Control Parameter Optimization Method for the Acceleration Process of a Variable Cycle Aeroengine

Abstract

The present invention relates to a method for optimizing control parameters during the acceleration process of a variable cycle aeroengine, which comprises the following steps. Step 1: Establish a non-linear model of the aeroengine acceleration process. Step 2: Determine the optimization model and constraint function of the acceleration process according to the aeroengine acceleration process. Step 3: Calculate the optimal control points of the aeroengine according to the intelligent optimization algorithm to achieve the optimal control of the acceleration process. Based on the non-linear model of the aeroengine acceleration process, the present invention establishes an optimization model of the aeroengine acceleration control process by establishing an optimization model and a constraint function for the engine acceleration process, and further realizes the optimal control of the engine acceleration process through an intelligent algorithm. On the premise of ensuring the safe operation of the engine, the engine acceleration time is shortened, the engine acceleration performance is effectively improved, and the maneuverability and flexibility of the aircraft are enhanced.

Description

Technical Field

[0001] This application relates to the field of aeroengine control technology, and particularly relates to a method for optimizing control parameters during the acceleration process of a variable cycle aeroengine. Background Art

[0002] Advanced aeroengines usually need to have the ability of long-range subsonic cruise and fast response ability at the same time. In the future, variable cycle aeroengines will continue to develop in three directions: long cruise range, high thrust-to-weight ratio, and wide operating range.

[0003] By studying the speed characteristics of conventional engines, researchers found that under supersonic conditions, turbojet engines have higher specific thrust and lower specific fuel consumption rate, while under subsonic conditions, high-bypass ratio turbofan engines have lower specific fuel consumption rate. Considering the performance requirements of the propulsion system, turbofan engines are more suitable for subsonic flight, while turbojet engines are more suitable for supersonic flight. Therefore, there is a variable cycle aeroengine with better performance. Under different operating conditions of the engine, by adopting different technical means such as adjusting the geometric shape, physical position or size of the characteristic components, the performance advantages of the two different variable cycle aeroengines, namely turbofan and turbojet, are concentrated together, so as to ensure that the variable cycle aeroengine works in a configuration similar to that of a turbofan engine under subsonic cruise conditions, thereby obtaining higher economy, and works in a configuration similar to that of a turbojet engine under supersonic conditions, thereby obtaining continuous and reliable high specific thrust, achieving the purpose of integrating the performance advantages of turbofan and turbojet engines, and enabling the variable cycle aeroengine to have excellent performance throughout the entire engine operation process.

[0004] Due to the situation where the maneuverability requirements for aircraft are very high, good maneuverability requires the engine to have good acceleration performance. Acceleration process control is a type of transient control of aeroengines. Compared with engine starting, engaging / disengaging afterburner, and deceleration control, acceleration process control has a more obvious impact on the performance of the engine and the aircraft. The acceleration process of the engine directly affects important flight indicators, such as acceleration, climb, and emergency landing and go-around, etc. Therefore, it is of great significance to study the optimal control model of the engine acceleration process and improve the engine acceleration performance. Although certain achievements have been made in the research on the optimal control of the engine acceleration process at home and abroad, there are still many technical problems that have not been solved or areas for improvement. Summary of the Invention

[0005] To overcome the deficiencies of the prior art, based on the non-linear model of the aero-engine acceleration process, the present invention establishes an optimization model and a constraint function for the engine acceleration process, constructs an optimization model for the aero-engine acceleration control process, and further realizes the optimal control of the engine acceleration process through intelligent algorithms. On the premise of ensuring the safe operation of the engine, the engine acceleration time is shortened, the engine acceleration performance is effectively improved, and the maneuverability and flexibility of the aircraft are enhanced.

[0006] To achieve the above object, the solution adopted by the present invention is as follows:

[0007] A method for optimizing control parameters for the acceleration process of a variable cycle aero-engine, comprising the following steps:

[0008] Step 1: Establish a non-linear model for the aero-engine acceleration process;

[0009] The non-linear model for the acceleration process of the variable cycle aero-engine is:

[0010] [sfc F] T = f(x) = f[W f A9 dvgl dvgh] T ;

[0011] Where: sfc represents the specific fuel consumption rate; F represents the engine thrust; f represents the non-linear vector function generating the system output; x represents the control parameter variable; W f represents the adjustment of the main fuel flow; A9 represents the area of the tail nozzle; dvgl represents the fan guide vane angle; dvgh represents the compressor guide vane angle;

[0012] Step 2: Determine the optimization model and constraint function for the acceleration process according to the aero-engine acceleration process;

[0013] Step 21: Establish a multi-objective optimization function for the engine acceleration process according to the constraint conditions of the acceleration process;

[0014] The multi-objective optimization function for the aero-engine acceleration process is:

[0015]

[0016] Where: J1 represents the first objective function for the aero-engine acceleration process; J2 represents the second objective function for the aero-engine acceleration process; n Hd represents the desired speed of the high-pressure rotor of the aero-engine; n H represents the actual speed of the high-pressure rotor of the aero-engine; T t4d represents the desired temperature of the high-pressure turbine of the aero-engine in the previous stage; T t4It represents the actual temperature in front of the high-pressure turbine of an aeroengine; t represents the engine startup time; min represents taking the minimum value of this variable;

[0017] Step 22: Use the linear weighted method to transform the multi-objective optimization function into a single-objective optimization function to determine the optimization objective function, which is:

[0018]

[0019] In the formula: J represents the single-objective function during the acceleration process of the aeroengine; ω a represents the weight coefficient of the first objective function during the acceleration process of the aeroengine; ω b represents the weight coefficient of the second objective function during the acceleration process of the aeroengine;

[0020] Step 23: Discretize and normalize the single-objective function during the acceleration process of the aeroengine; finally, determine the single-objective function during the acceleration process of the aeroengine as:

[0021]

[0022] In the formula: n H (k) represents the rotational speed of the aeroengine at the k-th iteration; T t4 (k) represents the temperature of the aeroengine at the k-th iteration; k represents the number of iterations in the optimization process;

[0023] Step 24: Referring to the form of the objective function, discretize and normalize the constraint conditions of the aeroengine, construct the constraint conditions for satisfying the aeroengine, and establish the optimization model for the acceleration control process of the aeroengine, which is:

[0024]

[0025] In the formula: R 4 represents a four-dimensional real vector; ω represents the weight adjustment coefficient matrix of the constraint function; g(x) represents the constraint function matrix;

[0026] Step 3: Calculate the optimal control points of the aeroengine according to the intelligent optimization algorithm to achieve the optimal control during the acceleration process;

[0027] Determine the initial values of the control variables of the aeroengine optimization model; construct the iterative relationship between the control variable speed and the control variable position, obtain the optimal control points of the aeroengine optimization model, and achieve the optimal control during the acceleration process of the variable cycle aeroengine.

[0028] Preferably, in the step 21, the constraint conditions for the acceleration process include: the temperature before the turbine does not exceed the limit, the high-pressure compressor does not surge, the fan does not surge, the high-pressure rotor does not over-speed, the fan does not over-speed, the combustion chamber does not flame out due to rich fuel, and the fuel supply of the main combustion chamber does not exceed its maximum fuel supply. Specifically:

[0029] The constraint condition for the temperature before the turbine not exceeding the limit is T t4 <T t4max ; The constraint condition for the high-pressure compressor not surging is SMC≥SMC min ; The constraint condition for the fan not surging is SMF≥SMF min ; The constraint condition for the high-pressure rotor not over-speeding is n H ≤n Hmax ; The constraint condition for the fan not over-speeding is n F ≤n Fmax ; The constraint condition for the combustion chamber not flaming out due to rich fuel is R OG ≤R OGmax ; The constraint condition for the fuel supply of the main combustion chamber not exceeding its maximum fuel supply is W f ≤W fmax ; where SMC represents the surge margin of the high-pressure compressor, SMF represents the surge margin of the fan, R OG represents the fuel-air ratio, W f represents the fuel supply of the main combustion chamber, and the subscripts min and max represent the minimum and maximum values of this quantity respectively.

[0030] Preferably, in the step 24, the constraint conditions of the aero-engine are also discretized and normalized. Specifically:

[0031] The process of discretizing and normalizing the constraint conditions of the aero-engine is as follows:

[0032]

[0033] In the formula: g1 represents the constraint function for the temperature before the turbine not exceeding the limit; T t4max represents the upper limit of the engine temperature; max represents taking the maximum value of this variable;

[0034]

[0035] In the formula: g2 represents the constraint function for the fan speed not over-speeding; n F represents the fan speed; n Fmax represents the maximum allowable speed of the fan;

[0036]

[0037] In the formula: g3 represents the constraint function for the high-pressure compressor not over-speeding; n HmaxRepresents the maximum allowable speed of the high-pressure compressor;

[0038]

[0039] Where: g4 represents the constraint function for the fan not to surge; SMF min Represents the minimum allowable value of the fan surge margin; SMF represents the fan surge margin; min represents taking the minimum value of this variable;

[0040]

[0041] Where: g5 represents the constraint function for the high-pressure compressor not to surge; SMC min Represents the minimum allowable value of the high-pressure compressor surge margin; SMC represents the high-pressure compressor surge margin;

[0042]

[0043] Where: g6 represents the constraint function for the combustor not to flame out due to excessive fuel; R OG Represents the fuel-air ratio in the combustor; R OGmax Represents the maximum allowable value of the fuel-air ratio in the combustor;

[0044]

[0045] Where: g7 represents the constraint function for the main fuel flow; W f Represents the main fuel flow; W fmax Represents the maximum allowable value of the main fuel flow.

[0046] Preferably, the structure in step 24 for satisfying the constraint conditions of the aeroengine is specifically:

[0047] The constraint condition of the aeroengine is ω·g(x), and the constraint function matrix formed is g n (x) (n = 1, 2,..., 7); After considering the constraint conditions, the weight adjustment coefficient matrix of the constraint function is:

[0048] ω = [ω1, ω2, ω3, ω4, ω5, ω6, ω7];

[0049] Where: ω1, ω2, ω3, ω4, ω5, ω6, ω7 respectively represent the adjustment weight coefficients for the turbine inlet temperature not exceeding the limit, the fan speed not exceeding the limit, the high-pressure compressor speed not exceeding the limit, the fan not surging, the high-pressure compressor not surging, the combustor not flaming out due to excessive fuel, and the main fuel flow.

[0050] Preferably, the determination of the initial values of the control variables of the aeroengine optimization model in step 3 is specifically:

[0051] Set the initial iteration value \(t = 0\), the storage pool dimension \(G\), the change domain \(B\), the number of changes \(C\), the judgment of its own position \(E\), and find \(S\) \(D\)-dimensional control variables \(X_1, X_2, \cdots, X\) through chaotic initialization s , \(i = 1, 2, \cdots, S\), randomly generate \(S\) initial velocities \(V_1, V_2, \cdots, V\) s , \(i = 1, 2, \cdots, S\), to form the initial control variables.

[0052] Preferably, the iterative relationship for constructing the control variable velocity and the control variable position in step 3 is as follows:

[0053] The iterative relationship of the control variable velocity is:

[0054] V i (t + 1)=\(\omega V\) i (t)+c[p g (t)-X i (t)];

[0055] In the formula: \(c\) represents the learning factor; \(V\) i (t + 1) and \(V\) i (t) represent the velocities of the next iteration and the current control point respectively; \(p\) g (t) represents the optimal position searched by the current entire control point group so far; \(X\) i (t) represents the value of the \(i\)-th control variable of the current entire control variable group; \(t\) represents the iteration update times;

[0056] The iterative relationship of the control variable position is:

[0057]

[0058] In the formula: \(X\) i (t + 1) represents the updated control variable position; \(X\) best represents the optimal control variable of the aero-engine; \(m\) represents a random number between \([0.45, 0.95]\); \(\beta\) represents a random number within \([-1, 1]\); \(X\) r1 (t) represents the position of the randomly selected \(r1\)-th control variable; if represents the condition is met; rand represents a random number between \([0, 1]\); else represents the situation where the judgment condition is not met.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] (1) Based on the non-linear model of the aero-engine acceleration process, the present invention establishes the optimization model and constraint function of the engine acceleration process, establishes the optimization model of the aero-engine acceleration control process, and further realizes the optimal control of the engine acceleration process through intelligent algorithms;

[0061] (2) On the premise of ensuring the safe operation of the engine, the present invention shortens the engine acceleration time, effectively improves the engine acceleration performance, and enhances the maneuverability and flexibility of the aircraft. Description of the Drawings

[0062] Figure 1 It is a flow chart of the control parameter optimization method for the acceleration process of a variable cycle aeroengine according to an embodiment of the present invention;

[0063] Figure 2 It is a schematic structural diagram of a variable cycle aeroengine according to an embodiment of the present invention. Detailed Embodiments

[0064] Hereinafter, the embodiments of the present invention will be described with reference to the drawings.

[0065] An embodiment of the present invention takes a certain type of aero turbofan variable cycle aeroengine as the research object. Based on the non-linear model of the engine acceleration process, by establishing the optimization model and constraint function of the engine acceleration process, an optimization model of the aeroengine acceleration control process is established, and further, the optimization of the control parameters is realized through an intelligent algorithm to achieve the optimal control of the aeroengine acceleration process; on the premise of ensuring the safe operation of the aeroengine, the engine acceleration time is shortened. Through the embodiment, it is further proved that the method effectively improves the engine acceleration performance, enhances the maneuverability and flexibility of the aircraft, and can meet the actual application requirements. As Figure 1 shown is a flow chart of the control parameter optimization method for the acceleration process of a variable cycle aeroengine according to an embodiment of the present invention.

[0066] An embodiment of the present invention provides a control parameter optimization method for the acceleration process of a variable cycle aeroengine. To prove the applicability of the present invention, it is applied to an example, which specifically includes the following steps:

[0067] S1: Establish a non-linear model of the aeroengine acceleration process;

[0068] The non-linear model of the variable cycle aeroengine acceleration process is:

[0069] [sfc F] T = f(x) = f[W f A9 dvgl dvgh] T ;

[0070] In the formula: sfc represents the specific fuel consumption rate; F represents the engine thrust; f represents the non-linear vector function generating the system output; x represents the control parameter variable; W f represents adjusting the main fuel flow; A9 represents the area of the tail nozzle; dvgl represents the fan guide vane angle; dvgh represents the compressor guide vane angle;

[0071] Compared with ordinary dual-axis turbofan variable cycle aeroengines, the variable cycle aeroengine in the embodiment of the present invention has more adjustable components. The variable cycle aeroengine with a CDFS component mainly has 8 adjustable components, specifically as Figure 2 FIG. 2 shows a schematic structural diagram of the variable cycle aeroengine according to the embodiment of the present invention. In the figure, 1 represents the secondary bypass duct, 2 represents the primary bypass duct, 3 represents the total bypass duct, 4 represents the tail-end duct, 5 represents the inlet duct, 6 represents the fan, 7 represents the CDFS, 8 represents the high-pressure compressor, 9 represents the high-pressure turbine, 10 represents the low-pressure turbine, 11 represents the mixing chamber, 12 represents the afterburner, and 13 represents the tail nozzle.

[0072] S2: Determine the optimization model and constraint function of the acceleration process according to the acceleration process of the aeroengine;

[0073] S21: Establish a multi-objective optimization function for the engine acceleration process according to the constraint conditions of the acceleration process;

[0074] The constraint conditions of the acceleration process include: the turbine inlet temperature does not exceed the limit, the high-pressure compressor does not surge, the fan does not surge, the high-pressure rotor does not overspeed, the fan does not overspeed, the combustion chamber does not flame out due to rich fuel, and the fuel supply of the main combustion chamber does not exceed its maximum fuel supply; the constraint condition for the turbine inlet temperature not to exceed the limit is T t4 <T t4max ; the constraint condition for the high-pressure compressor not to surge is SMC≥SMC min ; the constraint condition for the fan not to surge is SMF≥SMF min ; the constraint condition for the high-pressure rotor not to overspeed is n H ≤n Hmax ; the constraint condition for the fan not to overspeed is n F ≤n Fmax ; the constraint condition for the combustion chamber not to flame out due to rich fuel is R OG ≤R OGmax ; the constraint condition for the fuel supply of the main combustion chamber not to exceed its maximum fuel supply is W f ≤W fmax ; where SMC represents the surge margin of the high-pressure compressor, SMF represents the surge margin of the fan, R OG represents the fuel-air ratio, W f represents the fuel supply of the main combustion chamber, and the subscripts min and max respectively represent the minimum and maximum values of this quantity.

[0075] The multi-objective optimization function of the aeroengine acceleration process is:

[0076]

[0077] In the formula: J1 represents the first objective function of the aeroengine acceleration process; J2 represents the second objective function of the aeroengine acceleration process; n HdRepresents the desired speed of the high-pressure rotor of an aeroengine; n H Represents the actual speed of the high-pressure rotor of an aeroengine; T t4d Represents the desired temperature before the high-pressure turbine of an aeroengine; T t4 Represents the actual temperature before the high-pressure turbine of an aeroengine; t represents the engine startup time; min represents taking the minimum value of this variable;

[0078] S22: Use the linear weighted method to transform the multi-objective optimization function into a single-objective optimization function to determine the optimization objective function, which is:

[0079]

[0080] In the formula: J represents the single-objective function of the aeroengine acceleration process; ω a Represents the weight coefficient of the first objective function in the aeroengine acceleration process; ω b Represents the weight coefficient of the second objective function in the aeroengine acceleration process;

[0081] S23: Discretize and normalize the single-objective function of the aeroengine acceleration process; finally determine the single-objective function of the aeroengine acceleration process as:

[0082]

[0083] In the formula: n H (k) represents the aeroengine speed at the k-th iteration; T t4 (k) represents the aeroengine temperature at the k-th iteration; k represents the iteration number of the optimization process;

[0084] S24: Referring to the form of the objective function, discretize and normalize the aeroengine constraint conditions. The process of discretizing and normalizing the aeroengine constraint conditions is as follows:

[0085]

[0086] In the formula: g1 represents the constraint function that the temperature before the turbine does not exceed the limit; T t4max Represents the upper limit of the engine temperature; max represents taking the maximum value of this variable;

[0087]

[0088] In the formula: g2 represents the constraint function that the fan speed does not exceed the limit; n F Represents the fan speed; n Fmax Represents the maximum allowable speed of the fan;

[0089]

[0090] where: g3 represents the constraint function for the high-pressure compressor not to overspeed; n Hmax represents the maximum allowable speed of the high-pressure compressor;

[0091]

[0092] where: g4 represents the constraint function for the fan not to surge; SMF min represents the minimum allowable value of the fan surge margin; SMF represents the fan surge margin; min represents taking the minimum value of this variable;

[0093]

[0094] where: g5 represents the constraint function for the high-pressure compressor not to surge; SMC min represents the minimum allowable value of the high-pressure compressor surge margin; SMC represents the high-pressure compressor surge margin;

[0095]

[0096] where: g6 represents the constraint function for the combustor not to flame out due to rich fuel; R OG represents the fuel-air ratio in the combustor; R OGmax represents the maximum allowable value of the fuel-air ratio in the combustor;

[0097]

[0098] where: g7 represents the constraint function for the main fuel flow; W f represents the main fuel flow; W fmax represents the maximum allowable value of the main fuel flow.

[0099] Construct to satisfy the constraint conditions of the aeroengine, and the constraint conditions of the aeroengine are ω·g(x), and the constraint function matrix formed is g n (x) (n = 1, 2,..., 7); After considering the constraint conditions, the weight adjustment coefficient matrix of the constraint function is:

[0100] ω = [ω1, ω2, ω3, ω4, ω5, ω6, ω7];

[0101] where: ω1, ω2, ω3, ω4, ω5, ω6, ω7 respectively represent the adjustment weight coefficients of the turbine inlet temperature not exceeding the limit, the fan speed not exceeding the limit, the high-pressure compressor not exceeding the limit, the fan not surging, the high-pressure compressor not surging, the combustor not flaming out due to rich fuel, and the main fuel flow.

[0102] Establish the optimization model for the aeroengine acceleration control process, which is:

[0103]

[0104] Where: R 4 represents a four-dimensional real vector; ω represents the weight adjustment coefficient matrix of the constraint function; g(x) represents the constraint function matrix;

[0105] S3: Calculate the optimal control point of the aeroengine according to the intelligent optimization algorithm to achieve the optimal control of the acceleration process;

[0106] Determine the initial value of the control variable of the aeroengine optimization model; set the initial iteration value t = 0, the storage pool dimension G, the variation domain B, the variation number C, the self-position judgment E, and use chaotic initialization to find S D-dimensional control variables X1, X2,..., X s , i = 1, 2,..., S, and randomly generate S initial velocities V1, V2,..., V s , i = 1, 2,..., S, to form the initial control variables.

[0107] Construct the iterative relationship between the control variable velocity and the control variable position. The iterative relationship of the control variable velocity is:

[0108] V i (t + 1) = ωV i (t) + c[p g (t) - X i (t)];

[0109] Where: c represents the learning factor; V i (t + 1) and V i (t) represent the velocities of the next iteration and the current control point respectively; p g (t) represents the optimal position searched by the current entire control point group so far; X i (t) represents the value of the i-th control variable of the current entire control variable group; t represents the iteration update times;

[0110] The iterative relationship of the control variable position is:

[0111]

[0112] Where: X i (t + 1) represents the updated control variable position; X best represents the optimal control variable of the aeroengine; m represents a random number between [0.45, 0.95]; β represents a random number within [-1, 1]; X r1 (t) represents the position of the r1-th randomly selected control variable; if represents satisfying the condition; rand represents a random number between [0, 1]; else represents the second case, and in the preferred implementation, the second case represents other cases that do not satisfy the judgment condition.

[0113] Obtain the optimal control points of the aero-engine optimization model to achieve the optimal control of the acceleration process of the variable cycle aero-engine.

[0114] Compare the optimization algorithm proposed in this invention application with the traditional genetic algorithm and particle swarm optimization algorithm, and optimize the acceleration process respectively. The starting condition is the throttle lever angle of 20°, and the acceleration end point is the throttle lever angle of 70°. The simulation results are shown in Table 1. Therefore, the intelligent optimization algorithm proposed in this invention application has a shorter acceleration time and a lower error accuracy under the premise of meeting the constraint conditions.

[0115] Table 1 Comparison of the results of the proposed solution of this invention with the genetic algorithm and the particle swarm optimization algorithm

[0116]

[0117]

[0118] In summary, the prediction results of the control parameter optimization method for the acceleration process of the variable cycle aero-engine in this case prove to have good effects.

[0119] (1) The embodiment of this invention is based on the non-linear model of the aero-engine acceleration process. By establishing the optimization model and constraint function of the engine acceleration process, the optimization model of the aero-engine acceleration control process is established, and further, the optimization of the control parameters is realized through the intelligent algorithm to achieve the optimal control of the aero-engine acceleration process; the effectiveness of this method is proved by the embodiment.

[0120] (2) The embodiment of this invention shortens the engine acceleration time on the premise of ensuring the safe operation of the aero-engine. The effectiveness of this method in further improving the engine acceleration performance, enhancing the maneuverability and flexibility of the aircraft, and meeting the actual application requirements is proved by the embodiment.

[0121] The embodiments described above are only descriptions of the preferred embodiments of this invention, and do not limit the scope of this invention. Without departing from the design spirit of this invention, various deformations and improvements made by those of ordinary skill in the art to the technical solution of this invention shall fall within the protection scope determined by the claims of this invention.

Claims

1. A control parameter optimization method for the acceleration process of a variable cycle aeroengine, characterized in that, It includes the following steps: Step 1: Establish a non-linear model for the acceleration process of an aero-engine; The non-linear model for the acceleration process of the variable cycle aero-engine is: [sfc F] T = f(x) = f[W f A9 dvgl dvgh] T ; Where: sfc represents the specific fuel consumption rate; F represents the engine thrust; f represents the non-linear vector function that generates the system output; x represents the control parameter variable; W f represents the adjustment of the main fuel flow; A9 represents the area of the tail nozzle; dvgl represents the fan guide vane angle; dvgh represents the compressor guide vane angle; Step 2: Determine the optimization model and constraint function for the acceleration process according to the acceleration process of the aero-engine; Step 21: Establish a multi-objective optimization function for the acceleration process of the aero-engine according to the constraint conditions of the acceleration process; The multi-objective optimization function for the acceleration process of the aero-engine is: Where: J1 represents the first objective function during the acceleration process of the aero-engine; J2 represents the second objective function during the acceleration process of the aero-engine; n Hd represents the desired speed of the high-pressure rotor of the aero-engine; n H represents the actual speed of the high-pressure rotor of the aero-engine; T t4d represents the desired temperature before the high-pressure turbine of the aero-engine; T t4 represents the actual temperature before the high-pressure turbine of the aero-engine; t represents the engine startup time; min represents the minimum value; Step 22: Use the linear weighted method to transform the multi-objective optimization function into a single-objective optimization function to determine the optimization objective function, which is: Where: J represents the single-objective function during the acceleration process of an aero-engine; ω a represents the weight coefficient of the first objective function during the acceleration process of an aero-engine; ω b represents the weight coefficient of the second objective function during the acceleration process of an aero-engine; Step 23: Discretize and normalize the single-objective function for the acceleration process of the aero-engine; finally, determine the single-objective function for the acceleration process of the aero-engine as: Where: n H (k) represents the rotational speed of the aero-engine at the k-th iteration; T t4 (k) represents the temperature of the aero-engine at the k-th iteration; k represents the number of iterations in the optimization process; Step 24: Refer to the form of the objective function, discretize and normalize the constraint conditions of the aero-engine, construct the constraint conditions for satisfying the aero-engine, and establish the optimization model for the acceleration control process of the aero-engine, which is: where: R 4 represents a four-dimensional real vector; ω represents the weight adjustment coefficient matrix of the constraint function; g(x) represents the constraint function matrix; Step 3: Calculate the optimal control points of the aero-engine to achieve the optimal control of the acceleration process; Determine the initial values of the control variables of the aero-engine optimization model; Construct the iterative relationship between the control variable speed and the control variable position, obtain the optimal control points of the aero-engine optimization model, and achieve the optimal control of the acceleration process of the variable cycle aero-engine.

2. The control parameter optimization method for the acceleration process of a variable cycle aeroengine according to claim 1, wherein, In the said Step 21, the constraint conditions of the acceleration process include: the turbine inlet temperature does not exceed the limit, the high-pressure compressor does not surge, the fan does not surge, the high-pressure rotor does not overspeed, the fan does not overspeed, the combustion chamber does not flame out due to rich fuel, and the main combustion chamber fuel supply does not exceed its maximum fuel supply, specifically: The constraint condition that the temperature before the turbine does not exceed the limit is T t4 <T t4max ; The constraint condition that the high-pressure compressor does not surge is SMC≥SMC min ; The constraint condition that the fan does not surge is SMF≥SMF min ; The constraint condition that the high-pressure rotor does not overspeed is n H ≤n Hmax ; The constraint condition that the fan does not overspeed is n F ≤n Fmax ; The constraint condition that the combustor does not flame out due to rich fuel is R OG ≤R OGmax ; The constraint condition that the fuel supply of the main combustor does not exceed its maximum fuel supply is W f ≤W fmax ; Where SMC represents the surge margin of the high-pressure compressor, SMF represents the surge margin of the fan, R OG represents the fuel-air ratio, W f represents the fuel supply of the main combustor, and the subscripts min and max represent the minimum and maximum values of this quantity respectively.

3. The control parameter optimization method for the acceleration process of a variable cycle aeroengine according to claim 1, wherein In the said Step 24, the constraint conditions of the aero-engine are also discretized and normalized, specifically: The process of discretizing and normalizing the constraint conditions of the aero-engine is: where: g1 represents the constraint function that the temperature before the turbine does not exceed the temperature limit; T t4max represents the upper limit of the engine temperature; max represents taking the maximum value of this variable; where: g2 represents the constraint function that the fan speed does not exceed the rated speed; n F represents the fan speed; n Fmax represents the maximum allowable speed of the fan; where: g3 represents the constraint function for the high-pressure compressor not to overspeed; n Hmax represents the maximum allowable speed of the high-pressure compressor; where: g4 represents the constraint function for the fan not to surge; SMF min represents the minimum allowable value of the fan surge margin; SMF represents the fan surge margin; min represents taking the minimum value of this variable; where: g5 represents the constraint function for the high-pressure compressor to avoid surge; SMC min represents the minimum allowable value of the surge margin of the high-pressure compressor; SMC represents the surge margin of the high-pressure compressor; In the formula: g6 represents the constraint function for the non-rich blowout of the combustion chamber; R OG represents the fuel-air ratio in the combustion chamber; R OGmax represents the maximum allowable value of the fuel-air ratio in the combustion chamber; where: g7 represents the constraint function of the main fuel flow; W f represents the main fuel flow; W fmax represents the maximum allowable value of the main fuel flow.

4. The control parameter optimization method for the acceleration process of a variable cycle aeroengine according to claim 1, wherein The construction of the constraint conditions for satisfying the aero-engine in the said Step 24 is specifically: The constraint condition of the aero-engine is ω·g(x), and the constraint function matrix formed is g n (x), where n = 1, 2,..., 7; after considering the constraint conditions, the weight adjustment coefficient matrix of the constraint function is as follows: ω = [ω1, ω2, ω3, ω4, ω5, ω6, ω7]; In the formula: ω1, ω2, ω3, ω4, ω5, ω6, ω7 respectively represent the weight coefficients for the turbine inlet temperature not exceeding the limit, the fan speed not exceeding the limit, the high-pressure compressor speed not exceeding the limit, the fan not surging, the high-pressure compressor not surging, the combustion chamber not flaming out due to rich fuel, and the adjustment of the main fuel flow.

5. The control parameter optimization method for the acceleration process of a variable cycle aeroengine according to claim 1, characterized in that, The determination of the initial values of the control variables of the aero-engine optimization model in the said Step 3 is specifically: Set the initial iteration value \(t = 0\), the storage pool dimension \(G\), the change domain \(B\), the number of changes \(C\), the self-position judgment \(E\), and initialize the chaos to find \(S\) \(D\)-dimensional control variables \(X_1, X_2, \ldots, X\) s , where \(i = 1, 2, \ldots, S\), randomly generate \(S\) initial velocities \(V_1, V_2, \ldots, V\) s , where \(i = 1, 2, \ldots, S\), to form the initial control variables.

6. The control parameter optimization method for the acceleration process of a variable cycle aeroengine according to claim 1, characterized in that, The construction of the iterative relationship between the control variable speed and the control variable position in the said Step 3 is: The iterative relationship of the control variable speed is: V i (t + 1) = ωV i (t) + c[p g (t) - X i (t)]; where: c represents the learning factor; V i (t + 1) and V i (t) represent the speeds of the next iteration and the current control point respectively; p g (t) represents the optimal position found so far by the current entire control point group; X i (t) represents the value of the i-th control variable of the current entire control variable group; t represents the number of iterative updates; The iterative relationship of the control variable position is: Where: X i (t + 1) represents the position of the updated control variable; X best represents the optimal control variable of the aeroengine; m represents a random number between [0.45, 0.95]; β represents a random number within [-1, 1]; X r1 (t) represents the position of the r1-th control variable randomly selected; if represents satisfying the condition; rand represents a random number between [0, 1]; else represents the situation where the judgment condition is not satisfied.

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