton-Raphson method. The specific calculation steps are as follows: S11. Select a conversion speed line Nc, take the maximum value of the auxiliary line β, calculate and observe whether the derived MTF design model converges; S12. If the derived MTF design model does not converge, reduce the β value, calculate the derived MTF design model, and then repeat S12. If the derived MTF design model does not converge and β reaches the minimum value, it means that the entire speed line cannot find an operating point that meets the requirements. Check the derived MTF design model and design parameters. If converged, proceed to S13. S13, recording the current β value as the critical β value of the current speed line; S14, confirm whether there is any speed line whose critical β is not calculated. If yes, cut the speed line and return to S11; if no, execute S15; S15. Connect the critical β values on each speed line to obtain the cooling demand constraint boundary.

3. The method for calculating the feasible region of an aircraft engine operating point suitable for a core engine derivative design according to claim 1 is characterized in that: The mechanical strength constraint boundary in S2 transforms the design parameter β into an iterative parameter. The calculation steps of the mechanical strength constraint boundary are as follows: S211, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the physical speed 100% as the critical β; S212: Check whether there are any conversion speed lines that have not calculated the critical β. If yes, cut the speed line and return to S211; if not, execute S213; S213 , connecting the critical β on each speed line to obtain the mechanical strength constraint boundary.

4. The method for calculating the feasible region of an aircraft engine operating point suitable for a core engine derivative design according to claim 1, characterized in that: The temperature-strength constraint boundary in S2 uses the design parameter β as an iteration parameter. The calculation steps of the temperature-strength constraint boundary are as follows: S221, select a conversion speed line Nc, run the multi-constraint boundary calculation model, and record the auxiliary line β that currently makes the turbine front temperature T4 = 2200K as the critical β; S222: Is there any converted speed line without calculating the critical β? If yes, cut the speed line and return to S221; if no, execute S223; S223 , connecting the critical β on each speed line to obtain the temperature intensity constraint boundary.

5. The method for calculating the feasible region of an aircraft engine operating point suitable for core engine derivative design according to claim 1 is characterized in that: The compressor surge margin constraint boundary in S2 is used as a newly added constraint condition to achieve a compressor surge margin SM of no more than 10%, and is calculated by surge margin interpolation on the characteristic diagram.

6. The method for calculating the feasible region of an aircraft engine operating point suitable for core engine derivative design according to claim 1, characterized in that: In S3, the cooling demand constraint boundary, the mechanical strength constraint boundary, the temperature strength constraint boundary, and the compressor surge margin boundary are plotted on the component characteristic diagram, and the operating point feasible region is obtained through the constraint boundary superposition algorithm.

7. The method for calculating the feasible region of an aircraft engine operating point suitable for core engine derivative design according to claim 1 is characterized in that: The optimal working point Fmax in S4 is obtained based on the feasible region analysis, and is verified by coinciding the maximum thrust point Fopt obtained by optimization with Fmax.

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